Adding to the testing menu

R/clonalScatter.R

@@ -34,7 +34,7 @@
 #' 
 #' @export
 #' @concept Visualizing_Clones
-#' @return ggplot of the relative clonotype numbers between two sequencing runs
+#' @return ggplot of the relative clone numbers between two sequencing runs or groups
 
 clonalScatter <- function(input.data, 
                           cloneCall ="strict", 
@@ -109,8 +109,12 @@ clonalScatter <- function(input.data,
     y <- mat[,y.axis] }
   if (dot.size != "total") {
     size <- mat[,dot.size]
-  } else { size <- mat[,"sum"] }
-  if (exportTable == TRUE) { return(mat) }
+  } else { 
+    mat[,"size"] <- mat[,"sum"] 
+  }
+  if (exportTable == TRUE) { 
+    return(mat) 
+  }
 
   plot <- ggplot(mat, aes(x=x, y = y, fill = class)) + 
                 theme_classic() + 
@@ -129,7 +133,7 @@ clonalScatter <- function(input.data,
             xlim(0, max(x,y)) 
   }
   plot <- plot + 
-          geom_jitter(aes(size = size,), shape = 21, color = "black", stroke = 0.25)
+          geom_point(aes(size = size), shape = 21, color = "black", stroke = 0.25)
 
   return(plot)  
 }

R/loadContigs.R

@@ -189,9 +189,15 @@ loadContigs <- function(input,
 
 .parseOmniscope <- function(df) {
   for (i in seq_along(df)) {
-    df[[i]] <- df[[i]][,c("contig_id", "locus", "umi_count", "v_call", "d_call", "j_call", "c_call", "cdr3", "cdr3_aa")]
-    colnames(df[[i]]) <- c("barcode", "chain", "reads", "v_gene", "d_gene", "j_gene", "c_gene", "cdr3_nt", "cdr3")
+    if("c_call" %in% colnames(df[[i]])) {
+      df[[i]] <- df[[i]][,c("contig_id", "locus", "umi_count", "v_call", "d_call", "j_call", "c_call", "cdr3", "cdr3_aa")]
+      colnames(df[[i]]) <- c("barcode", "chain", "reads", "v_gene", "d_gene", "j_gene", "c_gene", "cdr3_nt", "cdr3")
+    } else { #TCR contigs do not include C gene
+      df[[i]] <- df[[i]][,c("contig_id", "locus", "umi_count", "v_call", "d_call", "j_call", "cdr3", "cdr3_aa")]
+      colnames(df[[i]]) <- c("barcode", "chain", "reads", "v_gene", "d_gene", "j_gene", "cdr3_nt", "cdr3")
+      df[[i]][,"c_gene"] <- NA
     df[[i]] <- df[[i]][with(df[[i]], order(reads, chain)),]
+    }
   }
   return(df)
 }

R/utils.R

@@ -231,9 +231,8 @@ is_seurat_or_se_object <- function(obj) {
       colData(sc)[["evalNA"]]<-evalNA
       return(sc[, !is.na(sc$cloneSize)])
     }else{
-      col.name <- names(evalNA) %||% colnames(evalNA)
-      sc[[col.name]] <- evalNA
-      sc <- subset(sc, cloneSize != 0)
+      pos <- which(evalNA[,"indicator"] != 0)
+      sc <- subset(sc, cells = rownames(evalNA)[pos])
       return(sc)
     }
 }

tests/testthat/test-alluvialClones.R

@@ -15,6 +15,15 @@ test_that("alluvialClones works", {
                    color = "Type")
   )
 
+  expect_doppelganger(
+    "alluvialClones_TRB_plot",
+    alluvialClones(test_obj, 
+                   cloneCall = "aa", 
+                   chain = "TRB",
+                   y.axes = c("Patient", "ident"), 
+                   color = "Type")
+  )
+
   expect_doppelganger(
     "alluvialClones_nocolor_plot",
     alluvialClones(test_obj, 
@@ -23,6 +32,24 @@ test_that("alluvialClones works", {
                    color = NULL)
   )
 
+  expect_doppelganger(
+    "alluvialClones_alpha_plot",
+    alluvialClones(test_obj, 
+                   cloneCall = "aa", 
+                   y.axes = c("Type", "ident"), 
+                   alpha = "Patient")
+  )
+
+  expect_doppelganger(
+    "alluvialClones_alphapluscolor_plot",
+    alluvialClones(test_obj, 
+                   cloneCall = "aa", 
+                   y.axes = c("Type", "ident"), 
+                   alpha = "Patient", 
+                   color = "Type")
+  )
+
+
   expect_doppelganger(
     "alluvialClones_facet_plot",
     alluvialClones(test_obj, 

tests/testthat/test-clonalCluster.R

@@ -16,6 +16,13 @@ test_that("clonalCluster works", {
     getdata("clustering", "clonalCluster_TRBaa_data")
   )
 
