From e47458a24619ffd49a4e619b2e539211e9a6db1f Mon Sep 17 00:00:00 2001 From: "Zachary S.L Foster" Date: Tue, 1 May 2018 16:03:06 -0700 Subject: [PATCH] rebuild vignettes --- README.md | 420 ++++++++++++---------- man/figures/README-unnamed-chunk-15-1.png | Bin 72609 -> 69850 bytes 2 files changed, 232 insertions(+), 188 deletions(-) diff --git a/README.md b/README.md index a3a2190..8402e7e 100755 --- a/README.md +++ b/README.md @@ -52,30 +52,37 @@ This is the preferred way to encode this type of abundance information in `metac ``` r library(metacoder) #> Loading required package: taxa +#> This is metacoder verison 0.2.1 (stable). If you use metacoder for published research, please cite our paper: +#> +#> Foster Z, Sharpton T and Grunwald N (2017). "Metacoder: An R package for visualization and manipulation of community taxonomic diversity data." PLOS Computational Biology, 13(2), pp. 1-15. doi: 10.1371/journal.pcbi.1005404 +#> +#> Enter `citation("metacoder")` for a BibTeX entry for this citation. print(hmp_otus) #> # A tibble: 1,000 x 52 -#> otu_id lineage `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… -#> -#> 1 OTU_9… r__Roo… 0 2 1 0 0 0 0 0 0 0 2 6 -#> 2 OTU_9… r__Roo… 0 0 0 0 0 0 0 0 0 0 0 0 -#> 3 OTU_9… r__Roo… 0 1 0 0 0 0 0 0 0 0 1 1 -#> 4 OTU_9… r__Roo… 8 36 10 5 66 38 8 4 17 21 0 0 -#> 5 OTU_9… r__Roo… 3 25 0 0 0 1 0 0 0 0 10 67 -#> 6 OTU_9… r__Roo… 42 277 16 22 85 211 20 33 44 52 0 0 -#> 7 OTU_9… r__Roo… 4 17 21 1 74 12 5 15 13 115 0 0 -#> 8 OTU_9… r__Roo… 0 0 0 0 0 0 0 0 4 1 0 0 -#> 9 OTU_9… r__Roo… 0 0 0 0 0 0 0 0 0 0 0 7 -#> 10 OTU_9… r__Roo… 0 0 0 0 1 0 0 0 0 0 0 0 -#> # ... with 990 more rows, and 38 more variables: `700103488` , `700096869` , -#> # `700107379` , `700096422` , `700102417` , `700114168` , -#> # `700037540` , `700106397` , `700113498` , `700033743` , -#> # `700105205` , `700024238` , `700034183` , `700038390` , -#> # `700015973` , `700038124` , `700107206` , `700037403` , -#> # `700098429` , `700101224` , `700114615` , `700024234` , -#> # `700108596` , `700101076` , `700105882` , `700016902` , -#> # `700102242` , `700038231` , `700109394` , `700102530` , -#> # `700108229` , `700099013` , `700098680` , `700106938` , -#> # `700014916` , `700095535` , `700102367` , `700101358` +#> otu_id lineage `700035949` `700097855` `700100489` `700111314` `700033744` `700109581` +#> +#> 1 OTU_9… r__Roo… 0 2 1 0 0 0 +#> 2 OTU_9… r__Roo… 0 0 0 0 0 0 +#> 3 OTU_9… r__Roo… 0 1 0 0 0 0 +#> 4 OTU_9… r__Roo… 8 36 10 5 66 38 +#> 5 OTU_9… r__Roo… 3 25 0 0 0 1 +#> 6 OTU_9… r__Roo… 42 277 16 22 85 211 +#> 7 OTU_9… r__Roo… 4 17 21 1 74 12 +#> 8 OTU_9… r__Roo… 0 0 0 0 0 0 +#> 9 OTU_9… r__Roo… 0 0 0 0 0 0 +#> 10 OTU_9… r__Roo… 0 0 0 0 1 0 +#> # ... with 990 more rows, and 44 more variables: `700111044` , `700101365` , +#> # `700100431` , `700016050` , `700032425` , `700024855` , +#> # `700103488` , `700096869` , `700107379` , `700096422` , +#> # `700102417` , `700114168` , `700037540` , `700106397` , +#> # `700113498` , `700033743` , `700105205` , `700024238` , +#> # `700034183` , `700038390` , `700015973` , `700038124` , +#> # `700107206` , `700037403` , `700098429` , `700101224` , +#> # `700114615` , `700024234` , `700108596` , `700101076` , +#> # `700105882` , `700016902` , `700102242` , `700038231` , +#> # `700109394` , `700102530` , `700108229` , `700099013` , +#> # `700098680` , `700106938` , `700014916` , `700095535` , +#> # `700102367` , `700101358` print(hmp_samples) #> # A tibble: 50 x 3 #> # Groups: body_site, sex [10] @@ -112,29 +119,34 @@ print(obj) #> 2 data sets: #> tax_data: #> # A tibble: 1,000 x 53 -#> taxo… otu_… line… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… -#> -#> 1 dm OTU_… r__R… 0 2 1 0 0 0 0 0 0 0 -#> 2 dn OTU_… r__R… 0 0 0 0 0 0 0 0 0 0 -#> 3 do OTU_… r__R… 0 1 0 0 0 0 0 0 0 0 -#> # ... with 997 more rows, and 40 more variables: `700032425` , -#> # `700024855` , `700103488` , `700096869` , `700107379` , -#> # `700096422` , `700102417` , `700114168` , `700037540` , -#> # `700106397` , `700113498` , `700033743` , `700105205` , -#> # `700024238` , `700034183` , `700038390` , `700015973` , -#> # `700038124` , `700107206` , `700037403` , `700098429` , -#> # `700101224` , `700114615` , `700024234` , `700108596` , -#> # `700101076` , `700105882` , `700016902` , `700102242` , -#> # `700038231` , `700109394` , `700102530` , `700108229` , -#> # `700099013` , `700098680` , `700106938` , `700014916` , +#> taxon_id otu_id lineage `700035949` `700097855` `700100489` `700111314` +#> +#> 1 dm OTU_9… r__Roo… 0 2 1 0 +#> 2 dn OTU_9… r__Roo… 0 0 0 0 +#> 3 do OTU_9… r__Roo… 0 1 0 0 +#> # ... with 997 more rows, and 46 more variables: `700033744` , +#> # `700109581` , `700111044` , `700101365` , +#> # `700100431` , `700016050` , `700032425` , +#> # `700024855` , `700103488` , `700096869` , +#> # `700107379` , `700096422` , `700102417` , +#> # `700114168` , `700037540` , `700106397` , +#> # `700113498` , `700033743` , `700105205` , +#> # `700024238` , `700034183` , `700038390` , +#> # `700015973` , `700038124` , `700107206` , +#> # `700037403` , `700098429` , `700101224` , +#> # `700114615` , `700024234` , `700108596` , +#> # `700101076` , `700105882` , `700016902` , +#> # `700102242` , `700038231` , `700109394` , +#> # `700102530` , `700108229` , `700099013` , +#> # `700098680` , `700106938` , `700014916` , #> # `700095535` , `700102367` , `700101358` #> class_data: #> # A tibble: 5,922 x 5 -#> taxon_id input_index tax_rank tax_name regex_match -#> -#> 1 ab 1 r Root r__Root -#> 2 ac 1 p Proteobacteria p__Proteobacteria -#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacteria +#> taxon_id input_index tax_rank tax_name regex_match +#> +#> 1 ab 1 r Root r__Root +#> 2 ac 1 p Proteobacteria p__Proteobacteria +#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacter… #> # ... with 5,919 more rows #> 0 functions: ``` @@ -148,7 +160,6 @@ Low-abundance sequences might be the result of sequencing error, so typically we ``` r obj$data$tax_data <- zero_low_counts(obj, "tax_data", min_count = 5) -#> Converting to zero all counts less than 5. #> No `cols` specified, so using all numeric columns: #> 700035949, 700097855, 700100489 ... 700095535, 700102367, 700101358 #> Zeroing 4325 of 50000 counts less than 5. @@ -173,30 +184,35 @@ print(obj) #> 2 data sets: #> tax_data: #> # A tibble: 789 x 51 -#> taxo… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… `700… -#> -#> 1 dm 0 0 0 0 0 0 0 0 0 0 0 6.00 -#> 2 dn 0 0 0 0 0 0 0 0 0 0 0 0 -#> 3 do 0 0 0 0 0 0 0 0 0 0 0 0 -#> # ... with 786 more rows, and 38 more variables: `700103488` , -#> # `700096869` , `700107379` , `700096422` , `700102417` , -#> # `700114168` , `700037540` , `700106397` , `700113498` , -#> # `700033743` , `700105205` , `700024238` , `700034183` , -#> # `700038390` , `700015973` , `700038124` , `700107206` , -#> # `700037403` , `700098429` , `700101224` , `700114615` , -#> # `700024234` , `700108596` , `700101076` , `700105882` , -#> # `700016902` , `700102242` , `700038231` , `700109394` , -#> # `700102530` , `700108229` , `700099013` , `700098680` , -#> # `700106938` , `700014916` , `700095535` , `700102367` , -#> # `700101358` +#> taxon_id `700035949` `700097855` `700100489` `700111314` `700033744` +#> +#> 1 dm 0. 