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diff --git a/pkgdown.yml b/pkgdown.yml index e883e18..363788b 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -4,7 +4,7 @@ pkgdown_sha: ~ articles: enONE_tutorial-zh: enONE_tutorial-zh.html enONE_tutorial: enONE_tutorial.html -last_built: 2024-05-10T08:28Z +last_built: 2024-05-10T08:49Z urls: reference: https://thereallda.github.io/enONE/reference article: https://thereallda.github.io/enONE/articles diff --git a/reference/figures/logo.jpg b/reference/figures/logo.jpg new file mode 100644 index 0000000..0aa3d9b Binary files /dev/null and b/reference/figures/logo.jpg differ diff --git a/search.json b/search.json index 7a3aa77..dc5f9b7 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://thereallda.github.io/enONE/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022 enONE authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"前言","dir":"Articles","previous_headings":"","what":"前言","title":"enONE_tutorial-zh","text":"细胞内的核苷类代谢物，如烟酰胺腺嘌呤二核苷酸（NAD），能够被添加到mRNA 5’末端，形成NAD帽修饰的RNA（NAD-RNA）；这类非经典起始核苷酸（NCIN）修饰的RNA被统称为NCIN帽修饰的RNA（NCIN-RNA）。NCIN-RNA天然地将代谢物与基因表达紧密地联系起来，定义了一种新颖但功能未知的表观转录修饰。NCIN-RNA的表征是功能研究的前提。当前NCIN-RNA的表征方法，如ONE-seq和DO-seq，实现了代谢物帽修饰RNA在细胞和组织中的鉴定。然而，这些方法产出的数据会受到源于化学酶促标记及亲和富集反应等实验操作误差的影响。因此，这里提出了enONE这种NAD-RNA表观转录组的计算分析方法，实现NAD-RNA数据的定量和比较分析。 enONE 的R包可以在GitHub中找到（https://github.com/thereallda/enONE）","code":""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"enONE_tutorial-zh","text":"在这个教程中，我们会使用人类外周血单核细胞（PBMCs）的NAD-RNA-seq数据来展示 enONE 的工作流程。 要注意的是，这里混入了三种spike-RNAs： 1. 来自模式生物黑腹果蝇的总RNA，用于 enONE 的normalization； 2. Synthetic RNA，其中含有5％的NAD修饰形式（相对于m7G-RNA），用于确定富集灵敏度； 3. Synthetic RNA，其为100％ m7G加帽的形式，用于确定富集特异性。 Figure: Schematic workflow total RNAs PBMCs three sets spike-ins. 我们首先读取数据，包括基因表达量的count matrix和样本信息的metadata。matrix和样本信息的。metadata至少包括两列，即用于样本生物分组的”condition”列和用于样本富集分组的”enrich”列。 另外，metadata也可以包括其他信息，例如样本的批次信息。 接下来，我们使用count matrix和metadata创建一个 Enone 对象。 Enone 对象，包含了NAD-RNA-seq数据集的数据（raw normalized counts data）和结果信息（例如，数据标准化性能的打分和NAD-RNA富集结果）。 在构建 Enone 对象时，我们可以提供以下参数： spike-基因的前缀，例如果蝇Flybase基因id前缀为FB (spike..prefix = \"^FB\")； synthetic spike-in的id (synthetic.id = c(\"Syn1\", \"Syn2\"))，这需要与 counts_mat 中的行名相同； Input样本分组的id (input.id = \"Input\") 和Enrichment样本分组的id (enrich.id = \"Enrich\")，与enrich列相同。 其中, “Syn1”代表5% NAD-RNA spike-“Syn2”代表100% m7G-RNA spike-. synthetic.id 是可选的参数.","code":"library(enONE) library(tidyverse) library(patchwork) # read in metadata and counts matrix counts_mat <- read.csv(\"data/Counts.csv\", row.names = 1) meta <- read.csv(\"data/metadata.csv\", comment.char = \"#\") head(meta) #> id sample.id condition batch enrich #> 1 D1 Y2 Y.Input D Input #> 2 D2 Y10 Y.Input D Input #> 3 D3 Y14 Y.Input D Input #> 4 D4 Y16 Y.Input D Input #> 5 D5 M13 M.Input D Input #> 6 D6 M14 M.Input D Input # rownames of metadata should be consistent with the colnames of counts_mat rownames(meta) <- meta$id # prefix of Drosophila spike-in genes spikeInPrefix <- \"^FB\" # create Enone Enone <- createEnone(data = counts_mat, col.data = meta, spike.in.prefix = spikeInPrefix, synthetic.id = c(\"Syn1\", \"Syn2\"), input.id = \"Input\", enrich.id = \"Enrich\" ) Enone #> class: Enone #> dim: 76290 62 #> metadata(0): #> assays(1): '' #> rownames(76290): ENSG00000223972 ENSG00000227232 ... Syn1 Syn2 #> rowData names(3): GeneID SpikeIn Synthetic #> colnames(62): D1 D2 ... F22 F24 #> colData names(7): id id.1 ... enrich replicate"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"标准流程","dir":"Articles","previous_headings":"","what":"标准流程","title":"enONE_tutorial-zh","text":"enONE 流程包括以下四步: 质量控制（Quality control）; 基因集选取（Gene set selection）; 数据校正（Normalization procedures）; 数据校正效果评估（Normalization performance assessment）.","code":""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"质量控制quality-control","dir":"Articles","previous_headings":"标准流程","what":"质量控制（Quality control）","title":"enONE_tutorial-zh","text":"enONE 首先进行质量控制步骤，通过 FilterLowExprGene 保留至少在 n 个样本中至少具有 min.count 的基因来完成此步骤。n 由 group 中指定的最小样本组的大小确定。 此外，可以通过 OutlierTest对异常样本进行评估并进一步移除（return=TRUE）。由于没有样本被标记为异常值，因此我们将在后续分析中使用所有样本。","code":"Enone <- FilterLowExprGene(Enone, group = Enone$condition, min.count = 20) Enone #> class: Enone #> dim: 27661 62 #> metadata(0): #> assays(1): '' #> rownames(27661): ENSG00000227232 ENSG00000268903 ... Syn1 Syn2 #> rowData names(3): GeneID SpikeIn Synthetic #> colnames(62): D1 D2 ... F22 F24 #> colData names(7): id id.1 ... enrich replicate ## ronser\"s test for outlier assessment OutlierTest(Enone, return=FALSE) #> Rosner's outlier test #> i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier #> 1 0 1.602661e-15 51.77231 80.37523 15 1.552475 3.212165 FALSE #> 2 1 -1.317627e+00 51.14304 78.20437 19 1.554894 3.205977 FALSE #> 3 2 -2.642993e+00 50.50717 70.38990 41 1.445991 3.199662 FALSE"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"运行enone","dir":"Articles","previous_headings":"标准流程","what":"运行enONE","title":"enONE_tutorial-zh","text":"enONE 函数可以进行基因选择、数据标准化和标准化效果评估。该函数在 rowData 中存储选择的基因集，在 counts slot中返回标准化计数矩阵（当 return.norm=TRUE 时），在 enone_factor slot中返回标准化因子，在 enone_metrics 和 enone_scores slots中返回评估指标和得分。以下是这些步骤的详细描述。","code":"Enone <- enONE(Enone, scaling.method = c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"), ruv.norm = TRUE, ruv.k = 3, eval.pam.k = 2:6, eval.pc.n = 3, return.norm = TRUE ) #> Gene set selection for normalization and assessment... #> - The number of negative control genes for normalization: 1000 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> - The number of positive evaluation genes: 500 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> - The number of negative evaluation genes: 500 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> Apply normalization... #> - Scaling... #> - Regression-based normalization... #> Perform assessment..."},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"基因集选取gene-set-selection","dir":"Articles","previous_headings":"标准流程 > 运行enONE","what":"基因集选取（Gene set selection）","title":"enONE_tutorial-zh","text":"enONE 定义了三组基因，包括： 负对照（NegControl）：默认情况下，enONE 将 果蝇 spike-ins（或其他外源生物的RNA spike-）中 FDR 排名最低的 1,000 个基因作为负对照（不富集的基因），用于校正无关误差。 负评估（NegEvaluation）：默认情况下，enONE 将样本中 FDR 排名最低的 500 个基因作为负评估基因，用于评估无关误差。 正评估（PosEvaluation）：默认情况下，enONE 将样本中 FDR 排名最高的 500 个基因作为正评估基因，用于评估研究感兴趣的差异。 基因集的选择可以在 enONE 函数中使用 auto=TRUE 参数（默认值）自动定义，也可以在 neg.control, pos.eval, neg.eval 参数中分别提供。 可以通过 getGeneSet函数和基因集的名称（即\"NegControl\"、\"NegEvaluation\"、\"PosEvaluation\"）来获取相应的基因集。","code":"getGeneSet(Enone, name = \"NegControl\")[1:5] #> [1] \"FBgn0031247\" \"FBgn0031255\" \"FBgn0004583\" \"FBgn0031324\" \"FBgn0026397\""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"数据校正normalization","dir":"Articles","previous_headings":"标准流程 > 运行enONE","what":"数据校正（Normalization）","title":"enONE_tutorial-zh","text":"enONE 整合了全局缩放（global scaling）和基于回归的校正方法（regression-based normalization）来产生normalization方法。 对于全局缩放方法，enONE 整合了五种方法，包括： Total Count (TC); Upper-Quartile (UQ); Trimmed Mean M Values (TMM); DESeq; PossionSeq. 默认情况下，enONE 使用所有缩放方法，但用户可以在 scaling.method 参数中选择要使用的缩放程序。 对于regression-based方法，enONE 利用了三种RUV的方法，包括： RUVg; RUVs; RUVse. 例如，您可以通过选择 ruv.norm=TRUE, ruv.k=2 来执行使用前两个无关误差因子的 RUV。 RUVse 是类似于 RUVs 的方法。它基于每个富集样本或背景样本组的重复样本中的负对照基因来估计无关误差因子，其假设富集效应在重复之间是相对稳定的。 所有使用的normalization方法都可以使用 listNormalization 来提取。 校正后的数据可以通过 Counts 函数获取。","code":"listNormalization(Enone) #> [1] \"TC\" \"UQ\" \"TMM\" #> [4] \"DESeq\" \"PossionSeq\" \"Raw\" #> [7] \"Raw_RUVg_k1\" \"Raw_RUVg_k2\" \"Raw_RUVg_k3\" #> [10] \"Raw_RUVs_k1\" \"Raw_RUVs_k2\" \"Raw_RUVs_k3\" #> [13] \"Raw_RUVse_k1\" \"Raw_RUVse_k2\" \"Raw_RUVse_k3\" #> [16] \"TC_RUVg_k1\" \"TC_RUVg_k2\" \"TC_RUVg_k3\" #> [19] \"TC_RUVs_k1\" \"TC_RUVs_k2\" \"TC_RUVs_k3\" #> [22] \"TC_RUVse_k1\" \"TC_RUVse_k2\" \"TC_RUVse_k3\" #> [25] \"UQ_RUVg_k1\" \"UQ_RUVg_k2\" \"UQ_RUVg_k3\" #> [28] \"UQ_RUVs_k1\" \"UQ_RUVs_k2\" \"UQ_RUVs_k3\" #> [31] \"UQ_RUVse_k1\" \"UQ_RUVse_k2\" \"UQ_RUVse_k3\" #> [34] \"TMM_RUVg_k1\" \"TMM_RUVg_k2\" \"TMM_RUVg_k3\" #> [37] \"TMM_RUVs_k1\" \"TMM_RUVs_k2\" \"TMM_RUVs_k3\" #> [40] \"TMM_RUVse_k1\" \"TMM_RUVse_k2\" \"TMM_RUVse_k3\" #> [43] \"DESeq_RUVg_k1\" \"DESeq_RUVg_k2\" \"DESeq_RUVg_k3\" #> [46] \"DESeq_RUVs_k1\" \"DESeq_RUVs_k2\" \"DESeq_RUVs_k3\" #> [49] \"DESeq_RUVse_k1\" \"DESeq_RUVse_k2\" \"DESeq_RUVse_k3\" #> [52] \"PossionSeq_RUVg_k1\" \"PossionSeq_RUVg_k2\" \"PossionSeq_RUVg_k3\" #> [55] \"PossionSeq_RUVs_k1\" \"PossionSeq_RUVs_k2\" \"PossionSeq_RUVs_k3\" #> [58] \"PossionSeq_RUVse_k1\" \"PossionSeq_RUVse_k2\" \"PossionSeq_RUVse_k3\" head(enONE::Counts(Enone, slot=\"sample\", method=\"DESeq_RUVg_k2\"))[,1:5] #> D1 D2 D3 D4 D5 #> ENSG00000227232 8.513936 11.645203 12.431441 7.453572 11.104087 #> ENSG00000268903 3.305409 8.100305 5.277670 26.325845 7.088225 #> ENSG00000269981 2.270299 4.876642 2.402698 18.118129 6.068101 #> ENSG00000279457 11.895695 9.998730 8.760526 11.204313 16.375896 #> ENSG00000225630 13.767575 16.292915 15.365490 10.140265 17.834578 #> ENSG00000237973 28.042040 37.277287 39.847772 34.078301 33.952026"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"数据校正效果评估","dir":"Articles","previous_headings":"标准流程 > 运行enONE","what":"数据校正效果评估","title":"enONE_tutorial-zh","text":"enONE 利用了八个与基因表达分布的不同方面相关的标准化性能指标来评估数据标准化的性能。这八个指标分别为： BIO_SIM：生物组的相似性。通过计算前三个表达PCs（默认情况下）上的欧氏距离度量，由 condition 列定义的聚类的平均轮廓宽度。BIO_SIM越大越好。 EN_SIM：富集组的相似性。通过计算前三个表达PCs（默认情况下）上的欧氏距离度量，由 enrich 列定义的聚类的平均轮廓宽度。EN_SIM 越大越好。 BAT_SIM：批次组的相似性。通过计算前三个表达PCs（默认情况下）上的欧氏距离度量，由 batch 列定义的聚类的平均轮廓宽度。BAT_SIM 越小越好。 PAM_SIM：PAM 聚类组的相似性。通过计算前三个表达PCs（默认情况下）上的欧氏距离度量，由 PAM 聚类（根据 eval.pam.k 进行聚类）定义的聚类的最大平均轮廓宽度。PAM_SIM 越大越好。 WV_COR：研究相关变量的保留程度。对原始计数的正评估基因（PosEvaluation）子矩阵的前 eval.pc.n 个PCs 进行回归的 R2 测量。WV_COR 越大越好。 UV_COR：无关误差移除的程度。对原始计数的负评估基因（NegEvaluation）子矩阵的前 eval.pc.n 个PCs 进行回归的 R2 测量。UV_COR 越小越好。 RLE_MED：平均平方中位数相对对数表达（RLE）。RLE_MED 越小越好。 RLE_IQR：相对于四分位距（IQR）的方差。RLE_IQR 越小越好。 评估指标可以通过 getMetrics 进行获取。 评估指标通过转换为秩值，并通过平均各指标的秩值来获得特定方法的打分（score）。数据标准化效果的打分可以通过 getScore 获取。","code":"getMetrics(Enone)[1:5,] #> BIO_SIM EN_SIM BATCH_SIM PAM_SIM RLE_MED #> DESeq_RUVg_k2 -0.02467593 0.3576650 -0.03483061 0.5713398 9.319387e-06 #> PossionSeq_RUVs_k3 0.10240479 0.3374029 -0.03965069 0.5556398 9.971803e-05 #> TMM_RUVg_k2 -0.02664678 0.3518415 -0.03524885 0.5595637 8.500478e-05 #> DESeq_RUVg_k3 -0.04651463 0.3485155 -0.04427468 0.5708689 1.370353e-05 #> DESeq_RUVs_k1 0.03352455 0.3391415 -0.04444996 0.5224180 7.861089e-06 #> RLE_IQR WV_COR UV_COR #> DESeq_RUVg_k2 0.011996148 0.7401496 0.3258051 #> PossionSeq_RUVs_k3 0.012314397 0.7709943 0.3154878 #> TMM_RUVg_k2 0.012089861 0.7404610 0.3288269 #> DESeq_RUVg_k3 0.011133971 0.6688187 0.4166522 #> DESeq_RUVs_k1 0.009145155 0.6495402 0.3680677 getScore(Enone)[1:5,] #> BIO_SIM EN_SIM BATCH_SIM PAM_SIM RLE_MED RLE_IQR WV_COR #> DESeq_RUVg_k2 32 59 40 59 56 25 20 #> PossionSeq_RUVs_k3 60 23 47 50 33 23 23 #> TMM_RUVg_k2 30 50 42 54 39 24 21 #> DESeq_RUVg_k3 20 48 53 58 54 29 12 #> DESeq_RUVs_k1 55 32 54 25 57 32 5 #> UV_COR SCORE #> DESeq_RUVg_k2 59 43.750 #> PossionSeq_RUVs_k3 60 39.875 #> TMM_RUVg_k2 58 39.750 #> DESeq_RUVg_k3 39 39.125 #> DESeq_RUVs_k1 52 39.000"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"选择数据标准化方法","dir":"Articles","previous_headings":"","what":"选择数据标准化方法","title":"enONE_tutorial-zh","text":"enONE 提供了biplot来探索数据校正方法的全空间，可以通过 interactive=TRUE 来开启交互模式。 在这个图中，每个点对应一个标准化程序，并颜色代表性能得分（八个性能指标rank的均值）。蓝色箭头对应于性能指标的PCA载荷。蓝色箭头的方向和长度可以解释为每个指标对前两个主成分的贡献程度。 这里使用打分最高的标准化程序 DESeq_RUVg_k2 进行下游分析。 需要注意的是，如果 enONE 运行的时候没有返回校正后数据（.e., return.norm=FALSE），可以通过下面的代码手动执行数据标准化。","code":"# get performance score enScore <- getScore(Enone) # perform PCA based on evaluation score, excluding BAT_SIM column (3) if no batch information provided, and SCORE column (9). # pca.eval <- prcomp(enScore[,-c(3, 9)], scale = TRUE) pca.eval <- prcomp(enScore[,-c(9)], scale = TRUE) # pca biplot PCA_Biplot(pca.eval, score = enScore$SCORE, interactive = FALSE) # select normalization norm.method <- rownames(enScore[1,]) # get normalized counts norm.data <- enONE::Counts(Enone, slot = \"sample\", method = norm.method) # get normalization factors norm.factors <- getFactor(Enone, slot = \"sample\", method = norm.method) norm.method #> [1] \"DESeq_RUVg_k2\" # perform normalization Enone <- UseNormalization(Enone, slot = \"sample\", method = norm.method) # get normalized counts norm.data <- Counts(Enone, slot = \"sample\", method = norm.method)"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"数据校正的效果","dir":"Articles","previous_headings":"","what":"数据校正的效果","title":"enONE_tutorial-zh","text":"这里使用PCA展示数据校正的效果。","code":"# create sample name, e.g., Y3.Input samples_name <- paste(Enone$condition, Enone$replicate, sep=\".\") # PCA for raw count p1 <- PCAplot(enONE::Counts(Enone, slot=\"sample\", \"Raw\"), color = Enone$batch, shape = Enone$enrich, label = samples_name, vst.norm = TRUE) + ggtitle(\"Before normalization\") # PCA for normalized count p2 <- PCAplot(log1p(norm.data), color = Enone$batch, shape = Enone$enrich, label = samples_name, vst.norm = FALSE) + ggtitle(\"After normalization\") # combine two plots p1 + p2 #> Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider #> increasing max.overlaps"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"鉴定富集基因","dir":"Articles","previous_headings":"","what":"鉴定富集基因","title":"enONE_tutorial-zh","text":"enONE 可用于富集基因的鉴定。FindEnrichment 函数自动地根据 condition 列中的每个生物学分组鉴定富集的基因。 默认情况下，富集基因（即NAD-RNA）为Enrichment/Input ≥ 2（logfc.cutoff = 1），FDR < 0.05（p.cutoff = 0.05）的基因。 使用getEnrichment函数可以获取一个列表包含了各组的富集结果。 res.sig.ls 中每个表都是一个data.frame，其中行为基因，列为基因的相关信息（GeneID、logFC、p值等）。表格中包含以下列： GeneID：基因的ID。 logFC：富集样本和输入样本之间的log2倍变化。正值表示基因在富集组中更高度富集。 logCPM：所有样本平均表达的log2 CPM（counts per million）。 LR：似然比检验的似然比。 PValue：来自似然比检验的p值。 FDR：p值的假阳性发现率，默认使用\"BH\"方法。 enONE 提供 reduceRes 函数将各个富集表格的结果转换到同一个 data.frame 中。接着，可以使用 BetweenStatPlot 可视化各组NAD-RNA修饰水平。","code":"# find all enriched genes Enone <- FindEnrichment(Enone, slot=\"sample\", norm.method = norm.method, logfc.cutoff = 1, p.cutoff = 0.05) #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... # get filtered enrichment results res.sig.ls <- getEnrichment(Enone, slot=\"sample\", filter=TRUE) # count number of enrichment in each group unlist(lapply(res.sig.ls, nrow)) #> Y.Enrich_Y.Input M.Enrich_M.Input O.Enrich_O.Input #> 579 672 683 head(res.sig.ls[[1]]) #> GeneID logFC logCPM LR PValue FDR #> 1 ENSG00000013275 2.271643 7.518198 731.6422 3.936608e-161 5.444329e-157 #> 2 ENSG00000113387 2.643018 9.732157 631.6712 2.164465e-139 1.496728e-135 #> 3 ENSG00000104853 2.244442 7.839256 587.7484 7.738626e-130 3.567507e-126 #> 4 ENSG00000105185 2.094086 6.147852 558.6392 1.662061e-123 5.746574e-120 #> 5 ENSG00000143110 3.084209 9.115976 550.0416 1.232914e-121 3.410239e-118 #> 6 ENSG00000173915 2.324782 7.005478 483.7653 3.239173e-107 7.466295e-104 # simplify group id names(res.sig.ls) <- c(\"Young\", \"Mid\", \"Old\") # logfc.col specify the name of logFC column nad_df1 <- reduceRes(res.sig.ls, logfc.col = \"logFC\") # convert the Group column as factor nad_df1$Group <- factor(nad_df1$Group, levels = unique(nad_df1$Group)) # draw plot bxp1 <- BetweenStatPlot(nad_df1, x=\"Group\", y=\"logFC\", color=\"Group\", step.increase = 0.6, add.p = \"p\", comparisons = list(c(\"Young\", \"Mid\"), c(\"Young\", \"Old\"))) bxp1"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"处理synthetic-spike-in","dir":"Articles","previous_headings":"","what":"处理synthetic spike-in","title":"enONE_tutorial-zh","text":"由于样本中加入了synthetic RNA，其中一个带有5%的NAD帽，另一个带有100%的m7G帽，我们可以利用这些spike-ins来确定捕获的灵敏度和特异性。 synEnrichment计算具有给定标准化方法的spike-ins的富集水平。DotPlot可用于可视化spike-in的富集水平。 其中，具有5% NAD-RNA的Syn1有明显富集，而100% m7G-RNA的Syn2并没有出现富集，表明了富集实验的特异性。","code":"# compute synthetic spike-in enrichment syn_level <- synEnrichment(Enone, method=norm.method, log=TRUE) # transform to long format syn_df <- as.data.frame(syn_level) %>% rownames_to_column(\"syn_id\") %>% pivot_longer(cols = -syn_id, names_to = \"id\", values_to = \"logFC\") %>% left_join(meta[,c(\"id\",\"condition\")], by=\"id\") # remove suffix of condition for simplification syn_df$condition <- gsub(\"\\\\..*\", \"\", syn_df$condition) # rename facet label samples_label <- setNames(c(\"5% NAD-RNA\", \"100% m7G-RNA\"), nm=c(\"Syn1\", \"Syn2\")) # draw dotplot DotPlot(syn_df, x=\"syn_id\", y=\"logFC\", fill=\"syn_id\") + theme(legend.position = \"none\") + scale_x_discrete(labels=samples_label) #> Bin width defaults to 1/30 of the range of the data. Pick better value with #> `binwidth`. # save Enone data save(Enone, file=\"data/Enone.RData\")"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"enONE_tutorial-zh","text":"","code":"sessionInfo() #> R version 4.4.0 (2024-04-24) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] patchwork_1.2.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 #> [5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 #> [9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 enONE_1.0.0 #> #> loaded via a namespace (and not attached): #> [1] pbapply_1.7-2 rlang_1.1.3 #> [3] magrittr_2.0.3 matrixStats_1.3.0 #> [5] compiler_4.4.0 flexmix_2.3-19 #> [7] systemfonts_1.0.6 vctrs_0.6.5 #> [9] pkgconfig_2.0.3 crayon_1.5.2 #> [11] fastmap_1.1.1 backports_1.4.1 #> [13] XVector_0.44.0 labeling_0.4.3 #> [15] utf8_1.2.4 rmarkdown_2.26 #> [17] tzdb_0.4.0 UCSC.utils_1.0.0 #> [19] ragg_1.3.1 xfun_0.43 #> [21] modeltools_0.2-23 zlibbioc_1.50.0 #> [23] cachem_1.0.8 GenomeInfoDb_1.40.0 #> [25] jsonlite_1.8.8 EnvStats_2.8.1 #> [27] highr_0.10 DelayedArray_0.30.1 #> [29] fpc_2.2-12 BiocParallel_1.38.0 #> [31] broom_1.0.5 parallel_4.4.0 #> [33] prabclus_2.3-3 cluster_2.1.6 #> [35] R6_2.5.1 stringi_1.8.4 #> [37] bslib_0.7.0 limma_3.60.0 #> [39] car_3.1-2 GenomicRanges_1.56.0 #> [41] jquerylib_0.1.4 diptest_0.77-1 #> [43] Rcpp_1.0.12 SummarizedExperiment_1.34.0 #> [45] knitr_1.46 IRanges_2.38.0 #> [47] splines_4.4.0 timechange_0.3.0 #> [49] Matrix_1.7-0 nnet_7.3-19 #> [51] tidyselect_1.2.1 abind_1.4-5 #> [53] yaml_2.3.8 codetools_0.2-20 #> [55] lattice_0.22-6 withr_3.0.0 #> [57] Biobase_2.64.0 evaluate_0.23 #> [59] desc_1.4.3 mclust_6.1.1 #> [61] kernlab_0.9-32 pillar_1.9.0 #> [63] ggpubr_0.6.0 MatrixGenerics_1.16.0 #> [65] carData_3.0-5 stats4_4.4.0 #> [67] plotly_4.10.4 generics_0.1.3 #> [69] hms_1.1.3 S4Vectors_0.42.0 #> [71] munsell_0.5.1 scales_1.3.0 #> [73] class_7.3-22 glue_1.7.0 #> [75] lazyeval_0.2.2 tools_4.4.0 #> [77] robustbase_0.99-2 data.table_1.15.4 #> [79] locfit_1.5-9.9 ggsignif_0.6.4 #> [81] fs_1.6.4 grid_4.4.0 #> [83] edgeR_4.2.0 colorspace_2.1-0 #> [85] GenomeInfoDbData_1.2.12 cli_3.6.2 #> [87] textshaping_0.3.7 fansi_1.0.6 #> [89] viridisLite_0.4.2 S4Arrays_1.4.0 #> [91] paintingr_0.1.0 gtable_0.3.5 #> [93] DEoptimR_1.1-3 rstatix_0.7.2 #> [95] DESeq2_1.44.0 sass_0.4.9 #> [97] digest_0.6.35 BiocGenerics_0.50.0 #> [99] SparseArray_1.4.3 ggrepel_0.9.5 #> [101] farver_2.1.1 htmlwidgets_1.6.4 #> [103] memoise_2.0.1 htmltools_0.5.8.1 #> [105] pkgdown_2.0.9 lifecycle_1.0.4 #> [107] httr_1.4.7 statmod_1.5.0 #> [109] MASS_7.3-60.2"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"enONE_tutorial","text":"hub metabolite, nicotinamide adenine dinucleotide (NAD), can used initiating nucleotide RNA transcription result NAD-capped RNAs (NAD-RNAs). Epitranscriptome-wide profiling NAD-RNAs involves chemo-enzymatic labeling affinity-based enrichment; yet currently available computational analysis adequately remove variations inherently linked capture procedures. , propose spike--based normalization data-driven evaluation framework, enONE, omic-level analysis NAD-capped RNAs. enONE package implemented R publicly available https://github.com/thereallda/enONE.","code":""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"enONE_tutorial","text":"tutorial, demonstrate enONE workflow using NAD-RNA-seq data human peripheral blood mononuclear cells (PBMCs). Notably, included three types spike-RNAs: Total RNAs Drosophila melanogaster, invertebrate model organism well-annotated genome sequence, estimating unwanted variation; Synthetic RNAs, consisting 5% NAD- relative m7G-capped forms, used determine capture sensitivity; Synthetic RNAs, 100% m7G-capped forms, used determine capture specificity (Figure ). Figure: Schematic workflow total RNAs PBMCs three sets spike-ins. start reading data, including count matrix metadata. metadata include lease two columns, namely condition column biological grouping samples, enrich column enrichment grouping samples. Also, information, batch group can included metadata. Next, use count matrix metadata create Enone object. Enone object serves container contains data (raw normalized counts data) analysis (e.g., normalization performance evaluation score enrichment results) NAD-RNA-seq dataset. constructing Enone object, can provide following arguments: Prefix spike-genes (spike..prefix = \"^FB\"); id synthetic spike-(synthetic.id = c(\"Syn1\", \"Syn2\")), row names counts_mat; id input (input.id = \"Input\") enrichment (enrich.id = \"Enrich\"), enrich column. , “Syn1” represent spike-5% NAD-RNA “Syn2” spike-100% m7G-RNA. Notably, synthetic.id optional.","code":"library(enONE) library(tidyverse) library(patchwork) # read in metadata and counts matrix counts_mat <- read.csv(\"data/Counts.csv\", row.names = 1) meta <- read.csv(\"data/metadata.csv\", comment.char = \"#\") head(meta) #> id sample.id condition batch enrich #> 1 D1 Y2 Y.Input D Input #> 2 D2 Y10 Y.Input D Input #> 3 D3 Y14 Y.Input D Input #> 4 D4 Y16 Y.Input D Input #> 5 D5 M13 M.Input D Input #> 6 D6 M14 M.Input D Input # rownames of metadata should be consistent with the colnames of counts_mat rownames(meta) <- meta$id # prefix of Drosophila spike-in genes spikeInPrefix <- \"^FB\" # create Enone Enone <- createEnone(data = counts_mat, col.data = meta, spike.in.prefix = spikeInPrefix, synthetic.id = c(\"Syn1\", \"Syn2\"), input.id = \"Input\", enrich.id = \"Enrich\" ) Enone #> class: Enone #> dim: 76290 62 #> metadata(0): #> assays(1): '' #> rownames(76290): ENSG00000223972 ENSG00000227232 ... Syn1 Syn2 #> rowData names(3): GeneID SpikeIn Synthetic #> colnames(62): D1 D2 ... F22 F24 #> colData names(7): id id.1 ... enrich replicate"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"standard-workflow","dir":"Articles","previous_headings":"","what":"Standard workflow","title":"enONE_tutorial","text":"enONE workflow consists four steps: Quality control; Gene set selection; Normalization procedures; Normalization performance assessment.","code":""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"quality-control","dir":"Articles","previous_headings":"Standard workflow","what":"Quality Control","title":"enONE_tutorial","text":"enONE initiate quality control step filter lowly-expressed genes. step performed FilterLowExprGene keeping genes least min.count least n samples. n determined smallest group sample size specifying group . Additionally, outliers can assessed removed (return=TRUE) OutlierTest. Since sample flagged outlier, use samples following analysis.","code":"Enone <- FilterLowExprGene(Enone, group = Enone$condition, min.count = 20) Enone #> class: Enone #> dim: 27661 62 #> metadata(0): #> assays(1): '' #> rownames(27661): ENSG00000227232 ENSG00000268903 ... Syn1 Syn2 #> rowData names(3): GeneID SpikeIn Synthetic #> colnames(62): D1 D2 ... F22 F24 #> colData names(7): id id.1 ... enrich replicate ## ronser\"s test for outlier assessment OutlierTest(Enone, return=FALSE) #> Rosner's outlier test #> i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier #> 1 0 1.602661e-15 51.77231 80.37523 15 1.552475 3.212165 FALSE #> 2 1 -1.317627e+00 51.14304 78.20437 19 1.554894 3.205977 FALSE #> 3 2 -2.642993e+00 50.50717 70.38990 41 1.445991 3.199662 FALSE"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"running-enone","dir":"Articles","previous_headings":"Standard workflow","what":"Running enONE","title":"enONE_tutorial","text":"main enONE function can used perform gene selection, normalization, evaluation. function return selected gene set rowData, normalized count matrix counts slot (return.norm=TRUE), normalization factors (enone_factor slot), evaluation metrics scores enone_metrics enone_scores slots, respectively. Detailed steps described bellow.","code":"Enone <- enONE(Enone, scaling.method = c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"), ruv.norm = TRUE, ruv.k = 3, eval.pam.k = 2:6, eval.pc.n = 3, return.norm = TRUE ) #> Gene set selection for normalization and assessment... #> - The number of negative control genes for normalization: 1000 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> - The number of positive evaluation genes: 500 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> - The number of negative evaluation genes: 500 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> Apply normalization... #> - Scaling... #> - Regression-based normalization... #> Perform assessment..."