+  set.seed(42)
+  expect_equal(
+  clonalCluster(combined[1:2], 
+                chain = "TRB", 
+                sequence = "aa"),
+  getdata("clustering", "clonalCluster_2sample_data")
+  )
 
   set.seed(42)
   test_obj <- clonalCluster(test_obj, 
@@ -26,5 +33,17 @@ test_that("clonalCluster works", {
     test_obj@meta.data[, 7:16],
     getdata("clustering", "clonalCluster_TRBaa_metadata")
   )
+
+
+  BCR <- read.csv("https://www.borch.dev/uploads/contigs/b_contigs.csv")
+  combined.BCR <- combineBCR(BCR, 
+                             samples = "S1")
+  expect_equal(
+      clonalCluster(combined.BCR, 
+                    chain = "IGH", 
+                    sequence = "aa"),
+      getdata("clustering", "clonalCluster_IGHaa_data")
+  )
+
 })
 #TODO Add exportgraph test

tests/testthat/test-clonalLength.R

@@ -12,6 +12,18 @@ test_that("clonalLength works", {
                  scale = TRUE) 
   )
 
+  expect_doppelganger(
+    "clonalLength_TRB_plot", 
+    clonalLength(getCombined(), 
+                 chain = "TRB") 
+  )
+
+  expect_doppelganger(
+    "clonalLength_TRA_plot", 
+    clonalLength(getCombined(), 
+                 chain = "TRA") 
+  )
+
   combined <- addVariable(getCombined(), 
                           variable.name = "Type", 
                           variables = rep(c("B", "L"), 4))

tests/testthat/test-loadContigs.R

@@ -39,6 +39,11 @@ test_that("loadContigs works", {
                      getdata("load", "loadContigs_Immcantation")
     )
 
+    OS <- read.csv("https://www.borch.dev/uploads/contigs/OS_contigs.csv")
+    trial7 <- loadContigs(OS, format = "Omniscope")
+    expect_identical(trial7, 
+                     getdata("load", "loadContigs_Omniscope")
+    )
 }) 
 
 # TODO Add tests for .json and AIRR and OS

tests/testthat/test-percentKmer.R

@@ -32,4 +32,21 @@ test_that("percentKmer works for NTs", {
   )
 })
 
+test_that("tokenize_sequence works", {
+  expect_equal(.tokenize_sequence("CAYRSAQAGGTSYGKLTF", 3),
+               c("CAY", "AYR", "YRS", "RSA", "SAQ","AQA", "QAG", 
+                 "AGG", "GGT", "GTS", "TSY", "SYG", "YGK", "GKL", "KLT", "LTF")
+  )
+})
+
+test_that("tokenize_multiple_sequences works", {
+  expect_equal(.tokenize_multiple_sequences(c("TESTING", "MULTIPLE", "SEQUENCE", "TOKENIZER"), 4),
+               list(TESTING = c("TEST", "ESTI", "STIN", "TING"),
+                    MULTIPLE = c("MULT", "ULTI", "LTIP", "TIPL", "IPLE"),
+                    SEQUENCE = c("SEQU", "EQUE", "QUEN", "UENC", "ENCE"),
+                    TOKENIZER = c("TOKE", "OKEN", "KENI", "ENIZ", "NIZE", "IZER"))
+  )
+
+})
+
 # TODO test for cases where no kmers were counted (NA columns present)

tests/testthat/test-utils.R

@@ -12,11 +12,25 @@ test_that("'%!in%' works", {
 })
 
 # TODO off.the.chain
-# TODO groupList
 # TODO checkSingleObject
 # TODO .parseBCR
 # TODO lengthDF
 
+test_that(".removingMulti works", {
+  combined <- getCombined()
+  expect_equal(.removingMulti(combined[1:2]),
+               getdata("utils", "removingMulti_data"))
+
+})
+
+test_that(".filteringNA works", {
+  data("scRep_example")
+  test_obj <- combineExpression(getCombined(), scRep_example)
+  test_obj <- .filteringNA(test_obj)
+  expect_equal(test_obj@meta.data[, 7:13],
+               getdata("utils", "filteringNA_metadata"))
+
+})
 
 
 test_that(".list.input.return works", {