},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"gene-set-selection","dir":"Articles","previous_headings":"Standard workflow > Running enONE","what":"Gene set Selection","title":"enONE_tutorial","text":"gene set selection, enONE defined three sets control genes, including: Negative Control (NegControl): default, enONE define 1,000 least significantly enriched genes Drosophila spike-ins (RNA spike-exogenous organism), ranked FDR, negative controls adjustment unwanted variations. Negative Evaluation (NegEvaluation): default, enONE define 500 least significantly varied genes samples interest, ranked FDR, negative evaluation genes evaluation unwanted variations. Positive Evaluation (PosEvaluation): default, enONE define 500 significantly enriched genes samples interest, ranked FDR, positive evaluation genes evaluation wanted variations. Gene set selection can either automatically defined enONE function auto=TRUE parameter (default), provided neg.control, pos.eval, neg.eval parameters, respectively. Selected gene sets can assessed getGeneSet name gene set (.e., \"NegControl\", \"NegEvaluation\", \"PosEvaluation\").","code":"getGeneSet(Enone, name = \"NegControl\")[1:5] #> [1] \"FBgn0031247\" \"FBgn0031255\" \"FBgn0004583\" \"FBgn0031324\" \"FBgn0026397\""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"normalization","dir":"Articles","previous_headings":"Standard workflow > Running enONE","what":"Normalization","title":"enONE_tutorial","text":"enONE implements global scaling regression-based methods generation normalization procedures. global scaling normalization procedures, five different scaling procedures implemented, including Total Count (TC); Upper-Quartile (UQ); Trimmed Mean M Values (TMM); DESeq; PossionSeq. default, enONE uses scaling methods, user can perform selected scaling procedures scaling.method parameter. regression-based procedures, enONE use three variants RUV estimate factors unwanted variation, including RUVg; RUVs; RUVse. instance, can perform RUV selected first two unwanted factors ruv.norm=TRUE, ruv.k=2. RUVse modification RUVs. estimated factors unwanted variation based negative control genes replicate samples assay group (.e., enrichment input), enrichment effect assumed constant. applied normalization methods can assessed listNormalization Normalized counts can assessed Counts","code":"listNormalization(Enone) #> [1] \"TC\" \"UQ\" \"TMM\" #> [4] \"DESeq\" \"PossionSeq\" \"Raw\" #> [7] \"Raw_RUVg_k1\" \"Raw_RUVg_k2\" \"Raw_RUVg_k3\" #> [10] \"Raw_RUVs_k1\" \"Raw_RUVs_k2\" \"Raw_RUVs_k3\" #> [13] \"Raw_RUVse_k1\" \"Raw_RUVse_k2\" \"Raw_RUVse_k3\" #> [16] \"TC_RUVg_k1\" \"TC_RUVg_k2\" \"TC_RUVg_k3\" #> [19] \"TC_RUVs_k1\" \"TC_RUVs_k2\" \"TC_RUVs_k3\" #> [22] \"TC_RUVse_k1\" \"TC_RUVse_k2\" \"TC_RUVse_k3\" #> [25] \"UQ_RUVg_k1\" \"UQ_RUVg_k2\" \"UQ_RUVg_k3\" #> [28] \"UQ_RUVs_k1\" \"UQ_RUVs_k2\" \"UQ_RUVs_k3\" #> [31] \"UQ_RUVse_k1\" \"UQ_RUVse_k2\" \"UQ_RUVse_k3\" #> [34] \"TMM_RUVg_k1\" \"TMM_RUVg_k2\" \"TMM_RUVg_k3\" #> [37] \"TMM_RUVs_k1\" \"TMM_RUVs_k2\" \"TMM_RUVs_k3\" #> [40] \"TMM_RUVse_k1\" \"TMM_RUVse_k2\" \"TMM_RUVse_k3\" #> [43] \"DESeq_RUVg_k1\" \"DESeq_RUVg_k2\" \"DESeq_RUVg_k3\" #> [46] \"DESeq_RUVs_k1\" \"DESeq_RUVs_k2\" \"DESeq_RUVs_k3\" #> [49] \"DESeq_RUVse_k1\" \"DESeq_RUVse_k2\" \"DESeq_RUVse_k3\" #> [52] \"PossionSeq_RUVg_k1\" \"PossionSeq_RUVg_k2\" \"PossionSeq_RUVg_k3\" #> [55] \"PossionSeq_RUVs_k1\" \"PossionSeq_RUVs_k2\" \"PossionSeq_RUVs_k3\" #> [58] \"PossionSeq_RUVse_k1\" \"PossionSeq_RUVse_k2\" \"PossionSeq_RUVse_k3\" head(enONE::Counts(Enone, slot=\"sample\", method=\"DESeq_RUVg_k2\"))[,1:5] #> D1 D2 D3 D4 D5 #> ENSG00000227232 8.513936 11.645203 12.431441 7.453572 11.104087 #> ENSG00000268903 3.305409 8.100305 5.277670 26.325845 7.088225 #> ENSG00000269981 2.270299 4.876642 2.402698 18.118129 6.068101 #> ENSG00000279457 11.895695 9.998730 8.760526 11.204313 16.375896 #> ENSG00000225630 13.767575 16.292915 15.365490 10.140265 17.834578 #> ENSG00000237973 28.042040 37.277287 39.847772 34.078301 33.952026"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"evaluation","dir":"Articles","previous_headings":"Standard workflow > Running enONE","what":"Evaluation","title":"enONE_tutorial","text":"evaluate performance normalization, enONE leverages eight normalization performance metrics related different aspects distribution gene expression measures. eight metrics listed : BIO_SIM: Similarity biological groups. average silhouette width clusters defined condition column, computed Euclidean distance metric first 3 expression PCs (default). Large values BIO_SIM desirable. EN_SIM: Similarity enrichment groups. average silhouette width clusters defined enrich column, computed Euclidean distance metric first 3 expression PCs (default). Large values EN_SIM desirable. BAT_SIM: Similarity batch groups. average silhouette width clusters defined batch column, computed Euclidean distance metric first 3 expression PCs (default). Low values BAT_SIM desirable. PAM_SIM: Similarity PAM clustering groups. maximum average silhouette width clusters defined PAM clustering (cluster eval.pam.k), computed Euclidean distance metric first 3 expression PCs (default). Large values PAM_SIM desirable. WV_COR: Preservation biological variation. R2 measure regression first 3 expression PCs first eval.pc.n PCs positive evaluation genes (PosEvaluation) sub-matrix raw count. Large values WV_COR desirable. UV_COR: Removal unwanted variation. R2 measure regression first 3 expression PCs first eval.pc.n PCs negative evaluation genes (NegEvaluation) sub-matrix raw count. Low values UV_COR desirable. RLE_MED: mean squared-median Relative Log Expression (RLE). Low values RLE_MED desirable. RLE_IQR: variance inter-quartile range (IQR) RLE. Low values RLE_IQR desirable. Evaluation metrics can assessed getMetrics Evaluation score average rank performance metrics, can assessed getScore","code":"getMetrics(Enone)[1:5,] #> BIO_SIM EN_SIM BATCH_SIM PAM_SIM RLE_MED #> DESeq_RUVg_k2 -0.02467593 0.3576650 -0.03483061 0.5713398 9.319387e-06 #> PossionSeq_RUVs_k3 0.10240479 0.3374029 -0.03965069 0.5556398 9.971803e-05 #> TMM_RUVg_k2 -0.02664678 0.3518415 -0.03524885 0.5595637 8.500478e-05 #> DESeq_RUVg_k3 -0.04651463 0.3485155 -0.04427468 0.5708689 1.370353e-05 #> DESeq_RUVs_k1 0.03352455 0.3391415 -0.04444996 0.5224180 7.861089e-06 #> RLE_IQR WV_COR UV_COR #> DESeq_RUVg_k2 0.011996148 0.7401496 0.3258051 #> PossionSeq_RUVs_k3 0.012314397 0.7709943 0.3154878 #> TMM_RUVg_k2 0.012089861 0.7404610 0.3288269 #> DESeq_RUVg_k3 0.011133971 0.6688187 0.4166522 #> DESeq_RUVs_k1 0.009145155 0.6495402 0.3680677 getScore(Enone)[1:5,] #> BIO_SIM EN_SIM BATCH_SIM PAM_SIM RLE_MED RLE_IQR WV_COR #> DESeq_RUVg_k2 32 59 40 59 56 25 20 #> PossionSeq_RUVs_k3 60 23 47 50 33 23 23 #> TMM_RUVg_k2 30 50 42 54 39 24 21 #> DESeq_RUVg_k3 20 48 53 58 54 29 12 #> DESeq_RUVs_k1 55 32 54 25 57 32 5 #> UV_COR SCORE #> DESeq_RUVg_k2 59 43.750 #> PossionSeq_RUVs_k3 60 39.875 #> TMM_RUVg_k2 58 39.750 #> DESeq_RUVg_k3 39 39.125 #> DESeq_RUVs_k1 52 39.000"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"select-the-suitable-normalization-for-subsequent-analysis","dir":"Articles","previous_headings":"","what":"Select the suitable normalization for subsequent analysis","title":"enONE_tutorial","text":"enONE provides biplot exploit full space normalization methods, can turn interactive mode interactive=TRUE. plot, point corresponds normalization procedure colored performance score (mean eight scone performance metric ranks). blue arrows correspond PCA loadings performance metrics. direction length blue arrow can interpreted measure much metric contributed first two PCs , use top-ranked procedure DESeq_RUVg_k2 downstream analysis. noted, normalized counts returned enONE run step (.e., return.norm=FALSE), can manually perform normalization method, e.g.,","code":"# get performance score enScore <- getScore(Enone) # perform PCA based on evaluation score, excluding BAT_SIM column (3) if no batch information provided, and SCORE column (9). # pca.eval <- prcomp(enScore[,-c(3, 9)], scale = TRUE) pca.eval <- prcomp(enScore[,-c(9)], scale = TRUE) # pca biplot PCA_Biplot(pca.eval, score = enScore$SCORE, interactive = FALSE) # select normalization norm.method <- rownames(enScore[1,]) # get normalized counts norm.data <- enONE::Counts(Enone, slot = \"sample\", method = norm.method) # get normalization factors norm.factors <- getFactor(Enone, slot = \"sample\", method = norm.method) norm.method #> [1] \"DESeq_RUVg_k2\" # perform normalization Enone <- UseNormalization(Enone, slot = \"sample\", method = norm.method) # get normalized counts norm.data <- Counts(Enone, slot = \"sample\", method = norm.method)"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"effect-of-normalization","dir":"Articles","previous_headings":"","what":"Effect of normalization","title":"enONE_tutorial","text":"perform PCA based count matrix sample interest normalization, demonstrating effect normalization.","code":"# create sample name, e.g., Y3.Input samples_name <- paste(Enone$condition, Enone$replicate, sep=\".\") # PCA for raw count p1 <- PCAplot(enONE::Counts(Enone, slot=\"sample\", \"Raw\"), color = Enone$batch, shape = Enone$enrich, label = samples_name, vst.norm = TRUE) + ggtitle(\"Before normalization\") # PCA for normalized count p2 <- PCAplot(log1p(norm.data), color = Enone$batch, shape = Enone$enrich, label = samples_name, vst.norm = FALSE) + ggtitle(\"After normalization\") # combine two plots p1 + p2 #> Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider #> increasing max.overlaps"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"find-enrichment","dir":"Articles","previous_headings":"","what":"Find enrichment","title":"enONE_tutorial","text":"enONE package can help find enrichment genes biological groups via differential expression. can identify genes significantly increased enrichment samples compared input samples. FindEnrichment automates processes groups provided condition column. default, enriched genes (NAD-RNA ) defined fold change normalized transcript counts ≥ 2 (logfc.cutoff = 1), FDR < 0.05 (p.cutoff = 0.05) enrichment samples compared input samples. Use getEnrichment retrieve list enrichment result tables. enrichment table data.frame list genes rows, associated information columns (GeneID, logFC, p-values, etc.). following columns present table: GeneID: ID genes. logFC: log2 fold-change enrichment input samples. Positive values indicate gene highly enriched enrichment group. logCPM: log2 CPM (counts per million) average expression samples. LR: Likelihood ratio likelihood ratio test. PValue: p-value likelihood ratio test. FDR: False discovery rate p-value, default “BH” method applied. convert list enrichment result tables data frame long format, enONE package provide reduceRes function task. Finally, can use BetweenStatPlot compare global extent NAD-RNA modification level groups.","code":"# find all enriched genes Enone <- FindEnrichment(Enone, slot=\"sample\", norm.method = norm.method, logfc.cutoff = 1, p.cutoff = 0.05) #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... # get filtered enrichment results res.sig.ls <- getEnrichment(Enone, slot=\"sample\", filter=TRUE) # count number of enrichment in each group unlist(lapply(res.sig.ls, nrow)) #> Y.Enrich_Y.Input M.Enrich_M.Input O.Enrich_O.Input #> 579 672 683 head(res.sig.ls[[1]]) #> GeneID logFC logCPM LR PValue FDR #> 1 ENSG00000013275 2.271643 7.518198 731.6422 3.936608e-161 5.444329e-157 #> 2 ENSG00000113387 2.643018 9.732157 631.6712 2.164465e-139 1.496728e-135 #> 3 ENSG00000104853 2.244442 7.839256 587.7484 7.738626e-130 3.567507e-126 #> 4 ENSG00000105185 2.094086 6.147852 558.6392 1.662061e-123 5.746574e-120 #> 5 ENSG00000143110 3.084209 9.115976 550.0416 1.232914e-121 3.410239e-118 #> 6 ENSG00000173915 2.324782 7.005478 483.7653 3.239173e-107 7.466295e-104 # simplify group id names(res.sig.ls) <- c(\"Young\", \"Mid\", \"Old\") # logfc.col specify the name of logFC column nad_df1 <- reduceRes(res.sig.ls, logfc.col = \"logFC\") # convert the Group column as factor nad_df1$Group <- factor(nad_df1$Group, levels = unique(nad_df1$Group)) # draw plot bxp1 <- BetweenStatPlot(nad_df1, x=\"Group\", y=\"logFC\", color=\"Group\", step.increase = 0.6, add.p = \"p\", comparisons = list(c(\"Young\", \"Mid\"), c(\"Young\", \"Old\"))) bxp1"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"handling-synthetic-spike-in-rna","dir":"Articles","previous_headings":"","what":"Handling synthetic spike-in RNA","title":"enONE_tutorial","text":"Since synthetic RNAs, one 5% NAD-caps another 100% m7G-caps, included, can use spike-ins determine capture sensitivity specificity. synEnrichment calculate enrichment levels synthetic spike-ins given normalization method. DotPlot can used visualize enrichment levels synthetic spike-. Syn1, contained 5% NAD-RNA, significantly enriched, whereas enrichment found Syn2 made 100% m7G-RNA.","code":"# compute synthetic spike-in enrichment syn_level <- synEnrichment(Enone, method=norm.method, log=TRUE) # transform to long format syn_df <- as.data.frame(syn_level) %>% rownames_to_column(\"syn_id\") %>% pivot_longer(cols = -syn_id, names_to = \"id\", values_to = \"logFC\") %>% left_join(meta[,c(\"id\",\"condition\")], by=\"id\") # remove suffix of condition for simplification syn_df$condition <- gsub(\"\\\\..*\", \"\", syn_df$condition) # rename facet label samples_label <- setNames(c(\"5% NAD-RNA\", \"100% m7G-RNA\"), nm=c(\"Syn1\", \"Syn2\")) # draw dotplot DotPlot(syn_df, x=\"syn_id\", y=\"logFC\", fill=\"syn_id\") + theme(legend.position = \"none\") + scale_x_discrete(labels=samples_label) #> Bin width defaults to 1/30 of the range of the data. Pick better value with #> `binwidth`. # save Enone data save(Enone, file=\"data/Enone.RData\")"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"enONE_tutorial","text":"","code":"sessionInfo() #> R version 4.4.0 (2024-04-24) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] patchwork_1.2.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 #> [5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 #> [9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 enONE_1.0.0 #> #> loaded via a namespace (and not attached): #> [1] pbapply_1.7-2 rlang_1.1.3 #> [3] magrittr_2.0.3 matrixStats_1.3.0 #> [5] compiler_4.4.0 flexmix_2.3-19 #> [7] systemfonts_1.0.6 vctrs_0.6.5 #> [9] pkgconfig_2.0.3 crayon_1.5.2 #> [11] fastmap_1.1.1 backports_1.4.1 #> [13] XVector_0.44.0 labeling_0.4.3 #> [15] utf8_1.2.4 rmarkdown_2.26 #> [17] tzdb_0.4.0 UCSC.utils_1.0.0 #> [19] ragg_1.3.1 xfun_0.43 #> [21] modeltools_0.2-23 zlibbioc_1.50.0 #> [23] cachem_1.0.8 GenomeInfoDb_1.40.0 #> [25] jsonlite_1.8.8 EnvStats_2.8.1 #> [27] highr_0.10 DelayedArray_0.30.1 #> [29] fpc_2.2-12 BiocParallel_1.38.0 #> [31] broom_1.0.5 parallel_4.4.0 #> [33] prabclus_2.3-3 cluster_2.1.6 #> [35] R6_2.5.1 stringi_1.8.4 #> [37] bslib_0.7.0 limma_3.60.0 #> [39] car_3.1-2 GenomicRanges_1.56.0 #> [41] jquerylib_0.1.4 diptest_0.77-1 #> [43] Rcpp_1.0.12 SummarizedExperiment_1.34.0 #> [45] knitr_1.46 IRanges_2.38.0 #> [47] splines_4.4.0 timechange_0.3.0 #> [49] Matrix_1.7-0 nnet_7.3-19 #> [51] tidyselect_1.2.1 abind_1.4-5 #> [53] yaml_2.3.8 codetools_0.2-20 #> [55] lattice_0.22-6 withr_3.0.0 #> [57] Biobase_2.64.0 evaluate_0.23 #> [59] desc_1.4.3 mclust_6.1.1 #> [61] kernlab_0.9-32 pillar_1.9.0 #> [63] ggpubr_0.6.0 MatrixGenerics_1.16.0 #> [65] carData_3.0-5 stats4_4.4.0 #> [67] plotly_4.10.4 generics_0.1.3 #> [69] hms_1.1.3 S4Vectors_0.42.0 #> [71] munsell_0.5.1 scales_1.3.0 #> [73] class_7.3-22 glue_1.7.0 #> [75] lazyeval_0.2.2 tools_4.4.0 #> [77] robustbase_0.99-2 data.table_1.15.4 #> [79] locfit_1.5-9.9 ggsignif_0.6.4 #> [81] fs_1.6.4 grid_4.4.0 #> [83] edgeR_4.2.0 colorspace_2.1-0 #> [85] GenomeInfoDbData_1.2.12 cli_3.6.2 #> [87] textshaping_0.3.7 fansi_1.0.6 #> [89] viridisLite_0.4.2 S4Arrays_1.4.0 #> [91] paintingr_0.1.0 gtable_0.3.5 #> [93] DEoptimR_1.1-3 rstatix_0.7.2 #> [95] DESeq2_1.44.0 sass_0.4.9 #> [97] digest_0.6.35 BiocGenerics_0.50.0 #> [99] SparseArray_1.4.3 ggrepel_0.9.5 #> [101] farver_2.1.1 htmlwidgets_1.6.4 #> [103] memoise_2.0.1 htmltools_0.5.8.1 #> [105] pkgdown_2.0.9 lifecycle_1.0.4 #> [107] httr_1.4.7 statmod_1.5.0 #> [109] MASS_7.3-60.2"},{"path":"https://thereallda.github.io/enONE/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Dean Li. Author, maintainer.","code":""},{"path":"https://thereallda.github.io/enONE/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Li D (2024). enONE: Epitranscriptional NAD-capped RNA analysis spike-based Omic-level Normalization Evaluation. R package version 1.0.0, https://thereallda.github.io/enONE/.","code":"@Manual{, title = {enONE: Epitranscriptional NAD-capped RNA analysis by spike-in based Omic-level Normalization and Evaluation}, author = {Dean Li}, year = {2024}, note = {R package version 1.0.0}, url = {https://thereallda.github.io/enONE/}, }"},{"path":"https://thereallda.github.io/enONE/index.html","id":"enone","dir":"","previous_headings":"","what":"Epitranscriptional NAD-capped RNA analysis by spike-in based Omic-level Normalization and Evaluation","title":"Epitranscriptional NAD-capped RNA analysis by spike-in based Omic-level Normalization and Evaluation","text":"enONE R package performing spike-based normalization NAD-capped RNA sequencing data, well comparing ranking performance different normalization strategies. enONE also provides toolkit handle NAD-RNA sequencing data.","code":""},{"path":"https://thereallda.github.io/enONE/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Epitranscriptional NAD-capped RNA analysis by spike-in based Omic-level Normalization and Evaluation","text":"can install development version enONE GitHub : Tutorial can found https://thereallda.github.io/enONE/ use enONE analysis, please cite publication: Epitranscriptome analysis NAD-capped RNA spike--based normalization prediction chronological age (https://doi.org/10.1016/j.isci.2023.108558)","code":"# install.packages(\"devtools\") devtools::install_github(\"thereallda/enONE\")"},{"path":"https://thereallda.github.io/enONE/reference/ApplyNormalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies normalization on sequencing data — ApplyNormalization","title":"Applies normalization on sequencing data — ApplyNormalization","text":"Applies normalization sequencing data","code":""},{"path":"https://thereallda.github.io/enONE/reference/ApplyNormalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies normalization on sequencing data — ApplyNormalization","text":"","code":"ApplyNormalization( data, scaling.method = c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"), ruv.norm = TRUE, ruv.k = 1, ruv.drop = 0, control.idx = NULL, sc.idx = NULL, enrich.idx = NULL, spike.in.prefix = NULL, synthetic.id = NULL )"},{"path":"https://thereallda.github.io/enONE/reference/ApplyNormalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies normalization on sequencing data — ApplyNormalization","text":"data un-normalized count data matrix shape n x p, n number samples p number features. scaling.method Vector normalization methods applied data. Available methods : c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"). Select one multiple methods. default normalization methods applied. ruv.norm Whether perform RUV normalization. ruv.k number factors unwanted variation estimated data. ruv.drop number singular values drop estimation unwanted variation, default drop first singular value represent difference enrichment input. control.idx Vector negative control genes RUV normalization. sc.idx numeric matrix specifying replicate samples compute count differences used estimate factors unwanted variation. enrich.idx Matrix two rows indicating column index enrichment input samples raw/normalized count data matrix. first row column index input second row column index enrichment samples. spike..prefix character specify prefix spike-id. synthetic.id Character vector string specifying name synthetic RNAs.","code":""},{"path":"https://thereallda.github.io/enONE/reference/ApplyNormalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies normalization on sequencing data — ApplyNormalization","text":"List objects containing normalized data associated normalization factors.","code":""},{"path":"https://thereallda.github.io/enONE/reference/AssessNormalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Assessments of normalization performance — AssessNormalization","title":"Assessments of normalization performance — AssessNormalization","text":"Assessments normalization performance","code":""},{"path":"https://thereallda.github.io/enONE/reference/AssessNormalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assessments of normalization performance — AssessNormalization","text":"","code":"AssessNormalization( data.ls, bio.group = NULL, enrich.group = NULL, batch.group = NULL, eval.pam.k = 2:6, eval.pc.n = 3, log = TRUE, pos.eval.set = NULL, neg.eval.set = NULL )"},{"path":"https://thereallda.github.io/enONE/reference/AssessNormalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assessments of normalization performance — AssessNormalization","text":"data.ls List containing normalized counts adjust factors adjusting unwanted variation. Output ApplyNormalization. bio.group Vector index indicating column index samples biological groups raw/normalized count data matrix. enrich.group Vector index indicating column index enrichment input samples raw/normalized count data matrix. batch.group Vector index indicating column index batch groups raw/normalized count data matrix. eval.pam.k Integer vector integers indicates number clusters PAM clustering performance evaluation, default: 2:6. eval.pc.n Integer indicates evaluation metrics calculated first nth PCs, default: 3. log Whether perform log2-transformation 1 offset data matrix, default: TRUE. pos.eval.set Vector genes id. neg.eval.set Vector genes id.","code":""},{"path":"https://thereallda.github.io/enONE/reference/AssessNormalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assessments of normalization performance — AssessNormalization","text":"List containing metrics matrix ranking matrix, sorted score methods top bottom.","code":""},{"path":"https://thereallda.github.io/enONE/reference/BetweenStatPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Box-violin plot comparing values between groups — BetweenStatPlot","title":"Box-violin plot comparing values between groups — BetweenStatPlot","text":"Box-violin plot comparing values groups","code":""},{"path":"https://thereallda.github.io/enONE/reference/BetweenStatPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Box-violin plot comparing values between groups — BetweenStatPlot","text":"","code":"BetweenStatPlot( data, x, y, color, palette = NULL, test = c(\"wilcox.test\", \"t.test\", \"none\"), add.p = c(\"p\", \"p.adj\"), comparisons = NULL, step.increase = 0.3, title = NULL )"},{"path":"https://thereallda.github.io/enONE/reference/BetweenStatPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Box-violin plot comparing values between groups — BetweenStatPlot","text":"data data frame (tibble). x grouping variable data. y value variable data. color color variable data. palette color palette different groups. test Perform wilcoxon rank sum test t-test test, must one c(\"wilcox.test\", \"t.test\", \"none\"). add.p Label p-value adjusted p-value, must one c(\"p\", \"p.adj\"). comparisons list length-2 vectors specifying groups interest compared. example compare groups \"\" vs \"B\" \"B\" vs \"C\", argument follow: comparisons = list(c(\"\", \"B\"), c(\"B\", \"C\")) step.increase Numeric vector increase fraction total height every additional comparison minimize overlap. title Plot title, default NULL.","code":""},{"path":"https://thereallda.github.io/enONE/reference/BetweenStatPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Box-violin plot comparing values between groups — BetweenStatPlot","text":"ggplot2 object","code":""},{"path":"https://thereallda.github.io/enONE/reference/Counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor for the ","title":"Accessor for the ","text":"Accessor \"counts\" slot Enone object.","code":""},{"path":"https://thereallda.github.io/enONE/reference/Counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor for the ","text":"","code":"Counts(object, slot = c(\"sample\", \"spike_in\"), method) Counts(object, slot = c(\"sample\", \"spike_in\"), method) <- value # S4 method for Enone,character,character Counts(object, slot = c(\"sample\", \"spike_in\"), method) # S4 method for Enone,character,character,matrix Counts(object, slot = c(\"sample\", \"spike_in\"), method) <- value"},{"path":"https://thereallda.github.io/enONE/reference/Counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor for the ","text":"object Enone. slot slot get, one sample spike_in. method counts matrix get, must one raw normalized counts matrix presented selected slot. value Raw normalized counts matrix.","code":""},{"path":"https://thereallda.github.io/enONE/reference/Counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor for the ","text":"matrix.","code":""},{"path":"https://thereallda.github.io/enONE/reference/CreateGroupMatrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a matrix for RUVSeq — CreateGroupMatrix","title":"Create a matrix for RUVSeq — CreateGroupMatrix","text":"Create matrix RUVSeq","code":""},{"path":"https://thereallda.github.io/enONE/reference/CreateGroupMatrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a matrix for RUVSeq — CreateGroupMatrix","text":"","code":"CreateGroupMatrix(group.vec)"},{"path":"https://thereallda.github.io/enONE/reference/CreateGroupMatrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a matrix for RUVSeq — CreateGroupMatrix","text":"group.vec vector indicating membership group.","code":""},{"path":"https://thereallda.github.io/enONE/reference/CreateGroupMatrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a matrix for RUVSeq — CreateGroupMatrix","text":"matrix.","code":""},{"path":"https://thereallda.github.io/enONE/reference/CreateGroupMatrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a matrix for RUVSeq — CreateGroupMatrix","text":"","code":"CreateGroupMatrix(c('a','b','b','c','c','c','a','d','d')) #> [,1] [,2] [,3] #> [1,] 1 7 -1 #> [2,] 2 3 -1 #> [3,] 4 5 6 #> [4,] 8 9 -1"},{"path":"https://thereallda.github.io/enONE/reference/DotPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot-plot with mean_sd bar — DotPlot","title":"Dot-plot with mean_sd bar — DotPlot","text":"Dot-plot mean_sd bar","code":""},{"path":"https://thereallda.github.io/enONE/reference/DotPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot-plot with mean_sd bar — DotPlot","text":"","code":"DotPlot(data, x, y, fill = NULL, palette = NULL)"},{"path":"https://thereallda.github.io/enONE/reference/DotPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot-plot with mean_sd bar — DotPlot","text":"data data.frame (tibble). x grouping variable data. y value variable data. fill fill variable data. palette fill palette different groups.","code":""},{"path":"https://thereallda.github.io/enONE/reference/DotPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dot-plot with mean_sd bar — DotPlot","text":"ggplot2 object","code":""},{"path":"https://thereallda.github.io/enONE/reference/FilterLowExprGene.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter Low Expressed Genes — FilterLowExprGene","title":"Filter Low Expressed Genes — FilterLowExprGene","text":"Filter Low Expressed Genes","code":""},{"path":"https://thereallda.github.io/enONE/reference/FilterLowExprGene.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter Low Expressed Genes — FilterLowExprGene","text":"","code":"FilterLowExprGene(x, group = NULL, min.count = 10)"},{"path":"https://thereallda.github.io/enONE/reference/FilterLowExprGene.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter Low Expressed Genes — FilterLowExprGene","text":"x Input data, can counts matrix Enone object. group Vector factor giving group membership oneway layout, appropriate, default: NULL. min.count Minimum count required least samples, default: 10.","code":""},{"path":"https://thereallda.github.io/enONE/reference/FilterLowExprGene.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter Low Expressed Genes — FilterLowExprGene","text":"Filtered count matrix updated Enone object.","code":""},{"path":"https://thereallda.github.io/enONE/reference/FindEnrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"Find enriched genes between enrich and input samples — FindEnrichment","title":"Find enriched genes between enrich and input samples — FindEnrichment","text":"Find enriched genes enrich input samples","code":""},{"path":"https://thereallda.github.io/enONE/reference/FindEnrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find enriched genes between enrich and input samples — FindEnrichment","text":"","code":"FindEnrichment( object, slot = c(\"sample\", \"spike_in\"), norm.method, logfc.cutoff = 1, p.cutoff = 0.05, only.pos = TRUE, ... )"},{"path":"https://thereallda.github.io/enONE/reference/FindEnrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find enriched genes between enrich and input samples — FindEnrichment","text":"object object. slot slot, one sample spike_in. norm.method normalization method use differential analysis, must one methods presented selected slot. logfc.cutoff Filter genes least X-fold difference (log2-scale) two groups samples, default: 1. p.cutoff Filter genes p.cutoff adjusted p-value, default: 0.05. .pos Whether significant differential genes consider genes positive fold-change. ... Additional parameters can passed edgeRDE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/FindEnrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find enriched genes between enrich and input samples — FindEnrichment","text":"updated Enone","code":""},{"path":"https://thereallda.github.io/enONE/reference/GeneSelection.html","id":null,"dir":"Reference","previous_headings":"","what":"Gene set selection — GeneSelection","title":"Gene set selection — GeneSelection","text":"Select negative control genes RUV normalization, positive negative evaluation genes assessment.","code":""},{"path":"https://thereallda.github.io/enONE/reference/GeneSelection.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gene set selection — GeneSelection","text":"","code":"GeneSelection(object, n.neg.control = 1000, n.pos.eval = 500, n.neg.eval = 500)"},{"path":"https://thereallda.github.io/enONE/reference/GeneSelection.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gene set selection — GeneSelection","text":"object Enone object n.neg.control Number negative control genes RUV normalization, default: 1000. n.pos.eval Number positive evaluation genes wanted variation assessment, default: 500. n.neg.eval Number negative evaluation genes unwanted variation assessment, default: 500.","code":""},{"path":"https://thereallda.github.io/enONE/reference/GeneSelection.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gene set selection — GeneSelection","text":"list genes","code":""},{"path":"https://thereallda.github.io/enONE/reference/OutlierTest.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Test — OutlierTest","title":"Outlier Test — OutlierTest","text":"Rosner’s outlier test principal component 1 assess remove potential outliers.","code":""},{"path":"https://thereallda.github.io/enONE/reference/OutlierTest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Test — OutlierTest","text":"","code":"OutlierTest(x, return = FALSE, remove = FALSE)"},{"path":"https://thereallda.github.io/enONE/reference/OutlierTest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Test — OutlierTest","text":"x Enone object return Whether return object simply perform test, default FALSE remove Whether remove outliers. TRUE, must paired \"return=TRUE\", default: FALSE","code":""},{"path":"https://thereallda.github.io/enONE/reference/OutlierTest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Test — OutlierTest","text":"updated Enone object","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCA_Biplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Biplot of individuals and variables — PCA_Biplot","title":"Biplot of individuals and variables — PCA_Biplot","text":"Biplot individuals variables","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCA_Biplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Biplot of individuals and variables — PCA_Biplot","text":"","code":"PCA_Biplot(object, score, pt.label = TRUE, interactive = FALSE)"},{"path":"https://thereallda.github.io/enONE/reference/PCA_Biplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Biplot of individuals and variables — PCA_Biplot","text":"object PCA object returned prcomp. score Vector performance scores Enone evaluation score. pt.label Whether plot point labels, default: TRUE. interactive Whether demonstrate plot interactively, default: FALSE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCA_Biplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Biplot of individuals and variables — PCA_Biplot","text":"plot","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCAplot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA plot from counts matrix — PCAplot","title":"PCA plot from counts matrix — PCAplot","text":"PCA plot counts matrix","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCAplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA plot from counts matrix — PCAplot","text":"","code":"PCAplot( object, use.pc = c(1, 2), color = NULL, label = NULL, shape = NULL, title = NULL, vst.norm = FALSE, palette = NULL, repel = TRUE )"},{"path":"https://thereallda.github.io/enONE/reference/PCAplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA plot from counts matrix — PCAplot","text":"object count matrix. use.pc two PCs used, default PC1 x-axis PC2 y-axis. color Vector indicates color mapping samples, default NULL. label Vector sample names labels, default NULL. shape Vector indicates shape mapping samples, default NULL. title Plot title, default NULL. vst.norm Whether perform vst transformation, default FALSE. palette color palette different groups. repel Whether use ggrepel avoid overlapping text labels , default TRUE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCAplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA plot from counts matrix — PCAplot","text":"ggplot2 object","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCAplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA plot from counts matrix — PCAplot","text":"Perform PCA based matrix using prcomp, visualized scatter plot.","code":""},{"path":"https://thereallda.github.io/enONE/reference/PS.Est.Depth.html","id":null,"dir":"Reference","previous_headings":"","what":"Esimate sequencing depth — PS.Est.Depth","title":"Esimate sequencing depth — PS.Est.Depth","text":"Functions PossionSeq (https://doi.org/10.1093/biostatistics/kxr031)","code":""},{"path":"https://thereallda.github.io/enONE/reference/PS.Est.Depth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Esimate sequencing depth — PS.Est.Depth","text":"","code":"PS.Est.Depth(n, iter = 5, ct.sum = 5, ct.mean = 0.5)"},{"path":"https://thereallda.github.io/enONE/reference/PS.Est.Depth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Esimate sequencing depth — PS.Est.Depth","text":"n data matrix. rows counts gene, columns counts experiment. iter Number iterations used. Default value: 5. default value usually good choice. ct.sum total number reads gene across experiments <= ct.sum, gene considered estimating sequencing depth. Default value: 5. ct.mean mean number reads gene across experiments <= ct.mean, gene considered estimating sequencing depth. Default value: 0.5.","code":""},{"path":"https://thereallda.github.io/enONE/reference/PS.Est.Depth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Esimate sequencing depth — PS.Est.Depth","text":"estimated sequencing depth. vector. product 1.","code":""},{"path":"https://thereallda.github.io/enONE/reference/PS.Est.Depth.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Esimate sequencing depth — PS.Est.Depth","text":"","code":"mat <- matrix(rnbinom(300, mu=100, size=1), ncol=5) PS.Est.Depth(mat) #> [1] 1.3929053 1.0767802 0.9736520 0.7609921 0.8998445"},{"path":"https://thereallda.github.io/enONE/reference/UseNormalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply specific normalization method — UseNormalization","title":"Apply specific normalization method — UseNormalization","text":"Apply specific normalization method","code":""},{"path":"https://thereallda.github.io/enONE/reference/UseNormalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply specific normalization method — UseNormalization","text":"","code":"UseNormalization(object, slot = c(\"sample\", \"spike_in\"), method)"},{"path":"https://thereallda.github.io/enONE/reference/UseNormalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply specific normalization method — UseNormalization","text":"object Enone object. slot slot, one sample spike_in. method normalization methods perform.","code":""},{"path":"https://thereallda.github.io/enONE/reference/UseNormalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply specific normalization method — UseNormalization","text":"updated Enone","code":""},{"path":"https://thereallda.github.io/enONE/reference/countReplicate.html","id":null,"dir":"Reference","previous_headings":"","what":"Count numbers of each members — countReplicate","title":"Count numbers of each members — countReplicate","text":"Count numbers members","code":""},{"path":"https://thereallda.github.io/enONE/reference/countReplicate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count numbers of each members — countReplicate","text":"","code":"countReplicate(group.vec)"},{"path":"https://thereallda.github.io/enONE/reference/countReplicate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count numbers of each members — countReplicate","text":"group.vec Vector members.","code":""},{"path":"https://thereallda.github.io/enONE/reference/countReplicate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count numbers of each members — countReplicate","text":"vector","code":""},{"path":"https://thereallda.github.io/enONE/reference/countReplicate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count numbers of each members — countReplicate","text":"","code":"countReplicate(c('a','b','b','c','c','c','a','d','d')) #> [1] 1 1 2 1 2 3 2 1 2"},{"path":"https://thereallda.github.io/enONE/reference/createEnone.html","id":null,"dir":"Reference","previous_headings":"","what":"Enone object and constructor — Enone-class","title":"Enone object and constructor — Enone-class","text":"Enone object extends SummarizedExperiment class. createEnone easy constructor Enone object","code":""},{"path":"https://thereallda.github.io/enONE/reference/createEnone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enone object and constructor — Enone-class","text":"","code":"createEnone( data, col.data, spike.in.prefix = NULL, input.id = \"Input\", enrich.id = \"Enrich\", synthetic.id = NULL )"},{"path":"https://thereallda.github.io/enONE/reference/createEnone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enone object and constructor — Enone-class","text":"data un-normalized count data matrix shape n x p, n number samples p number features. col.data data.frame least two columns (indicate condition enrich groups). Rows col.data correspond columns data. spike..prefix character specify prefix spike-id, e.g., \"^FB\" stands fly spike-id, default: NULL. input.id Input library id, must consistent enrich column col.data, e.g., \"Input\". enrich.id Enrich library id, must consistent enrich column col.data, e.g., \"Enrich\". synthetic.id Vector synthetic RNA id, e.g. c(\"Syn1\",\"Syn2\"), default: NULL.","code":""},{"path":"https://thereallda.github.io/enONE/reference/createEnone.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enone object and constructor — Enone-class","text":"Enone object","code":""},{"path":"https://thereallda.github.io/enONE/reference/createEnone.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Enone object and constructor — Enone-class","text":"Description slot: assay SummarizedExperiment::Assays object, contains counts. counts list holding raw/normalized counts sample spike_in. enone_factor list holding normalization factors sample spike_in. enone_metrics data frame normalization methods row metrics columns. enone_score data frame normalization methods row scores columns. enrichment list holding differential analysis results. enrichment_filtered lists holding filtered differential analysis results. parameter list parameters.","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-keepConsRows.html","id":null,"dir":"Reference","previous_headings":"","what":"Keep Consistent Rows in Object — .keepConsRows","title":"Keep Consistent Rows in Object — .keepConsRows","text":"Make sure rows counts slot assay consistent.","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-keepConsRows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keep Consistent Rows in Object — .keepConsRows","text":"","code":".keepConsRows(object)"},{"path":"https://thereallda.github.io/enONE/reference/dot-keepConsRows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keep Consistent Rows in Object — .keepConsRows","text":"object Enone object","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-keepConsRows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keep Consistent Rows in Object — .keepConsRows","text":"Updated Enone","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-mean_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"Statistics summary (mean and +/- sd) — .mean_sd","title":"Statistics summary (mean and +/- sd) — .mean_sd","text":"Statistics summary (mean +/- sd)","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-mean_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Statistics summary (mean and +/- sd) — .mean_sd","text":"","code":".mean_sd(x)"},{"path":"https://thereallda.github.io/enONE/reference/dot-mean_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Statistics summary (mean and +/- sd) — .mean_sd","text":"x Value","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-mean_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Statistics summary (mean and +/- sd) — .mean_sd","text":"Vector mean mean +/- sd","code":""},{"path":"https://thereallda.github.io/enONE/reference/edgeRDE.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper of edgeR procedure — edgeRDE","title":"Wrapper of edgeR procedure — edgeRDE","text":"Wrapper edgeR procedure","code":""},{"path":"https://thereallda.github.io/enONE/reference/edgeRDE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper of edgeR procedure — edgeRDE","text":"","code":"edgeRDE( counts, group, norm.factors = NULL, adjust.factors = NULL, design.formula = NULL, contrast.df = NULL, coef = NULL, logfc.cutoff = 1, p.cutoff = 0.05, only.pos = TRUE )"},{"path":"https://thereallda.github.io/enONE/reference/edgeRDE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper of edgeR procedure — edgeRDE","text":"counts un-normalized counts data matrix. group Vector length p mapping columns counts corresponding samples group. norm.factors Vector normalization factors p length. adjust.factors Matrix column indicates adjusting factors estimated RUV. design.formula Formula contrast.df Data frame contrast, extracting results first column vs. second column. coef Integer character vector indicating coefficients linear model tested equal zero. Values must columns column names design. logfc.cutoff Filter genes least X-fold difference (log2-scale) two groups samples, default: 1. p.cutoff Filter genes Y adjusted p-value, default: 0.05. .pos return positive genes filtered results res.sig.ls, default: TRUE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/edgeRDE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper of edgeR procedure — edgeRDE","text":"List containing differential analysis object, result table filtered result table.","code":""},{"path":"https://thereallda.github.io/enONE/reference/enONE.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalization and assessment in one function — enONE","title":"Normalization and assessment in one function — enONE","text":"Normalization assessment one function","code":""},{"path":"https://thereallda.github.io/enONE/reference/enONE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalization and assessment in one function — enONE","text":"","code":"enONE( object, auto = TRUE, return.norm = FALSE, n.neg.control = 1000, n.pos.eval = 500, n.neg.eval = 500, neg.control = NULL, pos.eval = NULL, neg.eval = NULL, scaling.method = c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"), ruv.norm = TRUE, ruv.k = 1, ruv.drop = 0, eval.pam.k = 2:6, eval.pc.n = 3 )"},{"path":"https://thereallda.github.io/enONE/reference/enONE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalization and assessment in one function — enONE","text":"object Enone object. auto Whether automatically select negative control, positive evaluation negative evaluation genes, default: TRUE. return.norm Whether return normalized counts object. default, return normalized counts reducing memory costs. n.neg.control Number negative control genes RUV normalization, default: 1000. n.pos.eval Number positive evaluation genes wanted variation assessment, default: 500. n.neg.eval Number negative evaluation genes unwanted variation assessment, default: 500. neg.control Vector negative control genes' id RUV normalization, default: NULL. pos.eval Vector positive evaluation genes' id wanted variation assessment, default: NULL. neg.eval Vector negative evaluation genes' id unwanted variation assessment, default: NULL. scaling.method Vector scaling methods applied data. Available methods : c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"). Select one multiple methods. default scaling methods applied. ruv.norm Whether perform RUV normalization. ruv.k number factors unwanted variation estimated data, default: 1. ruv.drop number singular values drop estimation unwanted variation, default: 0. eval.pam.k Integer vector integers indicates number clusters PAM clustering performance evaluation, default: 2:6. eval.pc.n Integer indicates evaluation metrics calculated first nth PCs, default: 3.","code":""},{"path":"https://thereallda.github.io/enONE/reference/enONE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalization and assessment in one function — enONE","text":"Enone object.","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVg.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove unwanted variation using control genes — enRUVg","title":"Remove unwanted variation using control genes — enRUVg","text":"Adapted RUVSeq (https://doi.org/10.1038%2Fnbt.2931)","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove unwanted variation using control genes — enRUVg","text":"","code":"enRUVg(object, log = TRUE, k = 1, tolerance = 1e-08, control.idx, drop = 1)"},{"path":"https://thereallda.github.io/enONE/reference/enRUVg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove unwanted variation using control genes — enRUVg","text":"object counts matrix. log Whether perform log2-transformation 1 offset data matrix, default: TRUE. k number factors unwanted variation estimated data. tolerance Tolerance selection number positive singular values, .e., singular value must larger tolerance considered positive. control.idx ID control genes. drop number singular values drop estimation unwanted variation, default drop first singular value represent difference enrichment input","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove unwanted variation using control genes — enRUVg","text":"list contain matrix unwanted factors (W) corrected counts matrix (normalizedCounts).","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVs.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove unwanted variation using control genes within replicates — enRUVs","title":"Remove unwanted variation using control genes within replicates — enRUVs","text":"Adapted RUVSeq (https://doi.org/10.1038%2Fnbt.2931)","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove unwanted variation using control genes within replicates — enRUVs","text":"","code":"enRUVs( object, log = TRUE, k = 2, tolerance = 1e-08, control.idx, sc.idx, drop = 1 )"},{"path":"https://thereallda.github.io/enONE/reference/enRUVs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove unwanted variation using control genes within replicates — enRUVs","text":"object counts matrix. log Whether perform log2-transformation 1 offset data matrix, default: TRUE. k number factors unwanted variation estimated data. tolerance Tolerance selection number positive singular values, .e., singular value must larger tolerance considered positive. control.idx ID control genes. sc.idx numeric matrix specifying replicate samples compute count differences used estimate factors unwanted variation (see details). drop number singular values drop estimation unwanted variation, default drop first singular value represent difference enrichment input","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove unwanted variation using control genes within replicates — enRUVs","text":"list contain matrix unwanted factors (W) corrected counts matrix (normalizedCounts).","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Remove unwanted variation using control genes within replicates — enRUVs","text":"row sc.idx correspond set replicate samples. number columns size largest set replicates; rows smaller sets padded -1 values. example, sets replicate samples (1,2,3),(4,5),(6,7,8), scIdx 1 2 3 4 5 -1 6 7 8","code":""},{"path":"https://thereallda.github.io/enONE/reference/getEnrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of enrichment results — getEnrichment","title":"Accessor of enrichment results — getEnrichment","text":"Get filtered enrichment","code":""},{"path":"https://thereallda.github.io/enONE/reference/getEnrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of enrichment results — getEnrichment","text":"","code":"getEnrichment(object, slot = c(\"sample\", \"spike_in\"), filter = FALSE) # S4 method for Enone,character getEnrichment(object, slot = c(\"sample\", \"spike_in\"), filter = FALSE)"},{"path":"https://thereallda.github.io/enONE/reference/getEnrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of enrichment results — getEnrichment","text":"object Enone. slot slot get, one sample spike_in. filter Whether get filtered enrichment, default FALSE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getEnrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of enrichment results — getEnrichment","text":"list enrichment table","code":""},{"path":"https://thereallda.github.io/enONE/reference/getFactor.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of Enone normalization factors — getFactor","title":"Accessor of Enone normalization factors — getFactor","text":"Accessor Enone normalization factors","code":""},{"path":"https://thereallda.github.io/enONE/reference/getFactor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of Enone normalization factors — getFactor","text":"","code":"getFactor(object, slot = c(\"sample\", \"spike_in\"), method) # S4 method for Enone,character,character getFactor(object, slot = c(\"sample\", \"spike_in\"), method)"},{"path":"https://thereallda.github.io/enONE/reference/getFactor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of Enone normalization factors — getFactor","text":"object Enone. slot slot get, one sample spike_in. method normalization methods get, must one methods presented selected slot.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getFactor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of Enone normalization factors — getFactor","text":"vector list factors.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getGeneSet.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of gene set — getGeneSet","title":"Accessor of gene set — getGeneSet","text":"Get gene set.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getGeneSet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of gene set — getGeneSet","text":"","code":"getGeneSet(object, name = c(\"NegControl\", \"NegEvaluation\", \"PosEvaluation\")) # S4 method for Enone,character getGeneSet(object, name = c(\"NegControl\", \"NegEvaluation\", \"PosEvaluation\"))"},{"path":"https://thereallda.github.io/enONE/reference/getGeneSet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of gene set — getGeneSet","text":"object Enone. name Name gene set, one NegControl, NegEvaluation, PosEvaluation","code":""},{"path":"https://thereallda.github.io/enONE/reference/getGeneSet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of gene set — getGeneSet","text":"Vector gene ID","code":""},{"path":"https://thereallda.github.io/enONE/reference/getMetrics.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of Enone metrics — getMetrics","title":"Accessor of Enone metrics — getMetrics","text":"Accessor Enone metrics","code":""},{"path":"https://thereallda.github.io/enONE/reference/getMetrics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of Enone metrics — getMetrics","text":"","code":"getMetrics(object) # S4 method for Enone getMetrics(object)"},{"path":"https://thereallda.github.io/enONE/reference/getMetrics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of Enone metrics — getMetrics","text":"object Enone.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getMetrics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of Enone metrics — getMetrics","text":"data.frame","code":""},{"path":"https://thereallda.github.io/enONE/reference/getParameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of Enone parameter — listParameter","title":"Accessor of Enone parameter — listParameter","text":"List parameters. Get specific parameter.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getParameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of Enone parameter — listParameter","text":"","code":"listParameter(object) getParameter(object, name) # S4 method for Enone listParameter(object) # S4 method for Enone,character getParameter(object, name)"},{"path":"https://thereallda.github.io/enONE/reference/getParameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of Enone parameter — listParameter","text":"object Enone. name Name parameter.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getParameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of Enone parameter — listParameter","text":"Vector parameter names Vector parameter","code":""},{"path":"https://thereallda.github.io/enONE/reference/getScore.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of Enone score — getScore","title":"Accessor of Enone score — getScore","text":"Accessor Enone score","code":""},{"path":"https://thereallda.github.io/enONE/reference/getScore.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of Enone score — getScore","text":"","code":"getScore(object) # S4 method for Enone getScore(object)"},{"path":"https://thereallda.github.io/enONE/reference/getScore.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of Enone score — getScore","text":"object Enone.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getScore.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of Enone score — getScore","text":"data.frame","code":""},{"path":"https://thereallda.github.io/enONE/reference/listNormalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of normalization methods — listNormalization","title":"Accessor of normalization methods — listNormalization","text":"List normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/listNormalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of normalization methods — listNormalization","text":"","code":"listNormalization(object) # S4 method for Enone listNormalization(object)"},{"path":"https://thereallda.github.io/enONE/reference/listNormalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of normalization methods — listNormalization","text":"object Enone.","code":""},{"path":"https://thereallda.github.io/enONE/reference/listNormalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of normalization methods — listNormalization","text":"Vector normalization methods","code":""},{"path":"https://thereallda.github.io/enONE/reference/normDESeq.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform DESeq normalization — normDESeq","title":"Perform DESeq normalization — normDESeq","text":"Perform DESeq normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/normDESeq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform DESeq normalization — normDESeq","text":"","code":"normDESeq(data)"},{"path":"https://thereallda.github.io/enONE/reference/normDESeq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform DESeq normalization — normDESeq","text":"data un-normalized count data matrix shape n x p, n number samples p number features.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normDESeq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform DESeq normalization — normDESeq","text":"List containing normalized counts normalized factors library size.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normPossionSeq.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform PossionSeq normalization — normPossionSeq","title":"Perform PossionSeq normalization — normPossionSeq","text":"Perform PossionSeq normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/normPossionSeq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform PossionSeq normalization — normPossionSeq","text":"","code":"normPossionSeq(data, ...)"},{"path":"https://thereallda.github.io/enONE/reference/normPossionSeq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform PossionSeq normalization — normPossionSeq","text":"data un-normalized count data matrix shape n x p, n number samples p number features. ... Additional parameters can passed PS.Est.Depth.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normPossionSeq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform PossionSeq normalization — normPossionSeq","text":"List containing normalized counts normalized factors library size.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normRUV.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform RUV normalization — normRUV","title":"Perform RUV normalization — normRUV","text":"Perform RUV normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/normRUV.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform RUV normalization — normRUV","text":"","code":"normRUV( data, control.idx = NULL, sc.idx = NULL, method = c(\"RUVg\", \"RUVs\", \"RUVse\"), k = 1, drop = 0, log = TRUE )"},{"path":"https://thereallda.github.io/enONE/reference/normRUV.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform RUV normalization — normRUV","text":"data un-normalized count data matrix shape n x p, n number samples p number features. control.idx Vector control genes' id. sc.idx numeric matrix specifying replicate samples compute count differences used estimate factors unwanted variation. method Perform RUVg RUVs normalization. k number factors unwanted variation estimated data. drop number singular values drop estimation unwanted variation, default drop first singular value represent difference enrichment input. log Whether perform log2-transformation 1 offset data matrix (default: TRUE), normalized counts returned non-log format.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normRUV.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform RUV normalization — normRUV","text":"List containing normalized counts adjust factors adjusting unwanted variation.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normTC.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform total count normalization — normTC","title":"Perform total count normalization — normTC","text":"Perform total count normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/normTC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform total count normalization — normTC","text":"","code":"normTC(data)"},{"path":"https://thereallda.github.io/enONE/reference/normTC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform total count normalization — normTC","text":"data un-normalized count data matrix shape n x p, n number samples p number features.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normTC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform total count normalization — normTC","text":"List containing normalized counts normalized factors library size.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normTMM.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform TMM normalization — normTMM","title":"Perform TMM normalization — normTMM","text":"Perform TMM normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/normTMM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform TMM normalization — normTMM","text":"","code":"normTMM(data)"},{"path":"https://thereallda.github.io/enONE/reference/normTMM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform TMM normalization — normTMM","text":"data un-normalized count data matrix shape n x p, n number samples p number features.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normTMM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform TMM normalization — normTMM","text":"List containing normalized counts normalized factors library size.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normUQ.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform upper-quartile normalization — normUQ","title":"Perform upper-quartile normalization — normUQ","text":"Perform upper-quartile normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/normUQ.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform upper-quartile normalization — normUQ","text":"","code":"normUQ(data)"},{"path":"https://thereallda.github.io/enONE/reference/normUQ.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform upper-quartile normalization — normUQ","text":"data un-normalized count data matrix shape n x p, n number samples p number features.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normUQ.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform upper-quartile normalization — normUQ","text":"List containing normalized counts normalized factors library size.","code":""},{"path":"https://thereallda.github.io/enONE/reference/reduceRes.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine list of DE results — reduceRes","title":"Combine list of DE results — reduceRes","text":"Combine list DE results","code":""},{"path":"https://thereallda.github.io/enONE/reference/reduceRes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine list of DE results — reduceRes","text":"","code":"reduceRes(res.ls, logfc.col, levels = names(res.ls))"},{"path":"https://thereallda.github.io/enONE/reference/reduceRes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine list of DE results — reduceRes","text":"res.ls Named list differential analysis results tables. elements list correspond table differential analysis results two groups samples. logfc.col Column name log fold-change. levels Factor levels groups, default order element order res.ls.","code":""},{"path":"https://thereallda.github.io/enONE/reference/reduceRes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine list of DE results — reduceRes","text":"data.frame","code":""},{"path":"https://thereallda.github.io/enONE/reference/synEnrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"Enrichment level of synthetic RNA — synEnrichment","title":"Enrichment level of synthetic RNA — synEnrichment","text":"Enrichment level synthetic RNA","code":""},{"path":"https://thereallda.github.io/enONE/reference/synEnrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enrichment level of synthetic RNA — synEnrichment","text":"","code":"synEnrichment(object, method = \"TC\", log = TRUE)"},{"path":"https://thereallda.github.io/enONE/reference/synEnrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enrichment level of synthetic RNA — synEnrichment","text":"object Enone object. method normalization methods perform. log Whether return log2 values, default: TRUE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/synEnrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enrichment level of synthetic RNA — synEnrichment","text":"enrichment level synthetic RNA","code":""}] +[{"path":"https://thereallda.github.io/enONE/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022 enONE authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"前言","dir":"Articles","previous_headings":"","what":"前言","title":"enONE_tutorial-zh","text":"细胞内的核苷类代谢物，如烟酰胺腺嘌呤二核苷酸（NAD），能够被添加到mRNA 5’末端，形成NAD帽修饰的RNA（NAD-RNA）；这类非经典起始核苷酸（NCIN）修饰的RNA被统称为NCIN帽修饰的RNA（NCIN-RNA）。NCIN-RNA天然地将代谢物与基因表达紧密地联系起来，定义了一种新颖但功能未知的表观转录修饰。NCIN-RNA的表征是功能研究的前提。当前NCIN-RNA的表征方法，如ONE-seq和DO-seq，实现了代谢物帽修饰RNA在细胞和组织中的鉴定。然而，这些方法产出的数据会受到源于化学酶促标记及亲和富集反应等实验操作误差的影响。因此，这里提出了enONE这种NAD-RNA表观转录组的计算分析方法，实现NAD-RNA数据的定量和比较分析。 enONE 的R包可以在GitHub中找到（https://github.com/thereallda/enONE）","code":""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"enONE_tutorial-zh","text":"在这个教程中，我们会使用人类外周血单核细胞（PBMCs）的NAD-RNA-seq数据来展示 enONE 的工作流程。 要注意的是，这里混入了三种spike-RNAs： 1. 来自模式生物黑腹果蝇的总RNA，用于 enONE 的normalization； 2. Synthetic RNA，其中含有5％的NAD修饰形式（相对于m7G-RNA），用于确定富集灵敏度； 3. Synthetic RNA，其为100％ m7G加帽的形式，用于确定富集特异性。 Figure: Schematic workflow total RNAs PBMCs three sets spike-ins. 我们首先读取数据，包括基因表达量的count matrix和样本信息的metadata。matrix和样本信息的。metadata至少包括两列，即用于样本生物分组的”condition”列和用于样本富集分组的”enrich”列。 另外，metadata也可以包括其他信息，例如样本的批次信息。 接下来，我们使用count matrix和metadata创建一个 Enone 对象。 Enone 对象，包含了NAD-RNA-seq数据集的数据（raw normalized counts data）和结果信息（例如，数据标准化性能的打分和NAD-RNA富集结果）。 在构建 Enone 对象时，我们可以提供以下参数： spike-基因的前缀，例如果蝇Flybase基因id前缀为FB (spike..prefix = \"^FB\")； synthetic spike-in的id (synthetic.id = c(\"Syn1\", \"Syn2\"))，这需要与 counts_mat 中的行名相同； Input样本分组的id (input.id = \"Input\") 和Enrichment样本分组的id (enrich.id = \"Enrich\")，与enrich列相同。 其中, “Syn1”代表5% NAD-RNA spike-“Syn2”代表100% m7G-RNA spike-. synthetic.id 是可选的参数.","code":"library(enONE) library(tidyverse) library(patchwork) # read in metadata and counts matrix counts_mat <- read.csv(\"data/Counts.csv\", row.names = 1) meta <- read.csv(\"data/metadata.csv\", comment.char = \"#\") head(meta) #> id sample.id condition batch enrich #> 1 D1 Y2 Y.Input D Input #> 2 D2 Y10 Y.Input D Input #> 3 D3 Y14 Y.Input D Input #> 4 D4 Y16 Y.Input D Input #> 5 D5 M13 M.Input D Input #> 6 D6 M14 M.Input D Input # rownames of metadata should be consistent with the colnames of counts_mat rownames(meta) <- meta$id # prefix of Drosophila spike-in genes spikeInPrefix <- \"^FB\" # create Enone Enone <- createEnone(data = counts_mat, col.data = meta, spike.in.prefix = spikeInPrefix, synthetic.id = c(\"Syn1\", \"Syn2\"), input.id = \"Input\", enrich.id = \"Enrich\" ) Enone #> class: Enone #> dim: 76290 62 #> metadata(0): #> assays(1): '' #> rownames(76290): ENSG00000223972 ENSG00000227232 ... Syn1 Syn2 #> rowData names(3): GeneID SpikeIn Synthetic #> colnames(62): D1 D2 ... F22 F24 #> colData names(7): id id.1 ... enrich replicate"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"标准流程","dir":"Articles","previous_headings":"","what":"标准流程","title":"enONE_tutorial-zh","text":"enONE 流程包括以下四步: 质量控制（Quality control）; 基因集选取（Gene set selection）; 数据校正（Normalization procedures）; 数据校正效果评估（Normalization performance assessment）.","code":""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"质量控制quality-control","dir":"Articles","previous_headings":"标准流程","what":"质量控制（Quality control）","title":"enONE_tutorial-zh","text":"enONE 首先进行质量控制步骤，通过 FilterLowExprGene 保留至少在 n 个样本中至少具有 min.count 的基因来完成此步骤。n 由 group 中指定的最小样本组的大小确定。 此外，可以通过 OutlierTest对异常样本进行评估并进一步移除（return=TRUE）。由于没有样本被标记为异常值，因此我们将在后续分析中使用所有样本。","code":"Enone <- FilterLowExprGene(Enone, group = Enone$condition, min.count = 20) Enone #> class: Enone #> dim: 27661 62 #> metadata(0): #> assays(1): '' #> rownames(27661): ENSG00000227232 ENSG00000268903 ... Syn1 Syn2 #> rowData names(3): GeneID SpikeIn Synthetic #> colnames(62): D1 D2 ... F22 F24 #> colData names(7): id id.1 ... enrich replicate ## ronser\"s test for outlier assessment OutlierTest(Enone, return=FALSE) #> Rosner's outlier test #> i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier #> 1 0 1.602661e-15 51.77231 80.37523 15 1.552475 3.212165 FALSE #> 2 1 -1.317627e+00 51.14304 78.20437 19 1.554894 3.205977 FALSE #> 3 2 -2.642993e+00 50.50717 70.38990 41 1.445991 3.199662 FALSE"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"运行enone","dir":"Articles","previous_headings":"标准流程","what":"运行enONE","title":"enONE_tutorial-zh","text":"enONE 函数可以进行基因选择、数据标准化和标准化效果评估。该函数在 rowData 中存储选择的基因集，在 counts slot中返回标准化计数矩阵（当 return.norm=TRUE 时），在 enone_factor slot中返回标准化因子，在 enone_metrics 和 enone_scores slots中返回评估指标和得分。以下是这些步骤的详细描述。","code":"Enone <- enONE(Enone, scaling.method = c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"), ruv.norm = TRUE, ruv.k = 3, eval.pam.k = 2:6, eval.pc.n = 3, return.norm = TRUE ) #> Gene set selection for normalization and assessment... #> - The number of negative control genes for normalization: 1000 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> - The number of positive evaluation genes: 500 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> - The number of negative evaluation genes: 500 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> Apply normalization... #> - Scaling... #> - Regression-based normalization... #> Perform assessment..."},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"基因集选取gene-set-selection","dir":"Articles","previous_headings":"标准流程 > 运行enONE","what":"基因集选取（Gene set selection）","title":"enONE_tutorial-zh","text":"enONE 定义了三组基因，包括： 负对照（NegControl）：默认情况下，enONE 将 果蝇 spike-ins（或其他外源生物的RNA spike-）中 FDR 排名最低的 1,000 个基因作为负对照（不富集的基因），用于校正无关误差。 负评估（NegEvaluation）：默认情况下，enONE 将样本中 FDR 排名最低的 500 个基因作为负评估基因，用于评估无关误差。 正评估（PosEvaluation）：默认情况下，enONE 将样本中 FDR 排名最高的 500 个基因作为正评估基因，用于评估研究感兴趣的差异。 基因集的选择可以在 enONE 函数中使用 auto=TRUE 参数（默认值）自动定义，也可以在 neg.control, pos.eval, neg.eval 参数中分别提供。 可以通过 getGeneSet函数和基因集的名称（即\"NegControl\"、\"NegEvaluation\"、\"PosEvaluation\"）来获取相应的基因集。","code":"getGeneSet(Enone, name = \"NegControl\")[1:5] #> [1] \"FBgn0031247\" \"FBgn0031255\" \"FBgn0004583\" \"FBgn0031324\" \"FBgn0026397\""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"数据校正normalization","dir":"Articles","previous_headings":"标准流程 > 运行enONE","what":"数据校正（Normalization）","title":"enONE_tutorial-zh","text":"enONE 整合了全局缩放（global scaling）和基于回归的校正方法（regression-based normalization）来产生normalization方法。 对于全局缩放方法，enONE 整合了五种方法，包括： Total Count (TC); Upper-Quartile (UQ); Trimmed Mean M Values (TMM); DESeq; PossionSeq. 默认情况下，enONE 使用所有缩放方法，但用户可以在 scaling.method 参数中选择要使用的缩放程序。 对于regression-based方法，enONE 利用了三种RUV的方法，包括： RUVg; RUVs; RUVse. 例如，您可以通过选择 ruv.norm=TRUE, ruv.k=2 来执行使用前两个无关误差因子的 RUV。 RUVse 是类似于 RUVs 的方法。它基于每个富集样本或背景样本组的重复样本中的负对照基因来估计无关误差因子，其假设富集效应在重复之间是相对稳定的。 所有使用的normalization方法都可以使用 listNormalization 来提取。 校正后的数据可以通过 Counts 函数获取。","code":"listNormalization(Enone) #> [1] \"TC\" \"UQ\" \"TMM\" #> [4] \"DESeq\" \"PossionSeq\" \"Raw\" #> [7] \"Raw_RUVg_k1\" \"Raw_RUVg_k2\" \"Raw_RUVg_k3\" #> [10] \"Raw_RUVs_k1\" \"Raw_RUVs_k2\" \"Raw_RUVs_k3\" #> [13] \"Raw_RUVse_k1\" \"Raw_RUVse_k2\" \"Raw_RUVse_k3\" #> [16] \"TC_RUVg_k1\" \"TC_RUVg_k2\" \"TC_RUVg_k3\" #> [19] \"TC_RUVs_k1\" \"TC_RUVs_k2\" \"TC_RUVs_k3\" #> [22] \"TC_RUVse_k1\" \"TC_RUVse_k2\" \"TC_RUVse_k3\" #> [25] \"UQ_RUVg_k1\" \"UQ_RUVg_k2\" \"UQ_RUVg_k3\" #> [28] \"UQ_RUVs_k1\" \"UQ_RUVs_k2\" \"UQ_RUVs_k3\" #> [31] \"UQ_RUVse_k1\" \"UQ_RUVse_k2\" \"UQ_RUVse_k3\" #> [34] \"TMM_RUVg_k1\" \"TMM_RUVg_k2\" \"TMM_RUVg_k3\" #> [37] \"TMM_RUVs_k1\" \"TMM_RUVs_k2\" \"TMM_RUVs_k3\" #> [40] \"TMM_RUVse_k1\" \"TMM_RUVse_k2\" \"TMM_RUVse_k3\" #> [43] \"DESeq_RUVg_k1\" \"DESeq_RUVg_k2\" \"DESeq_RUVg_k3\" #> [46] \"DESeq_RUVs_k1\" \"DESeq_RUVs_k2\" \"DESeq_RUVs_k3\" #> [49] \"DESeq_RUVse_k1\" \"DESeq_RUVse_k2\" \"DESeq_RUVse_k3\" #> [52] \"PossionSeq_RUVg_k1\" \"PossionSeq_RUVg_k2\" \"PossionSeq_RUVg_k3\" #> [55] \"PossionSeq_RUVs_k1\" \"PossionSeq_RUVs_k2\" \"PossionSeq_RUVs_k3\" #> [58] \"PossionSeq_RUVse_k1\" \"PossionSeq_RUVse_k2\" \"PossionSeq_RUVse_k3\" head(enONE::Counts(Enone, slot=\"sample\", method=\"DESeq_RUVg_k2\"))[,1:5] #> D1 D2 D3 D4 D5 #> ENSG00000227232 8.513936 11.645203 12.431441 7.453572 11.104087 #> ENSG00000268903 3.305409 8.100305 5.277670 26.325845 7.088225 #> ENSG00000269981 2.270299 4.876642 2.402698 18.118129 6.068101 #> ENSG00000279457 11.895695 9.998730 8.760526 11.204313 16.375896 #> ENSG00000225630 13.767575 16.292915 15.365490 10.140265 17.834578 #> ENSG00000237973 28.042040 37.277287 39.847772 34.078301 33.952026"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"数据校正效果评估","dir":"Articles","previous_headings":"标准流程 > 运行enONE","what":"数据校正效果评估","title":"enONE_tutorial-zh","text":"enONE 利用了八个与基因表达分布的不同方面相关的标准化性能指标来评估数据标准化的性能。这八个指标分别为： BIO_SIM：生物组的相似性。通过计算前三个表达PCs（默认情况下）上的欧氏距离度量，由 condition 列定义的聚类的平均轮廓宽度。BIO_SIM越大越好。 EN_SIM：富集组的相似性。通过计算前三个表达PCs（默认情况下）上的欧氏距离度量，由 enrich 列定义的聚类的平均轮廓宽度。EN_SIM 越大越好。 BAT_SIM：批次组的相似性。通过计算前三个表达PCs（默认情况下）上的欧氏距离度量，由 batch 列定义的聚类的平均轮廓宽度。BAT_SIM 越小越好。 PAM_SIM：PAM 聚类组的相似性。通过计算前三个表达PCs（默认情况下）上的欧氏距离度量，由 PAM 聚类（根据 eval.pam.k 进行聚类）定义的聚类的最大平均轮廓宽度。PAM_SIM 越大越好。 WV_COR：研究相关变量的保留程度。对原始计数的正评估基因（PosEvaluation）子矩阵的前 eval.pc.n 个PCs 进行回归的 R2 测量。WV_COR 越大越好。 UV_COR：无关误差移除的程度。对原始计数的负评估基因（NegEvaluation）子矩阵的前 eval.pc.n 个PCs 进行回归的 R2 测量。UV_COR 越小越好。 RLE_MED：平均平方中位数相对对数表达（RLE）。RLE_MED 越小越好。 RLE_IQR：相对于四分位距（IQR）的方差。RLE_IQR 越小越好。 评估指标可以通过 getMetrics 进行获取。 评估指标通过转换为秩值，并通过平均各指标的秩值来获得特定方法的打分（score）。数据标准化效果的打分可以通过 getScore 获取。","code":"getMetrics(Enone)[1:5,] #> BIO_SIM EN_SIM BATCH_SIM PAM_SIM RLE_MED #> DESeq_RUVg_k2 -0.02467593 0.3576650 -0.03483061 0.5713398 9.319387e-06 #> PossionSeq_RUVs_k3 0.10240479 0.3374029 -0.03965069 0.5556398 9.971803e-05 #> TMM_RUVg_k2 -0.02664678 0.3518415 -0.03524885 0.5595637 8.500478e-05 #> DESeq_RUVg_k3 -0.04651463 0.3485155 -0.04427468 0.5708689 1.370353e-05 #> DESeq_RUVs_k1 0.03352455 0.3391415 -0.04444996 0.5224180 7.861089e-06 #> RLE_IQR WV_COR UV_COR #> DESeq_RUVg_k2 0.011996148 0.7401496 0.3258051 #> PossionSeq_RUVs_k3 0.012314397 0.7709943 0.3154878 #> TMM_RUVg_k2 0.012089861 0.7404610 0.3288269 #> DESeq_RUVg_k3 0.011133971 0.6688187 0.4166522 #> DESeq_RUVs_k1 0.009145155 0.6495402 0.3680677 getScore(Enone)[1:5,] #> BIO_SIM EN_SIM BATCH_SIM PAM_SIM RLE_MED RLE_IQR WV_COR #> DESeq_RUVg_k2 32 59 40 59 56 25 20 #> PossionSeq_RUVs_k3 60 23 47 50 33 23 23 #> TMM_RUVg_k2 30 50 42 54 39 24 21 #> DESeq_RUVg_k3 20 48 53 58 54 29 12 #> DESeq_RUVs_k1 55 32 54 25 57 32 5 #> UV_COR SCORE #> DESeq_RUVg_k2 59 43.750 #> PossionSeq_RUVs_k3 60 39.875 #> TMM_RUVg_k2 58 39.750 #> DESeq_RUVg_k3 39 39.125 #> DESeq_RUVs_k1 52 39.000"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"选择数据标准化方法","dir":"Articles","previous_headings":"","what":"选择数据标准化方法","title":"enONE_tutorial-zh","text":"enONE 提供了biplot来探索数据校正方法的全空间，可以通过 interactive=TRUE 来开启交互模式。 在这个图中，每个点对应一个标准化程序，并颜色代表性能得分（八个性能指标rank的均值）。蓝色箭头对应于性能指标的PCA载荷。蓝色箭头的方向和长度可以解释为每个指标对前两个主成分的贡献程度。 这里使用打分最高的标准化程序 DESeq_RUVg_k2 进行下游分析。 需要注意的是，如果 enONE 运行的时候没有返回校正后数据（.e., return.norm=FALSE），可以通过下面的代码手动执行数据标准化。","code":"# get performance score enScore <- getScore(Enone) # perform PCA based on evaluation score, excluding BAT_SIM column (3) if no batch information provided, and SCORE column (9). # pca.eval <- prcomp(enScore[,-c(3, 9)], scale = TRUE) pca.eval <- prcomp(enScore[,-c(9)], scale = TRUE) # pca biplot PCA_Biplot(pca.eval, score = enScore$SCORE, interactive = FALSE) # select normalization norm.method <- rownames(enScore[1,]) # get normalized counts norm.data <- enONE::Counts(Enone, slot = \"sample\", method = norm.method) # get normalization factors norm.factors <- getFactor(Enone, slot = \"sample\", method = norm.method) norm.method #> [1] \"DESeq_RUVg_k2\" # perform normalization Enone <- UseNormalization(Enone, slot = \"sample\", method = norm.method) # get normalized counts norm.data <- Counts(Enone, slot = \"sample\", method = norm.method)"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"数据校正的效果","dir":"Articles","previous_headings":"","what":"数据校正的效果","title":"enONE_tutorial-zh","text":"这里使用PCA展示数据校正的效果。","code":"# create sample name, e.g., Y3.Input samples_name <- paste(Enone$condition, Enone$replicate, sep=\".\") # PCA for raw count p1 <- PCAplot(enONE::Counts(Enone, slot=\"sample\", \"Raw\"), color = Enone$batch, shape = Enone$enrich, label = samples_name, vst.norm = TRUE) + ggtitle(\"Before normalization\") # PCA for normalized count p2 <- PCAplot(log1p(norm.data), color = Enone$batch, shape = Enone$enrich, label = samples_name, vst.norm = FALSE) + ggtitle(\"After normalization\") # combine two plots p1 + p2 #> Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider #> increasing max.overlaps"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"鉴定富集基因","dir":"Articles","previous_headings":"","what":"鉴定富集基因","title":"enONE_tutorial-zh","text":"enONE 可用于富集基因的鉴定。FindEnrichment 函数自动地根据 condition 列中的每个生物学分组鉴定富集的基因。 默认情况下，富集基因（即NAD-RNA）为Enrichment/Input ≥ 2（logfc.cutoff = 1），FDR < 0.05（p.cutoff = 0.05）的基因。 使用getEnrichment函数可以获取一个列表包含了各组的富集结果。 res.sig.ls 中每个表都是一个data.frame，其中行为基因，列为基因的相关信息（GeneID、logFC、p值等）。表格中包含以下列： GeneID：基因的ID。 logFC：富集样本和输入样本之间的log2倍变化。正值表示基因在富集组中更高度富集。 logCPM：所有样本平均表达的log2 CPM（counts per million）。 LR：似然比检验的似然比。 PValue：来自似然比检验的p值。 FDR：p值的假阳性发现率，默认使用\"BH\"方法。 enONE 提供 reduceRes 函数将各个富集表格的结果转换到同一个 data.frame 中。接着，可以使用 BetweenStatPlot 可视化各组NAD-RNA修饰水平。","code":"# find all enriched genes Enone <- FindEnrichment(Enone, slot=\"sample\", norm.method = norm.method, logfc.cutoff = 1, p.cutoff = 0.05) #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... # get filtered enrichment results res.sig.ls <- getEnrichment(Enone, slot=\"sample\", filter=TRUE) # count number of enrichment in each group unlist(lapply(res.sig.ls, nrow)) #> Y.Enrich_Y.Input M.Enrich_M.Input O.Enrich_O.Input #> 579 672 683 head(res.sig.ls[[1]]) #> GeneID logFC logCPM LR PValue FDR #> 1 ENSG00000013275 2.271643 7.518198 731.6422 3.936608e-161 5.444329e-157 #> 2 ENSG00000113387 2.643018 9.732157 631.6712 2.164465e-139 1.496728e-135 #> 3 ENSG00000104853 2.244442 7.839256 587.7484 7.738626e-130 3.567507e-126 #> 4 ENSG00000105185 2.094086 6.147852 558.6392 1.662061e-123 5.746574e-120 #> 5 ENSG00000143110 3.084209 9.115976 550.0416 1.232914e-121 3.410239e-118 #> 6 ENSG00000173915 2.324782 7.005478 483.7653 3.239173e-107 7.466295e-104 # simplify group id names(res.sig.ls) <- c(\"Young\", \"Mid\", \"Old\") # logfc.col specify the name of logFC column nad_df1 <- reduceRes(res.sig.ls, logfc.col = \"logFC\") # convert the Group column as factor nad_df1$Group <- factor(nad_df1$Group, levels = unique(nad_df1$Group)) # draw plot bxp1 <- BetweenStatPlot(nad_df1, x=\"Group\", y=\"logFC\", color=\"Group\", step.increase = 0.6, add.p = \"p\", comparisons = list(c(\"Young\", \"Mid\"), c(\"Young\", \"Old\"))) bxp1"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"处理synthetic-spike-in","dir":"Articles","previous_headings":"","what":"处理synthetic spike-in","title":"enONE_tutorial-zh","text":"由于样本中加入了synthetic RNA，其中一个带有5%的NAD帽，另一个带有100%的m7G帽，我们可以利用这些spike-ins来确定捕获的灵敏度和特异性。 synEnrichment计算具有给定标准化方法的spike-ins的富集水平。DotPlot可用于可视化spike-in的富集水平。 其中，具有5% NAD-RNA的Syn1有明显富集，而100% m7G-RNA的Syn2并没有出现富集，表明了富集实验的特异性。","code":"# compute synthetic spike-in enrichment syn_level <- synEnrichment(Enone, method=norm.method, log=TRUE) # transform to long format syn_df <- as.data.frame(syn_level) %>% rownames_to_column(\"syn_id\") %>% pivot_longer(cols = -syn_id, names_to = \"id\", values_to = \"logFC\") %>% left_join(meta[,c(\"id\",\"condition\")], by=\"id\") # remove suffix of condition for simplification syn_df$condition <- gsub(\"\\\\..*\", \"\", syn_df$condition) # rename facet label samples_label <- setNames(c(\"5% NAD-RNA\", \"100% m7G-RNA\"), nm=c(\"Syn1\", \"Syn2\")) # draw dotplot DotPlot(syn_df, x=\"syn_id\", y=\"logFC\", fill=\"syn_id\") + theme(legend.position = \"none\") + scale_x_discrete(labels=samples_label) #> Bin width defaults to 1/30 of the range of the data. Pick better value with #> `binwidth`. # save Enone data save(Enone, file=\"data/Enone.RData\")"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial-zh.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"enONE_tutorial-zh","text":"","code":"sessionInfo() #> R version 4.4.0 (2024-04-24) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] patchwork_1.2.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 #> [5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 #> [9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 enONE_1.0.0 #> #> loaded via a namespace (and not attached): #> [1] pbapply_1.7-2 rlang_1.1.3 #> [3] magrittr_2.0.3 matrixStats_1.3.0 #> [5] compiler_4.4.0 flexmix_2.3-19 #> [7] systemfonts_1.0.6 vctrs_0.6.5 #> [9] pkgconfig_2.0.3 crayon_1.5.2 #> [11] fastmap_1.1.1 backports_1.4.1 #> [13] XVector_0.44.0 labeling_0.4.3 #> [15] utf8_1.2.4 rmarkdown_2.26 #> [17] tzdb_0.4.0 UCSC.utils_1.0.0 #> [19] ragg_1.3.1 xfun_0.43 #> [21] modeltools_0.2-23 zlibbioc_1.50.0 #> [23] cachem_1.0.8 GenomeInfoDb_1.40.0 #> [25] jsonlite_1.8.8 EnvStats_2.8.1 #> [27] highr_0.10 DelayedArray_0.30.1 #> [29] fpc_2.2-12 BiocParallel_1.38.0 #> [31] broom_1.0.5 parallel_4.4.0 #> [33] prabclus_2.3-3 cluster_2.1.6 #> [35] R6_2.5.1 stringi_1.8.4 #> [37] bslib_0.7.0 limma_3.60.0 #> [39] car_3.1-2 GenomicRanges_1.56.0 #> [41] jquerylib_0.1.4 diptest_0.77-1 #> [43] Rcpp_1.0.12 SummarizedExperiment_1.34.0 #> [45] knitr_1.46 IRanges_2.38.0 #> [47] splines_4.4.0 timechange_0.3.0 #> [49] Matrix_1.7-0 nnet_7.3-19 #> [51] tidyselect_1.2.1 abind_1.4-5 #> [53] yaml_2.3.8 codetools_0.2-20 #> [55] lattice_0.22-6 withr_3.0.0 #> [57] Biobase_2.64.0 evaluate_0.23 #> [59] desc_1.4.3 mclust_6.1.1 #> [61] kernlab_0.9-32 pillar_1.9.0 #> [63] ggpubr_0.6.0 MatrixGenerics_1.16.0 #> [65] carData_3.0-5 stats4_4.4.0 #> [67] plotly_4.10.4 generics_0.1.3 #> [69] hms_1.1.3 S4Vectors_0.42.0 #> [71] munsell_0.5.1 scales_1.3.0 #> [73] class_7.3-22 glue_1.7.0 #> [75] lazyeval_0.2.2 tools_4.4.0 #> [77] robustbase_0.99-2 data.table_1.15.4 #> [79] locfit_1.5-9.9 ggsignif_0.6.4 #> [81] fs_1.6.4 grid_4.4.0 #> [83] edgeR_4.2.0 colorspace_2.1-0 #> [85] GenomeInfoDbData_1.2.12 cli_3.6.2 #> [87] textshaping_0.3.7 fansi_1.0.6 #> [89] viridisLite_0.4.2 S4Arrays_1.4.0 #> [91] paintingr_0.1.0 gtable_0.3.5 #> [93] DEoptimR_1.1-3 rstatix_0.7.2 #> [95] DESeq2_1.44.0 sass_0.4.9 #> [97] digest_0.6.35 BiocGenerics_0.50.0 #> [99] SparseArray_1.4.3 ggrepel_0.9.5 #> [101] farver_2.1.1 htmlwidgets_1.6.4 #> [103] memoise_2.0.1 htmltools_0.5.8.1 #> [105] pkgdown_2.0.9 lifecycle_1.0.4 #> [107] httr_1.4.7 statmod_1.5.0 #> [109] MASS_7.3-60.2"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"enONE_tutorial","text":"hub metabolite, nicotinamide adenine dinucleotide (NAD), can used initiating nucleotide RNA transcription result NAD-capped RNAs (NAD-RNAs). Epitranscriptome-wide profiling NAD-RNAs involves chemo-enzymatic labeling affinity-based enrichment; yet currently available computational analysis adequately remove variations inherently linked capture procedures. , propose spike--based normalization data-driven evaluation framework, enONE, omic-level analysis NAD-capped RNAs. enONE package implemented R publicly available https://github.com/thereallda/enONE.","code":""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"enONE_tutorial","text":"tutorial, demonstrate enONE workflow using NAD-RNA-seq data human peripheral blood mononuclear cells (PBMCs). Notably, included three types spike-RNAs: Total RNAs Drosophila melanogaster, invertebrate model organism well-annotated genome sequence, estimating unwanted variation; Synthetic RNAs, consisting 5% NAD- relative m7G-capped forms, used determine capture sensitivity; Synthetic RNAs, 100% m7G-capped forms, used determine capture specificity (Figure ). Figure: Schematic workflow total RNAs PBMCs three sets spike-ins. start reading data, including count matrix metadata. metadata include lease two columns, namely condition column biological grouping samples, enrich column enrichment grouping samples. Also, information, batch group can included metadata. Next, use count matrix metadata create Enone object. Enone object serves container contains data (raw normalized counts data) analysis (e.g., normalization performance evaluation score enrichment results) NAD-RNA-seq dataset. constructing Enone object, can provide following arguments: Prefix spike-genes (spike..prefix = \"^FB\"); id synthetic spike-(synthetic.id = c(\"Syn1\", \"Syn2\")), row names counts_mat; id input (input.id = \"Input\") enrichment (enrich.id = \"Enrich\"), enrich column. , “Syn1” represent spike-5% NAD-RNA “Syn2” spike-100% m7G-RNA. Notably, synthetic.id optional.","code":"library(enONE) library(tidyverse) library(patchwork) # read in metadata and counts matrix counts_mat <- read.csv(\"data/Counts.csv\", row.names = 1) meta <- read.csv(\"data/metadata.csv\", comment.char = \"#\") head(meta) #> id sample.id condition batch enrich #> 1 D1 Y2 Y.Input D Input #> 2 D2 Y10 Y.Input D Input #> 3 D3 Y14 Y.Input D Input #> 4 D4 Y16 Y.Input D Input #> 5 D5 M13 M.Input D Input #> 6 D6 M14 M.Input D Input # rownames of metadata should be consistent with the colnames of counts_mat rownames(meta) <- meta$id # prefix of Drosophila spike-in genes spikeInPrefix <- \"^FB\" # create Enone Enone <- createEnone(data = counts_mat, col.data = meta, spike.in.prefix = spikeInPrefix, synthetic.id = c(\"Syn1\", \"Syn2\"), input.id = \"Input\", enrich.id = \"Enrich\" ) Enone #> class: Enone #> dim: 76290 62 #> metadata(0): #> assays(1): '' #> rownames(76290): ENSG00000223972 ENSG00000227232 ... Syn1 Syn2 #> rowData names(3): GeneID SpikeIn Synthetic #> colnames(62): D1 D2 ... F22 F24 #> colData names(7): id id.1 ... enrich replicate"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"standard-workflow","dir":"Articles","previous_headings":"","what":"Standard workflow","title":"enONE_tutorial","text":"enONE workflow consists four steps: Quality control; Gene set selection; Normalization procedures; Normalization performance assessment.","code":""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"quality-control","dir":"Articles","previous_headings":"Standard workflow","what":"Quality Control","title":"enONE_tutorial","text":"enONE initiate quality control step filter lowly-expressed genes. step performed FilterLowExprGene keeping genes least min.count least n samples. n determined smallest group sample size specifying group . Additionally, outliers can assessed removed (return=TRUE) OutlierTest. Since sample flagged outlier, use samples following analysis.","code":"Enone <- FilterLowExprGene(Enone, group = Enone$condition, min.count = 20) Enone #> class: Enone #> dim: 27661 62 #> metadata(0): #> assays(1): '' #> rownames(27661): ENSG00000227232 ENSG00000268903 ... Syn1 Syn2 #> rowData names(3): GeneID SpikeIn Synthetic #> colnames(62): D1 D2 ... F22 F24 #> colData names(7): id id.1 ... enrich replicate ## ronser\"s test for outlier assessment OutlierTest(Enone, return=FALSE) #> Rosner's outlier test #> i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier #> 1 0 1.602661e-15 51.77231 80.37523 15 1.552475 3.212165 FALSE #> 2 1 -1.317627e+00 51.14304 78.20437 19 1.554894 3.205977 FALSE #> 3 2 -2.642993e+00 50.50717 70.38990 41 1.445991 3.199662 FALSE"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"running-enone","dir":"Articles","previous_headings":"Standard workflow","what":"Running enONE","title":"enONE_tutorial","text":"main enONE function can used perform gene selection, normalization, evaluation. function return selected gene set rowData, normalized count matrix counts slot (return.norm=TRUE), normalization factors (enone_factor slot), evaluation metrics scores enone_metrics enone_scores slots, respectively. Detailed steps described bellow.","code":"Enone <- enONE(Enone, scaling.method = c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"), ruv.norm = TRUE, ruv.k = 3, eval.pam.k = 2:6, eval.pc.n = 3, return.norm = TRUE ) #> Gene set selection for normalization and assessment... #> - The number of negative control genes for normalization: 1000 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> - The number of positive evaluation genes: 500 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> - The number of negative evaluation genes: 500 #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... #> Apply normalization... #> - Scaling... #> - Regression-based normalization... #> Perform assessment..."},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"gene-set-selection","dir":"Articles","previous_headings":"Standard workflow > Running enONE","what":"Gene set Selection","title":"enONE_tutorial","text":"gene set selection, enONE defined three sets control genes, including: Negative Control (NegControl): default, enONE define 1,000 least significantly enriched genes Drosophila spike-ins (RNA spike-exogenous organism), ranked FDR, negative controls adjustment unwanted variations. Negative Evaluation (NegEvaluation): default, enONE define 500 least significantly varied genes samples interest, ranked FDR, negative evaluation genes evaluation unwanted variations. Positive Evaluation (PosEvaluation): default, enONE define 500 significantly enriched genes samples interest, ranked FDR, positive evaluation genes evaluation wanted variations. Gene set selection can either automatically defined enONE function auto=TRUE parameter (default), provided neg.control, pos.eval, neg.eval parameters, respectively. Selected gene sets can assessed getGeneSet name gene set (.e., \"NegControl\", \"NegEvaluation\", \"PosEvaluation\").","code":"getGeneSet(Enone, name = \"NegControl\")[1:5] #> [1] \"FBgn0031247\" \"FBgn0031255\" \"FBgn0004583\" \"FBgn0031324\" \"FBgn0026397\""},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"normalization","dir":"Articles","previous_headings":"Standard workflow > Running enONE","what":"Normalization","title":"enONE_tutorial","text":"enONE implements global scaling regression-based methods generation normalization procedures. global scaling normalization procedures, five different scaling procedures implemented, including Total Count (TC); Upper-Quartile (UQ); Trimmed Mean M Values (TMM); DESeq; PossionSeq. default, enONE uses scaling methods, user can perform selected scaling procedures scaling.method parameter. regression-based procedures, enONE use three variants RUV estimate factors unwanted variation, including RUVg; RUVs; RUVse. instance, can perform RUV selected first two unwanted factors ruv.norm=TRUE, ruv.k=2. RUVse modification RUVs. estimated factors unwanted variation based negative control genes replicate samples assay group (.e., enrichment input), enrichment effect assumed constant. applied normalization methods can assessed listNormalization Normalized counts can assessed Counts","code":"listNormalization(Enone) #> [1] \"TC\" \"UQ\" \"TMM\" #> [4] \"DESeq\" \"PossionSeq\" \"Raw\" #> [7] \"Raw_RUVg_k1\" \"Raw_RUVg_k2\" \"Raw_RUVg_k3\" #> [10] \"Raw_RUVs_k1\" \"Raw_RUVs_k2\" \"Raw_RUVs_k3\" #> [13] \"Raw_RUVse_k1\" \"Raw_RUVse_k2\" \"Raw_RUVse_k3\" #> [16] \"TC_RUVg_k1\" \"TC_RUVg_k2\" \"TC_RUVg_k3\" #> [19] \"TC_RUVs_k1\" \"TC_RUVs_k2\" \"TC_RUVs_k3\" #> [22] \"TC_RUVse_k1\" \"TC_RUVse_k2\" \"TC_RUVse_k3\" #> [25] \"UQ_RUVg_k1\" \"UQ_RUVg_k2\" \"UQ_RUVg_k3\" #> [28] \"UQ_RUVs_k1\" \"UQ_RUVs_k2\" \"UQ_RUVs_k3\" #> [31] \"UQ_RUVse_k1\" \"UQ_RUVse_k2\" \"UQ_RUVse_k3\" #> [34] \"TMM_RUVg_k1\" \"TMM_RUVg_k2\" \"TMM_RUVg_k3\" #> [37] \"TMM_RUVs_k1\" \"TMM_RUVs_k2\" \"TMM_RUVs_k3\" #> [40] \"TMM_RUVse_k1\" \"TMM_RUVse_k2\" \"TMM_RUVse_k3\" #> [43] \"DESeq_RUVg_k1\" \"DESeq_RUVg_k2\" \"DESeq_RUVg_k3\" #> [46] \"DESeq_RUVs_k1\" \"DESeq_RUVs_k2\" \"DESeq_RUVs_k3\" #> [49] \"DESeq_RUVse_k1\" \"DESeq_RUVse_k2\" \"DESeq_RUVse_k3\" #> [52] \"PossionSeq_RUVg_k1\" \"PossionSeq_RUVg_k2\" \"PossionSeq_RUVg_k3\" #> [55] \"PossionSeq_RUVs_k1\" \"PossionSeq_RUVs_k2\" \"PossionSeq_RUVs_k3\" #> [58] \"PossionSeq_RUVse_k1\" \"PossionSeq_RUVse_k2\" \"PossionSeq_RUVse_k3\" head(enONE::Counts(Enone, slot=\"sample\", method=\"DESeq_RUVg_k2\"))[,1:5] #> D1 D2 D3 D4 D5 #> ENSG00000227232 8.513936 11.645203 12.431441 7.453572 11.104087 #> ENSG00000268903 3.305409 8.100305 5.277670 26.325845 7.088225 #> ENSG00000269981 2.270299 4.876642 2.402698 18.118129 6.068101 #> ENSG00000279457 11.895695 9.998730 8.760526 11.204313 16.375896 #> ENSG00000225630 13.767575 16.292915 15.365490 10.140265 17.834578 #> ENSG00000237973 28.042040 37.277287 39.847772 34.078301 33.952026"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"evaluation","dir":"Articles","previous_headings":"Standard workflow > Running enONE","what":"Evaluation","title":"enONE_tutorial","text":"evaluate performance normalization, enONE leverages eight normalization performance metrics related different aspects distribution gene expression measures. eight metrics listed : BIO_SIM: Similarity biological groups. average silhouette width clusters defined condition column, computed Euclidean distance metric first 3 expression PCs (default). Large values BIO_SIM desirable. EN_SIM: Similarity enrichment groups. average silhouette width clusters defined enrich column, computed Euclidean distance metric first 3 expression PCs (default). Large values EN_SIM desirable. BAT_SIM: Similarity batch groups. average silhouette width clusters defined batch column, computed Euclidean distance metric first 3 expression PCs (default). Low values BAT_SIM desirable. PAM_SIM: Similarity PAM clustering groups. maximum average silhouette width clusters defined PAM clustering (cluster eval.pam.k), computed Euclidean distance metric first 3 expression PCs (default). Large values PAM_SIM desirable. WV_COR: Preservation biological variation. R2 measure regression first 3 expression PCs first eval.pc.n PCs positive evaluation genes (PosEvaluation) sub-matrix raw count. Large values WV_COR desirable. UV_COR: Removal unwanted variation. R2 measure regression first 3 expression PCs first eval.pc.n PCs negative evaluation genes (NegEvaluation) sub-matrix raw count. Low values UV_COR desirable. RLE_MED: mean squared-median Relative Log Expression (RLE). Low values RLE_MED desirable. RLE_IQR: variance inter-quartile range (IQR) RLE. Low values RLE_IQR desirable. Evaluation metrics can assessed getMetrics Evaluation score average rank performance metrics, can assessed getScore","code":"getMetrics(Enone)[1:5,] #> BIO_SIM EN_SIM BATCH_SIM PAM_SIM RLE_MED #> DESeq_RUVg_k2 -0.02467593 0.3576650 -0.03483061 0.5713398 9.319387e-06 #> PossionSeq_RUVs_k3 0.10240479 0.3374029 -0.03965069 0.5556398 9.971803e-05 #> TMM_RUVg_k2 -0.02664678 0.3518415 -0.03524885 0.5595637 8.500478e-05 #> DESeq_RUVg_k3 -0.04651463 0.3485155 -0.04427468 0.5708689 1.370353e-05 #> DESeq_RUVs_k1 0.03352455 0.3391415 -0.04444996 0.5224180 7.861089e-06 #> RLE_IQR WV_COR UV_COR #> DESeq_RUVg_k2 0.011996148 0.7401496 0.3258051 #> PossionSeq_RUVs_k3 0.012314397 0.7709943 0.3154878 #> TMM_RUVg_k2 0.012089861 0.7404610 0.3288269 #> DESeq_RUVg_k3 0.011133971 0.6688187 0.4166522 #> DESeq_RUVs_k1 0.009145155 0.6495402 0.3680677 getScore(Enone)[1:5,] #> BIO_SIM EN_SIM BATCH_SIM PAM_SIM RLE_MED RLE_IQR WV_COR #> DESeq_RUVg_k2 32 59 40 59 56 25 20 #> PossionSeq_RUVs_k3 60 23 47 50 33 23 23 #> TMM_RUVg_k2 30 50 42 54 39 24 21 #> DESeq_RUVg_k3 20 48 53 58 54 29 12 #> DESeq_RUVs_k1 55 32 54 25 57 32 5 #> UV_COR SCORE #> DESeq_RUVg_k2 59 43.750 #> PossionSeq_RUVs_k3 60 39.875 #> TMM_RUVg_k2 58 39.750 #> DESeq_RUVg_k3 39 39.125 #> DESeq_RUVs_k1 52 39.000"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"select-the-suitable-normalization-for-subsequent-analysis","dir":"Articles","previous_headings":"","what":"Select the suitable normalization for subsequent analysis","title":"enONE_tutorial","text":"enONE provides biplot exploit full space normalization methods, can turn interactive mode interactive=TRUE. plot, point corresponds normalization procedure colored performance score (mean eight scone performance metric ranks). blue arrows correspond PCA loadings performance metrics. direction length blue arrow can interpreted measure much metric contributed first two PCs , use top-ranked procedure DESeq_RUVg_k2 downstream analysis. noted, normalized counts returned enONE run step (.e., return.norm=FALSE), can manually perform normalization method, e.g.,","code":"# get performance score enScore <- getScore(Enone) # perform PCA based on evaluation score, excluding BAT_SIM column (3) if no batch information provided, and SCORE column (9). # pca.eval <- prcomp(enScore[,-c(3, 9)], scale = TRUE) pca.eval <- prcomp(enScore[,-c(9)], scale = TRUE) # pca biplot PCA_Biplot(pca.eval, score = enScore$SCORE, interactive = FALSE) # select normalization norm.method <- rownames(enScore[1,]) # get normalized counts norm.data <- enONE::Counts(Enone, slot = \"sample\", method = norm.method) # get normalization factors norm.factors <- getFactor(Enone, slot = \"sample\", method = norm.method) norm.method #> [1] \"DESeq_RUVg_k2\" # perform normalization Enone <- UseNormalization(Enone, slot = \"sample\", method = norm.method) # get normalized counts norm.data <- Counts(Enone, slot = \"sample\", method = norm.method)"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"effect-of-normalization","dir":"Articles","previous_headings":"","what":"Effect of normalization","title":"enONE_tutorial","text":"perform PCA based count matrix sample interest normalization, demonstrating effect normalization.","code":"# create sample name, e.g., Y3.Input samples_name <- paste(Enone$condition, Enone$replicate, sep=\".\") # PCA for raw count p1 <- PCAplot(enONE::Counts(Enone, slot=\"sample\", \"Raw\"), color = Enone$batch, shape = Enone$enrich, label = samples_name, vst.norm = TRUE) + ggtitle(\"Before normalization\") # PCA for normalized count p2 <- PCAplot(log1p(norm.data), color = Enone$batch, shape = Enone$enrich, label = samples_name, vst.norm = FALSE) + ggtitle(\"After normalization\") # combine two plots p1 + p2 #> Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider #> increasing max.overlaps"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"find-enrichment","dir":"Articles","previous_headings":"","what":"Find enrichment","title":"enONE_tutorial","text":"enONE package can help find enrichment genes biological groups via differential expression. can identify genes significantly increased enrichment samples compared input samples. FindEnrichment automates processes groups provided condition column. default, enriched genes (NAD-RNA ) defined fold change normalized transcript counts ≥ 2 (logfc.cutoff = 1), FDR < 0.05 (p.cutoff = 0.05) enrichment samples compared input samples. Use getEnrichment retrieve list enrichment result tables. enrichment table data.frame list genes rows, associated information columns (GeneID, logFC, p-values, etc.). following columns present table: GeneID: ID genes. logFC: log2 fold-change enrichment input samples. Positive values indicate gene highly enriched enrichment group. logCPM: log2 CPM (counts per million) average expression samples. LR: Likelihood ratio likelihood ratio test. PValue: p-value likelihood ratio test. FDR: False discovery rate p-value, default “BH” method applied. convert list enrichment result tables data frame long format, enONE package provide reduceRes function task. Finally, can use BetweenStatPlot compare global extent NAD-RNA modification level groups.","code":"# find all enriched genes Enone <- FindEnrichment(Enone, slot=\"sample\", norm.method = norm.method, logfc.cutoff = 1, p.cutoff = 0.05) #> - Estimate dispersion & Fit GLM... #> - Testing differential genes... # get filtered enrichment results res.sig.ls <- getEnrichment(Enone, slot=\"sample\", filter=TRUE) # count number of enrichment in each group unlist(lapply(res.sig.ls, nrow)) #> Y.Enrich_Y.Input M.Enrich_M.Input O.Enrich_O.Input #> 579 672 683 head(res.sig.ls[[1]]) #> GeneID logFC logCPM LR PValue FDR #> 1 ENSG00000013275 2.271643 7.518198 731.6422 3.936608e-161 5.444329e-157 #> 2 ENSG00000113387 2.643018 9.732157 631.6712 2.164465e-139 1.496728e-135 #> 3 ENSG00000104853 2.244442 7.839256 587.7484 7.738626e-130 3.567507e-126 #> 4 ENSG00000105185 2.094086 6.147852 558.6392 1.662061e-123 5.746574e-120 #> 5 ENSG00000143110 3.084209 9.115976 550.0416 1.232914e-121 3.410239e-118 #> 6 ENSG00000173915 2.324782 7.005478 483.7653 3.239173e-107 7.466295e-104 # simplify group id names(res.sig.ls) <- c(\"Young\", \"Mid\", \"Old\") # logfc.col specify the name of logFC column nad_df1 <- reduceRes(res.sig.ls, logfc.col = \"logFC\") # convert the Group column as factor nad_df1$Group <- factor(nad_df1$Group, levels = unique(nad_df1$Group)) # draw plot bxp1 <- BetweenStatPlot(nad_df1, x=\"Group\", y=\"logFC\", color=\"Group\", step.increase = 0.6, add.p = \"p\", comparisons = list(c(\"Young\", \"Mid\"), c(\"Young\", \"Old\"))) bxp1"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"handling-synthetic-spike-in-rna","dir":"Articles","previous_headings":"","what":"Handling synthetic spike-in RNA","title":"enONE_tutorial","text":"Since synthetic RNAs, one 5% NAD-caps another 100% m7G-caps, included, can use spike-ins determine capture sensitivity specificity. synEnrichment calculate enrichment levels synthetic spike-ins given normalization method. DotPlot can used visualize enrichment levels synthetic spike-. Syn1, contained 5% NAD-RNA, significantly enriched, whereas enrichment found Syn2 made 100% m7G-RNA.","code":"# compute synthetic spike-in enrichment syn_level <- synEnrichment(Enone, method=norm.method, log=TRUE) # transform to long format syn_df <- as.data.frame(syn_level) %>% rownames_to_column(\"syn_id\") %>% pivot_longer(cols = -syn_id, names_to = \"id\", values_to = \"logFC\") %>% left_join(meta[,c(\"id\",\"condition\")], by=\"id\") # remove suffix of condition for simplification syn_df$condition <- gsub(\"\\\\..*\", \"\", syn_df$condition) # rename facet label samples_label <- setNames(c(\"5% NAD-RNA\", \"100% m7G-RNA\"), nm=c(\"Syn1\", \"Syn2\")) # draw dotplot DotPlot(syn_df, x=\"syn_id\", y=\"logFC\", fill=\"syn_id\") + theme(legend.position = \"none\") + scale_x_discrete(labels=samples_label) #> Bin width defaults to 1/30 of the range of the data. Pick better value with #> `binwidth`. # save Enone data save(Enone, file=\"data/Enone.RData\")"},{"path":"https://thereallda.github.io/enONE/articles/enONE_tutorial.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"enONE_tutorial","text":"","code":"sessionInfo() #> R version 4.4.0 (2024-04-24) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] patchwork_1.2.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 #> [5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 #> [9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 enONE_1.0.0 #> #> loaded via a namespace (and not attached): #> [1] pbapply_1.7-2 rlang_1.1.3 #> [3] magrittr_2.0.3 matrixStats_1.3.0 #> [5] compiler_4.4.0 flexmix_2.3-19 #> [7] systemfonts_1.0.6 vctrs_0.6.5 #> [9] pkgconfig_2.0.3 crayon_1.5.2 #> [11] fastmap_1.1.1 backports_1.4.1 #> [13] XVector_0.44.0 labeling_0.4.3 #> [15] utf8_1.2.4 rmarkdown_2.26 #> [17] tzdb_0.4.0 UCSC.utils_1.0.0 #> [19] ragg_1.3.1 xfun_0.43 #> [21] modeltools_0.2-23 zlibbioc_1.50.0 #> [23] cachem_1.0.8 GenomeInfoDb_1.40.0 #> [25] jsonlite_1.8.8 EnvStats_2.8.1 #> [27] highr_0.10 DelayedArray_0.30.1 #> [29] fpc_2.2-12 BiocParallel_1.38.0 #> [31] broom_1.0.5 parallel_4.4.0 #> [33] prabclus_2.3-3 cluster_2.1.6 #> [35] R6_2.5.1 stringi_1.8.4 #> [37] bslib_0.7.0 limma_3.60.0 #> [39] car_3.1-2 GenomicRanges_1.56.0 #> [41] jquerylib_0.1.4 diptest_0.77-1 #> [43] Rcpp_1.0.12 SummarizedExperiment_1.34.0 #> [45] knitr_1.46 IRanges_2.38.0 #> [47] splines_4.4.0 timechange_0.3.0 #> [49] Matrix_1.7-0 nnet_7.3-19 #> [51] tidyselect_1.2.1 abind_1.4-5 #> [53] yaml_2.3.8 codetools_0.2-20 #> [55] lattice_0.22-6 withr_3.0.0 #> [57] Biobase_2.64.0 evaluate_0.23 #> [59] desc_1.4.3 mclust_6.1.1 #> [61] kernlab_0.9-32 pillar_1.9.0 #> [63] ggpubr_0.6.0 MatrixGenerics_1.16.0 #> [65] carData_3.0-5 stats4_4.4.0 #> [67] plotly_4.10.4 generics_0.1.3 #> [69] hms_1.1.3 S4Vectors_0.42.0 #> [71] munsell_0.5.1 scales_1.3.0 #> [73] class_7.3-22 glue_1.7.0 #> [75] lazyeval_0.2.2 tools_4.4.0 #> [77] robustbase_0.99-2 data.table_1.15.4 #> [79] locfit_1.5-9.9 ggsignif_0.6.4 #> [81] fs_1.6.4 grid_4.4.0 #> [83] edgeR_4.2.0 colorspace_2.1-0 #> [85] GenomeInfoDbData_1.2.12 cli_3.6.2 #> [87] textshaping_0.3.7 fansi_1.0.6 #> [89] viridisLite_0.4.2 S4Arrays_1.4.0 #> [91] paintingr_0.1.0 gtable_0.3.5 #> [93] DEoptimR_1.1-3 rstatix_0.7.2 #> [95] DESeq2_1.44.0 sass_0.4.9 #> [97] digest_0.6.35 BiocGenerics_0.50.0 #> [99] SparseArray_1.4.3 ggrepel_0.9.5 #> [101] farver_2.1.1 htmlwidgets_1.6.4 #> [103] memoise_2.0.1 htmltools_0.5.8.1 #> [105] pkgdown_2.0.9 lifecycle_1.0.4 #> [107] httr_1.4.7 statmod_1.5.0 #> [109] MASS_7.3-60.2"},{"path":"https://thereallda.github.io/enONE/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Dean Li. Author, maintainer.","code":""},{"path":"https://thereallda.github.io/enONE/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Li D (2024). enONE: Epitranscriptional NAD-capped RNA analysis spike-based Omic-level Normalization Evaluation. R package version 1.0.0, https://thereallda.github.io/enONE/.","code":"@Manual{, title = {enONE: Epitranscriptional NAD-capped RNA analysis by spike-in based Omic-level Normalization and Evaluation}, author = {Dean Li}, year = {2024}, note = {R package version 1.0.0}, url = {https://thereallda.github.io/enONE/}, }"},{"path":"https://thereallda.github.io/enONE/index.html","id":"enone-","dir":"","previous_headings":"","what":"Epitranscriptional NAD-capped RNA analysis by spike-in based Omic-level Normalization and Evaluation","title":"Epitranscriptional NAD-capped RNA analysis by spike-in based Omic-level Normalization and Evaluation","text":"enONE R package performing spike-based normalization NAD-capped RNA sequencing data, well comparing ranking performance different normalization strategies. enONE also provides toolkit handle NAD-RNA sequencing data.","code":""},{"path":"https://thereallda.github.io/enONE/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Epitranscriptional NAD-capped RNA analysis by spike-in based Omic-level Normalization and Evaluation","text":"can install development version enONE GitHub : Tutorial can found https://thereallda.github.io/enONE/ use enONE analysis, please cite publication: Epitranscriptome analysis NAD-capped RNA spike--based normalization prediction chronological age (https://doi.org/10.1016/j.isci.2023.108558)","code":"# install.packages(\"devtools\") devtools::install_github(\"thereallda/enONE\")"},{"path":"https://thereallda.github.io/enONE/reference/ApplyNormalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies normalization on sequencing data — ApplyNormalization","title":"Applies normalization on sequencing data — ApplyNormalization","text":"Applies normalization sequencing data","code":""},{"path":"https://thereallda.github.io/enONE/reference/ApplyNormalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies normalization on sequencing data — ApplyNormalization","text":"","code":"ApplyNormalization( data, scaling.method = c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"), ruv.norm = TRUE, ruv.k = 1, ruv.drop = 0, control.idx = NULL, sc.idx = NULL, enrich.idx = NULL, spike.in.prefix = NULL, synthetic.id = NULL )"},{"path":"https://thereallda.github.io/enONE/reference/ApplyNormalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies normalization on sequencing data — ApplyNormalization","text":"data un-normalized count data matrix shape n x p, n number samples p number features. scaling.method Vector normalization methods applied data. Available methods : c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"). Select one multiple methods. default normalization methods applied. ruv.norm Whether perform RUV normalization. ruv.k number factors unwanted variation estimated data. ruv.drop number singular values drop estimation unwanted variation, default drop first singular value represent difference enrichment input. control.idx Vector negative control genes RUV normalization. sc.idx numeric matrix specifying replicate samples compute count differences used estimate factors unwanted variation. enrich.idx Matrix two rows indicating column index enrichment input samples raw/normalized count data matrix. first row column index input second row column index enrichment samples. spike..prefix character specify prefix spike-id. synthetic.id Character vector string specifying name synthetic RNAs.","code":""},{"path":"https://thereallda.github.io/enONE/reference/ApplyNormalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies normalization on sequencing data — ApplyNormalization","text":"List objects containing normalized data associated normalization factors.","code":""},{"path":"https://thereallda.github.io/enONE/reference/AssessNormalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Assessments of normalization performance — AssessNormalization","title":"Assessments of normalization performance — AssessNormalization","text":"Assessments normalization performance","code":""},{"path":"https://thereallda.github.io/enONE/reference/AssessNormalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assessments of normalization performance — AssessNormalization","text":"","code":"AssessNormalization( data.ls, bio.group = NULL, enrich.group = NULL, batch.group = NULL, eval.pam.k = 2:6, eval.pc.n = 3, log = TRUE, pos.eval.set = NULL, neg.eval.set = NULL )"},{"path":"https://thereallda.github.io/enONE/reference/AssessNormalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assessments of normalization performance — AssessNormalization","text":"data.ls List containing normalized counts adjust factors adjusting unwanted variation. Output ApplyNormalization. bio.group Vector index indicating column index samples biological groups raw/normalized count data matrix. enrich.group Vector index indicating column index enrichment input samples raw/normalized count data matrix. batch.group Vector index indicating column index batch groups raw/normalized count data matrix. eval.pam.k Integer vector integers indicates number clusters PAM clustering performance evaluation, default: 2:6. eval.pc.n Integer indicates evaluation metrics calculated first nth PCs, default: 3. log Whether perform log2-transformation 1 offset data matrix, default: TRUE. pos.eval.set Vector genes id. neg.eval.set Vector genes id.","code":""},{"path":"https://thereallda.github.io/enONE/reference/AssessNormalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assessments of normalization performance — AssessNormalization","text":"List containing metrics matrix ranking matrix, sorted score methods top bottom.","code":""},{"path":"https://thereallda.github.io/enONE/reference/BetweenStatPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Box-violin plot comparing values between groups — BetweenStatPlot","title":"Box-violin plot comparing values between groups — BetweenStatPlot","text":"Box-violin plot comparing values groups","code":""},{"path":"https://thereallda.github.io/enONE/reference/BetweenStatPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Box-violin plot comparing values between groups — BetweenStatPlot","text":"","code":"BetweenStatPlot( data, x, y, color, palette = NULL, test = c(\"wilcox.test\", \"t.test\", \"none\"), add.p = c(\"p\", \"p.adj\"), comparisons = NULL, step.increase = 0.3, title = NULL )"},{"path":"https://thereallda.github.io/enONE/reference/BetweenStatPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Box-violin plot comparing values between groups — BetweenStatPlot","text":"data data frame (tibble). x grouping variable data. y value variable data. color color variable data. palette color palette different groups. test Perform wilcoxon rank sum test t-test test, must one c(\"wilcox.test\", \"t.test\", \"none\"). add.p Label p-value adjusted p-value, must one c(\"p\", \"p.adj\"). comparisons list length-2 vectors specifying groups interest compared. example compare groups \"\" vs \"B\" \"B\" vs \"C\", argument follow: comparisons = list(c(\"\", \"B\"), c(\"B\", \"C\")) step.increase Numeric vector increase fraction total height every additional comparison minimize overlap. title Plot title, default NULL.","code":""},{"path":"https://thereallda.github.io/enONE/reference/BetweenStatPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Box-violin plot comparing values between groups — BetweenStatPlot","text":"ggplot2 object","code":""},{"path":"https://thereallda.github.io/enONE/reference/Counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor for the ","title":"Accessor for the ","text":"Accessor \"counts\" slot Enone object.","code":""},{"path":"https://thereallda.github.io/enONE/reference/Counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor for the ","text":"","code":"Counts(object, slot = c(\"sample\", \"spike_in\"), method) Counts(object, slot = c(\"sample\", \"spike_in\"), method) <- value # S4 method for Enone,character,character Counts(object, slot = c(\"sample\", \"spike_in\"), method) # S4 method for Enone,character,character,matrix Counts(object, slot = c(\"sample\", \"spike_in\"), method) <- value"},{"path":"https://thereallda.github.io/enONE/reference/Counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor for the ","text":"object Enone. slot slot get, one sample spike_in. method counts matrix get, must one raw normalized counts matrix presented selected slot. value Raw normalized counts matrix.","code":""},{"path":"https://thereallda.github.io/enONE/reference/Counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor for the ","text":"matrix.","code":""},{"path":"https://thereallda.github.io/enONE/reference/CreateGroupMatrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a matrix for RUVSeq — CreateGroupMatrix","title":"Create a matrix for RUVSeq — CreateGroupMatrix","text":"Create matrix RUVSeq","code":""},{"path":"https://thereallda.github.io/enONE/reference/CreateGroupMatrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a matrix for RUVSeq — CreateGroupMatrix","text":"","code":"CreateGroupMatrix(group.vec)"},{"path":"https://thereallda.github.io/enONE/reference/CreateGroupMatrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a matrix for RUVSeq — CreateGroupMatrix","text":"group.vec vector indicating membership group.","code":""},{"path":"https://thereallda.github.io/enONE/reference/CreateGroupMatrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a matrix for RUVSeq — CreateGroupMatrix","text":"matrix.","code":""},{"path":"https://thereallda.github.io/enONE/reference/CreateGroupMatrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a matrix for RUVSeq — CreateGroupMatrix","text":"","code":"CreateGroupMatrix(c('a','b','b','c','c','c','a','d','d')) #> [,1] [,2] [,3] #> [1,] 1 7 -1 #> [2,] 2 3 -1 #> [3,] 4 5 6 #> [4,] 8 9 -1"},{"path":"https://thereallda.github.io/enONE/reference/DotPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot-plot with mean_sd bar — DotPlot","title":"Dot-plot with mean_sd bar — DotPlot","text":"Dot-plot mean_sd bar","code":""},{"path":"https://thereallda.github.io/enONE/reference/DotPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot-plot with mean_sd bar — DotPlot","text":"","code":"DotPlot(data, x, y, fill = NULL, palette = NULL)"},{"path":"https://thereallda.github.io/enONE/reference/DotPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot-plot with mean_sd bar — DotPlot","text":"data data.frame (tibble). x grouping variable data. y value variable data. fill fill variable data. palette fill palette different groups.","code":""},{"path":"https://thereallda.github.io/enONE/reference/DotPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dot-plot with mean_sd bar — DotPlot","text":"ggplot2 object","code":""},{"path":"https://thereallda.github.io/enONE/reference/FilterLowExprGene.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter Low Expressed Genes — FilterLowExprGene","title":"Filter Low Expressed Genes — FilterLowExprGene","text":"Filter Low Expressed Genes","code":""},{"path":"https://thereallda.github.io/enONE/reference/FilterLowExprGene.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter Low Expressed Genes — FilterLowExprGene","text":"","code":"FilterLowExprGene(x, group = NULL, min.count = 10)"},{"path":"https://thereallda.github.io/enONE/reference/FilterLowExprGene.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter Low Expressed Genes — FilterLowExprGene","text":"x Input data, can counts matrix Enone object. group Vector factor giving group membership oneway layout, appropriate, default: NULL. min.count Minimum count required least samples, default: 10.","code":""},{"path":"https://thereallda.github.io/enONE/reference/FilterLowExprGene.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter Low Expressed Genes — FilterLowExprGene","text":"Filtered count matrix updated Enone object.","code":""},{"path":"https://thereallda.github.io/enONE/reference/FindEnrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"Find enriched genes between enrich and input samples — FindEnrichment","title":"Find enriched genes between enrich and input samples — FindEnrichment","text":"Find enriched genes enrich input samples","code":""},{"path":"https://thereallda.github.io/enONE/reference/FindEnrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find enriched genes between enrich and input samples — FindEnrichment","text":"","code":"FindEnrichment( object, slot = c(\"sample\", \"spike_in\"), norm.method, logfc.cutoff = 1, p.cutoff = 0.05, only.pos = TRUE, ... )"},{"path":"https://thereallda.github.io/enONE/reference/FindEnrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find enriched genes between enrich and input samples — FindEnrichment","text":"object object. slot slot, one sample spike_in. norm.method normalization method use differential analysis, must one methods presented selected slot. logfc.cutoff Filter genes least X-fold difference (log2-scale) two groups samples, default: 1. p.cutoff Filter genes p.cutoff adjusted p-value, default: 0.05. .pos Whether significant differential genes consider genes positive fold-change. ... Additional parameters can passed edgeRDE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/FindEnrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find enriched genes between enrich and input samples — FindEnrichment","text":"updated Enone","code":""},{"path":"https://thereallda.github.io/enONE/reference/GeneSelection.html","id":null,"dir":"Reference","previous_headings":"","what":"Gene set selection — GeneSelection","title":"Gene set selection — GeneSelection","text":"Select negative control genes RUV normalization, positive negative evaluation genes assessment.","code":""},{"path":"https://thereallda.github.io/enONE/reference/GeneSelection.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gene set selection — GeneSelection","text":"","code":"GeneSelection(object, n.neg.control = 1000, n.pos.eval = 500, n.neg.eval = 500)"},{"path":"https://thereallda.github.io/enONE/reference/GeneSelection.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gene set selection — GeneSelection","text":"object Enone object n.neg.control Number negative control genes RUV normalization, default: 1000. n.pos.eval Number positive evaluation genes wanted variation assessment, default: 500. n.neg.eval Number negative evaluation genes unwanted variation assessment, default: 500.","code":""},{"path":"https://thereallda.github.io/enONE/reference/GeneSelection.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gene set selection — GeneSelection","text":"list genes","code":""},{"path":"https://thereallda.github.io/enONE/reference/OutlierTest.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Test — OutlierTest","title":"Outlier Test — OutlierTest","text":"Rosner’s outlier test principal component 1 assess remove potential outliers.","code":""},{"path":"https://thereallda.github.io/enONE/reference/OutlierTest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Test — OutlierTest","text":"","code":"OutlierTest(x, return = FALSE, remove = FALSE)"},{"path":"https://thereallda.github.io/enONE/reference/OutlierTest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Test — OutlierTest","text":"x Enone object return Whether return object simply perform test, default FALSE remove Whether remove outliers. TRUE, must paired \"return=TRUE\", default: FALSE","code":""},{"path":"https://thereallda.github.io/enONE/reference/OutlierTest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Test — OutlierTest","text":"updated Enone object","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCA_Biplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Biplot of individuals and variables — PCA_Biplot","title":"Biplot of individuals and variables — PCA_Biplot","text":"Biplot individuals variables","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCA_Biplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Biplot of individuals and variables — PCA_Biplot","text":"","code":"PCA_Biplot(object, score, pt.label = TRUE, interactive = FALSE)"},{"path":"https://thereallda.github.io/enONE/reference/PCA_Biplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Biplot of individuals and variables — PCA_Biplot","text":"object PCA object returned prcomp. score Vector performance scores Enone evaluation score. pt.label Whether plot point labels, default: TRUE. interactive Whether demonstrate plot interactively, default: FALSE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCA_Biplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Biplot of individuals and variables — PCA_Biplot","text":"plot","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCAplot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA plot from counts matrix — PCAplot","title":"PCA plot from counts matrix — PCAplot","text":"PCA plot counts matrix","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCAplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA plot from counts matrix — PCAplot","text":"","code":"PCAplot( object, use.pc = c(1, 2), color = NULL, label = NULL, shape = NULL, title = NULL, vst.norm = FALSE, palette = NULL, repel = TRUE )"},{"path":"https://thereallda.github.io/enONE/reference/PCAplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA plot from counts matrix — PCAplot","text":"object count matrix. use.pc two PCs used, default PC1 x-axis PC2 y-axis. color Vector indicates color mapping samples, default NULL. label Vector sample names labels, default NULL. shape Vector indicates shape mapping samples, default NULL. title Plot title, default NULL. vst.norm Whether perform vst transformation, default FALSE. palette color palette different groups. repel Whether use ggrepel avoid overlapping text labels , default TRUE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCAplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA plot from counts matrix — PCAplot","text":"ggplot2 object","code":""},{"path":"https://thereallda.github.io/enONE/reference/PCAplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA plot from counts matrix — PCAplot","text":"Perform PCA based matrix using prcomp, visualized scatter plot.","code":""},{"path":"https://thereallda.github.io/enONE/reference/PS.Est.Depth.html","id":null,"dir":"Reference","previous_headings":"","what":"Esimate sequencing depth — PS.Est.Depth","title":"Esimate sequencing depth — PS.Est.Depth","text":"Functions PossionSeq (https://doi.org/10.1093/biostatistics/kxr031)","code":""},{"path":"https://thereallda.github.io/enONE/reference/PS.Est.Depth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Esimate sequencing depth — PS.Est.Depth","text":"","code":"PS.Est.Depth(n, iter = 5, ct.sum = 5, ct.mean = 0.5)"},{"path":"https://thereallda.github.io/enONE/reference/PS.Est.Depth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Esimate sequencing depth — PS.Est.Depth","text":"n data matrix. rows counts gene, columns counts experiment. iter Number iterations used. Default value: 5. default value usually good choice. ct.sum total number reads gene across experiments <= ct.sum, gene considered estimating sequencing depth. Default value: 5. ct.mean mean number reads gene across experiments <= ct.mean, gene considered estimating sequencing depth. Default value: 0.5.","code":""},{"path":"https://thereallda.github.io/enONE/reference/PS.Est.Depth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Esimate sequencing depth — PS.Est.Depth","text":"estimated sequencing depth. vector. product 1.","code":""},{"path":"https://thereallda.github.io/enONE/reference/PS.Est.Depth.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Esimate sequencing depth — PS.Est.Depth","text":"","code":"mat <- matrix(rnbinom(300, mu=100, size=1), ncol=5) PS.Est.Depth(mat) #> [1] 1.3929053 1.0767802 0.9736520 0.7609921 0.8998445"},{"path":"https://thereallda.github.io/enONE/reference/UseNormalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply specific normalization method — UseNormalization","title":"Apply specific normalization method — UseNormalization","text":"Apply specific normalization method","code":""},{"path":"https://thereallda.github.io/enONE/reference/UseNormalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply specific normalization method — UseNormalization","text":"","code":"UseNormalization(object, slot = c(\"sample\", \"spike_in\"), method)"},{"path":"https://thereallda.github.io/enONE/reference/UseNormalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply specific normalization method — UseNormalization","text":"object Enone object. slot slot, one sample spike_in. method normalization methods perform.","code":""},{"path":"https://thereallda.github.io/enONE/reference/UseNormalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply specific normalization method — UseNormalization","text":"updated Enone","code":""},{"path":"https://thereallda.github.io/enONE/reference/countReplicate.html","id":null,"dir":"Reference","previous_headings":"","what":"Count numbers of each members — countReplicate","title":"Count numbers of each members — countReplicate","text":"Count numbers members","code":""},{"path":"https://thereallda.github.io/enONE/reference/countReplicate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count numbers of each members — countReplicate","text":"","code":"countReplicate(group.vec)"},{"path":"https://thereallda.github.io/enONE/reference/countReplicate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count numbers of each members — countReplicate","text":"group.vec Vector members.","code":""},{"path":"https://thereallda.github.io/enONE/reference/countReplicate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count numbers of each members — countReplicate","text":"vector","code":""},{"path":"https://thereallda.github.io/enONE/reference/countReplicate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count numbers of each members — countReplicate","text":"","code":"countReplicate(c('a','b','b','c','c','c','a','d','d')) #> [1] 1 1 2 1 2 3 2 1 2"},{"path":"https://thereallda.github.io/enONE/reference/createEnone.html","id":null,"dir":"Reference","previous_headings":"","what":"Enone object and constructor — Enone-class","title":"Enone object and constructor — Enone-class","text":"Enone object extends SummarizedExperiment class. createEnone easy constructor Enone object","code":""},{"path":"https://thereallda.github.io/enONE/reference/createEnone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enone object and constructor — Enone-class","text":"","code":"createEnone( data, col.data, spike.in.prefix = NULL, input.id = \"Input\", enrich.id = \"Enrich\", synthetic.id = NULL )"},{"path":"https://thereallda.github.io/enONE/reference/createEnone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enone object and constructor — Enone-class","text":"data un-normalized count data matrix shape n x p, n number samples p number features. col.data data.frame least two columns (indicate condition enrich groups). Rows col.data correspond columns data. spike..prefix character specify prefix spike-id, e.g., \"^FB\" stands fly spike-id, default: NULL. input.id Input library id, must consistent enrich column col.data, e.g., \"Input\". enrich.id Enrich library id, must consistent enrich column col.data, e.g., \"Enrich\". synthetic.id Vector synthetic RNA id, e.g. c(\"Syn1\",\"Syn2\"), default: NULL.","code":""},{"path":"https://thereallda.github.io/enONE/reference/createEnone.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enone object and constructor — Enone-class","text":"Enone object","code":""},{"path":"https://thereallda.github.io/enONE/reference/createEnone.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Enone object and constructor — Enone-class","text":"Description slot: assay SummarizedExperiment::Assays object, contains counts. counts list holding raw/normalized counts sample spike_in. enone_factor list holding normalization factors sample spike_in. enone_metrics data frame normalization methods row metrics columns. enone_score data frame normalization methods row scores columns. enrichment list holding differential analysis results. enrichment_filtered lists holding filtered differential analysis results. parameter list parameters.","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-keepConsRows.html","id":null,"dir":"Reference","previous_headings":"","what":"Keep Consistent Rows in Object — .keepConsRows","title":"Keep Consistent Rows in Object — .keepConsRows","text":"Make sure rows counts slot assay consistent.","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-keepConsRows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keep Consistent Rows in Object — .keepConsRows","text":"","code":".keepConsRows(object)"},{"path":"https://thereallda.github.io/enONE/reference/dot-keepConsRows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keep Consistent Rows in Object — .keepConsRows","text":"object Enone object","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-keepConsRows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keep Consistent Rows in Object — .keepConsRows","text":"Updated Enone","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-mean_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"Statistics summary (mean and +/- sd) — .mean_sd","title":"Statistics summary (mean and +/- sd) — .mean_sd","text":"Statistics summary (mean +/- sd)","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-mean_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Statistics summary (mean and +/- sd) — .mean_sd","text":"","code":".mean_sd(x)"},{"path":"https://thereallda.github.io/enONE/reference/dot-mean_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Statistics summary (mean and +/- sd) — .mean_sd","text":"x Value","code":""},{"path":"https://thereallda.github.io/enONE/reference/dot-mean_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Statistics summary (mean and +/- sd) — .mean_sd","text":"Vector mean mean +/- sd","code":""},{"path":"https://thereallda.github.io/enONE/reference/edgeRDE.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper of edgeR procedure — edgeRDE","title":"Wrapper of edgeR procedure — edgeRDE","text":"Wrapper edgeR procedure","code":""},{"path":"https://thereallda.github.io/enONE/reference/edgeRDE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper of edgeR procedure — edgeRDE","text":"","code":"edgeRDE( counts, group, norm.factors = NULL, adjust.factors = NULL, design.formula = NULL, contrast.df = NULL, coef = NULL, logfc.cutoff = 1, p.cutoff = 0.05, only.pos = TRUE )"},{"path":"https://thereallda.github.io/enONE/reference/edgeRDE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper of edgeR procedure — edgeRDE","text":"counts un-normalized counts data matrix. group Vector length p mapping columns counts corresponding samples group. norm.factors Vector normalization factors p length. adjust.factors Matrix column indicates adjusting factors estimated RUV. design.formula Formula contrast.df Data frame contrast, extracting results first column vs. second column. coef Integer character vector indicating coefficients linear model tested equal zero. Values must columns column names design. logfc.cutoff Filter genes least X-fold difference (log2-scale) two groups samples, default: 1. p.cutoff Filter genes Y adjusted p-value, default: 0.05. .pos return positive genes filtered results res.sig.ls, default: TRUE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/edgeRDE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper of edgeR procedure — edgeRDE","text":"List containing differential analysis object, result table filtered result table.","code":""},{"path":"https://thereallda.github.io/enONE/reference/enONE.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalization and assessment in one function — enONE","title":"Normalization and assessment in one function — enONE","text":"Normalization assessment one function","code":""},{"path":"https://thereallda.github.io/enONE/reference/enONE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalization and assessment in one function — enONE","text":"","code":"enONE( object, auto = TRUE, return.norm = FALSE, n.neg.control = 1000, n.pos.eval = 500, n.neg.eval = 500, neg.control = NULL, pos.eval = NULL, neg.eval = NULL, scaling.method = c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"), ruv.norm = TRUE, ruv.k = 1, ruv.drop = 0, eval.pam.k = 2:6, eval.pc.n = 3 )"},{"path":"https://thereallda.github.io/enONE/reference/enONE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalization and assessment in one function — enONE","text":"object Enone object. auto Whether automatically select negative control, positive evaluation negative evaluation genes, default: TRUE. return.norm Whether return normalized counts object. default, return normalized counts reducing memory costs. n.neg.control Number negative control genes RUV normalization, default: 1000. n.pos.eval Number positive evaluation genes wanted variation assessment, default: 500. n.neg.eval Number negative evaluation genes unwanted variation assessment, default: 500. neg.control Vector negative control genes' id RUV normalization, default: NULL. pos.eval Vector positive evaluation genes' id wanted variation assessment, default: NULL. neg.eval Vector negative evaluation genes' id unwanted variation assessment, default: NULL. scaling.method Vector scaling methods applied data. Available methods : c(\"TC\", \"UQ\", \"TMM\", \"DESeq\", \"PossionSeq\"). Select one multiple methods. default scaling methods applied. ruv.norm Whether perform RUV normalization. ruv.k number factors unwanted variation estimated data, default: 1. ruv.drop number singular values drop estimation unwanted variation, default: 0. eval.pam.k Integer vector integers indicates number clusters PAM clustering performance evaluation, default: 2:6. eval.pc.n Integer indicates evaluation metrics calculated first nth PCs, default: 3.","code":""},{"path":"https://thereallda.github.io/enONE/reference/enONE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalization and assessment in one function — enONE","text":"Enone object.","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVg.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove unwanted variation using control genes — enRUVg","title":"Remove unwanted variation using control genes — enRUVg","text":"Adapted RUVSeq (https://doi.org/10.1038%2Fnbt.2931)","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove unwanted variation using control genes — enRUVg","text":"","code":"enRUVg(object, log = TRUE, k = 1, tolerance = 1e-08, control.idx, drop = 1)"},{"path":"https://thereallda.github.io/enONE/reference/enRUVg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove unwanted variation using control genes — enRUVg","text":"object counts matrix. log Whether perform log2-transformation 1 offset data matrix, default: TRUE. k number factors unwanted variation estimated data. tolerance Tolerance selection number positive singular values, .e., singular value must larger tolerance considered positive. control.idx ID control genes. drop number singular values drop estimation unwanted variation, default drop first singular value represent difference enrichment input","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove unwanted variation using control genes — enRUVg","text":"list contain matrix unwanted factors (W) corrected counts matrix (normalizedCounts).","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVs.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove unwanted variation using control genes within replicates — enRUVs","title":"Remove unwanted variation using control genes within replicates — enRUVs","text":"Adapted RUVSeq (https://doi.org/10.1038%2Fnbt.2931)","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove unwanted variation using control genes within replicates — enRUVs","text":"","code":"enRUVs( object, log = TRUE, k = 2, tolerance = 1e-08, control.idx, sc.idx, drop = 1 )"},{"path":"https://thereallda.github.io/enONE/reference/enRUVs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove unwanted variation using control genes within replicates — enRUVs","text":"object counts matrix. log Whether perform log2-transformation 1 offset data matrix, default: TRUE. k number factors unwanted variation estimated data. tolerance Tolerance selection number positive singular values, .e., singular value must larger tolerance considered positive. control.idx ID control genes. sc.idx numeric matrix specifying replicate samples compute count differences used estimate factors unwanted variation (see details). drop number singular values drop estimation unwanted variation, default drop first singular value represent difference enrichment input","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove unwanted variation using control genes within replicates — enRUVs","text":"list contain matrix unwanted factors (W) corrected counts matrix (normalizedCounts).","code":""},{"path":"https://thereallda.github.io/enONE/reference/enRUVs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Remove unwanted variation using control genes within replicates — enRUVs","text":"row sc.idx correspond set replicate samples. number columns size largest set replicates; rows smaller sets padded -1 values. example, sets replicate samples (1,2,3),(4,5),(6,7,8), scIdx 1 2 3 4 5 -1 6 7 8","code":""},{"path":"https://thereallda.github.io/enONE/reference/getEnrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of enrichment results — getEnrichment","title":"Accessor of enrichment results — getEnrichment","text":"Get filtered enrichment","code":""},{"path":"https://thereallda.github.io/enONE/reference/getEnrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of enrichment results — getEnrichment","text":"","code":"getEnrichment(object, slot = c(\"sample\", \"spike_in\"), filter = FALSE) # S4 method for Enone,character getEnrichment(object, slot = c(\"sample\", \"spike_in\"), filter = FALSE)"},{"path":"https://thereallda.github.io/enONE/reference/getEnrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of enrichment results — getEnrichment","text":"object Enone. slot slot get, one sample spike_in. filter Whether get filtered enrichment, default FALSE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getEnrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of enrichment results — getEnrichment","text":"list enrichment table","code":""},{"path":"https://thereallda.github.io/enONE/reference/getFactor.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of Enone normalization factors — getFactor","title":"Accessor of Enone normalization factors — getFactor","text":"Accessor Enone normalization factors","code":""},{"path":"https://thereallda.github.io/enONE/reference/getFactor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of Enone normalization factors — getFactor","text":"","code":"getFactor(object, slot = c(\"sample\", \"spike_in\"), method) # S4 method for Enone,character,character getFactor(object, slot = c(\"sample\", \"spike_in\"), method)"},{"path":"https://thereallda.github.io/enONE/reference/getFactor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of Enone normalization factors — getFactor","text":"object Enone. slot slot get, one sample spike_in. method normalization methods get, must one methods presented selected slot.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getFactor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of Enone normalization factors — getFactor","text":"vector list factors.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getGeneSet.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of gene set — getGeneSet","title":"Accessor of gene set — getGeneSet","text":"Get gene set.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getGeneSet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of gene set — getGeneSet","text":"","code":"getGeneSet(object, name = c(\"NegControl\", \"NegEvaluation\", \"PosEvaluation\")) # S4 method for Enone,character getGeneSet(object, name = c(\"NegControl\", \"NegEvaluation\", \"PosEvaluation\"))"},{"path":"https://thereallda.github.io/enONE/reference/getGeneSet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of gene set — getGeneSet","text":"object Enone. name Name gene set, one NegControl, NegEvaluation, PosEvaluation","code":""},{"path":"https://thereallda.github.io/enONE/reference/getGeneSet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of gene set — getGeneSet","text":"Vector gene ID","code":""},{"path":"https://thereallda.github.io/enONE/reference/getMetrics.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of Enone metrics — getMetrics","title":"Accessor of Enone metrics — getMetrics","text":"Accessor Enone metrics","code":""},{"path":"https://thereallda.github.io/enONE/reference/getMetrics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of Enone metrics — getMetrics","text":"","code":"getMetrics(object) # S4 method for Enone getMetrics(object)"},{"path":"https://thereallda.github.io/enONE/reference/getMetrics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of Enone metrics — getMetrics","text":"object Enone.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getMetrics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of Enone metrics — getMetrics","text":"data.frame","code":""},{"path":"https://thereallda.github.io/enONE/reference/getParameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of Enone parameter — listParameter","title":"Accessor of Enone parameter — listParameter","text":"List parameters. Get specific parameter.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getParameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of Enone parameter — listParameter","text":"","code":"listParameter(object) getParameter(object, name) # S4 method for Enone listParameter(object) # S4 method for Enone,character getParameter(object, name)"},{"path":"https://thereallda.github.io/enONE/reference/getParameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of Enone parameter — listParameter","text":"object Enone. name Name parameter.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getParameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of Enone parameter — listParameter","text":"Vector parameter names Vector parameter","code":""},{"path":"https://thereallda.github.io/enONE/reference/getScore.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of Enone score — getScore","title":"Accessor of Enone score — getScore","text":"Accessor Enone score","code":""},{"path":"https://thereallda.github.io/enONE/reference/getScore.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of Enone score — getScore","text":"","code":"getScore(object) # S4 method for Enone getScore(object)"},{"path":"https://thereallda.github.io/enONE/reference/getScore.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of Enone score — getScore","text":"object Enone.","code":""},{"path":"https://thereallda.github.io/enONE/reference/getScore.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of Enone score — getScore","text":"data.frame","code":""},{"path":"https://thereallda.github.io/enONE/reference/listNormalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessor of normalization methods — listNormalization","title":"Accessor of normalization methods — listNormalization","text":"List normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/listNormalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessor of normalization methods — listNormalization","text":"","code":"listNormalization(object) # S4 method for Enone listNormalization(object)"},{"path":"https://thereallda.github.io/enONE/reference/listNormalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessor of normalization methods — listNormalization","text":"object Enone.","code":""},{"path":"https://thereallda.github.io/enONE/reference/listNormalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessor of normalization methods — listNormalization","text":"Vector normalization methods","code":""},{"path":"https://thereallda.github.io/enONE/reference/normDESeq.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform DESeq normalization — normDESeq","title":"Perform DESeq normalization — normDESeq","text":"Perform DESeq normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/normDESeq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform DESeq normalization — normDESeq","text":"","code":"normDESeq(data)"},{"path":"https://thereallda.github.io/enONE/reference/normDESeq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform DESeq normalization — normDESeq","text":"data un-normalized count data matrix shape n x p, n number samples p number features.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normDESeq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform DESeq normalization — normDESeq","text":"List containing normalized counts normalized factors library size.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normPossionSeq.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform PossionSeq normalization — normPossionSeq","title":"Perform PossionSeq normalization — normPossionSeq","text":"Perform PossionSeq normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/normPossionSeq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform PossionSeq normalization — normPossionSeq","text":"","code":"normPossionSeq(data, ...)"},{"path":"https://thereallda.github.io/enONE/reference/normPossionSeq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform PossionSeq normalization — normPossionSeq","text":"data un-normalized count data matrix shape n x p, n number samples p number features. ... 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data matrix shape n x p, n number samples p number features.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normTMM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform TMM normalization — normTMM","text":"List containing normalized counts normalized factors library size.","code":""},{"path":"https://thereallda.github.io/enONE/reference/normUQ.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform upper-quartile normalization — normUQ","title":"Perform upper-quartile normalization — normUQ","text":"Perform upper-quartile normalization","code":""},{"path":"https://thereallda.github.io/enONE/reference/normUQ.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform upper-quartile normalization — 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reduceRes","text":"data.frame","code":""},{"path":"https://thereallda.github.io/enONE/reference/synEnrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"Enrichment level of synthetic RNA — synEnrichment","title":"Enrichment level of synthetic RNA — synEnrichment","text":"Enrichment level synthetic RNA","code":""},{"path":"https://thereallda.github.io/enONE/reference/synEnrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enrichment level of synthetic RNA — synEnrichment","text":"","code":"synEnrichment(object, method = \"TC\", log = TRUE)"},{"path":"https://thereallda.github.io/enONE/reference/synEnrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enrichment level of synthetic RNA — synEnrichment","text":"object Enone object. method normalization methods perform. log Whether return log2 values, default: TRUE.","code":""},{"path":"https://thereallda.github.io/enONE/reference/synEnrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enrichment level of synthetic RNA — synEnrichment","text":"enrichment level synthetic RNA","code":""}]