0. 0. 0. 0. +#> 2 dn 0. 0. 0. 0. 0. +#> 3 do 0. 0. 0. 0. 0. +#> # ... with 786 more rows, and 45 more variables: `700109581` , +#> # `700111044` , `700101365` , `700100431` , +#> # `700016050` , `700032425` , `700024855` , +#> # `700103488` , `700096869` , `700107379` , +#> # `700096422` , `700102417` , `700114168` , +#> # `700037540` , `700106397` , `700113498` , +#> # `700033743` , `700105205` , `700024238` , +#> # `700034183` , `700038390` , `700015973` , +#> # `700038124` , `700107206` , `700037403` , +#> # `700098429` , `700101224` , `700114615` , +#> # `700024234` , `700108596` , `700101076` , +#> # `700105882` , `700016902` , `700102242` , +#> # `700038231` , `700109394` , `700102530` , +#> # `700108229` , `700099013` , `700098680` , +#> # `700106938` , `700014916` , `700095535` , +#> # `700102367` , `700101358` #> class_data: -#> # A tibble: 5,922 x 5 -#> taxon_id input_index tax_rank tax_name regex_match -#> -#> 1 ab 1 r Root r__Root -#> 2 ac 1 p Proteobacteria p__Proteobacteria -#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacteria -#> # ... with 5,919 more rows +#> # A tibble: 5,903 x 5 +#> taxon_id input_index tax_rank tax_name regex_match +#> +#> 1 ab 1 r Root r__Root +#> 2 ac 1 p Proteobacteria p__Proteobacteria +#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacter… +#> # ... with 5,900 more rows #> 0 functions: ``` @@ -210,6 +226,7 @@ These are raw counts, but people typically work with rarefied counts or proporti obj$data$tax_data <- calc_obs_props(obj, "tax_data") #> No `cols` specified, so using all numeric columns: #> 700035949, 700097855, 700100489 ... 700095535, 700102367, 700101358 +#> Calculating proportions from counts for 50 columns for 789 observations. print(obj) #> #> 155 taxa: ab. Root, ac. Proteobacteria ... gs. Clostridium @@ -217,30 +234,35 @@ print(obj) #> 2 data sets: #> tax_data: #> # A tibble: 789 x 51 -#> taxon… `7000… `7000… `7001… `7001… `7000… `700… `700… `700… `700… `700… `700… -#> -#> 1 dm 0 0 0 0 0 0 0 0 0 0 0 -#> 2 dn 0 0 0 0 0 0 0 0 0 0 0 -#> 3 do 0 0 0 0 0 0 0 0 0 0 0 -#> # ... with 786 more rows, and 39 more variables: `700024855` , -#> # `700103488` , `700096869` , `700107379` , `700096422` , -#> # `700102417` , `700114168` , `700037540` , `700106397` , -#> # `700113498` , `700033743` , `700105205` , `700024238` , -#> # `700034183` , `700038390` , `700015973` , `700038124` , -#> # `700107206` , `700037403` , `700098429` , `700101224` , -#> # `700114615` , `700024234` , `700108596` , `700101076` , -#> # `700105882` , `700016902` , `700102242` , `700038231` , -#> # `700109394` , `700102530` , `700108229` , `700099013` , -#> # `700098680` , `700106938` , `700014916` , `700095535` , +#> taxon_id `700035949` `700097855` `700100489` `700111314` `700033744` +#> +#> 1 dm 0. 0. 0. 0. 0. +#> 2 dn 0. 0. 0. 0. 0. +#> 3 do 0. 0. 0. 0. 0. +#> # ... with 786 more rows, and 45 more variables: `700109581` , +#> # `700111044` , `700101365` , `700100431` , +#> # `700016050` , `700032425` , `700024855` , +#> # `700103488` , `700096869` , `700107379` , +#> # `700096422` , `700102417` , `700114168` , +#> # `700037540` , `700106397` , `700113498` , +#> # `700033743` , `700105205` , `700024238` , +#> # `700034183` , `700038390` , `700015973` , +#> # `700038124` , `700107206` , `700037403` , +#> # `700098429` , `700101224` , `700114615` , +#> # `700024234` , `700108596` , `700101076` , +#> # `700105882` , `700016902` , `700102242` , +#> # `700038231` , `700109394` , `700102530` , +#> # `700108229` , `700099013` , `700098680` , +#> # `700106938` , `700014916` , `700095535` , #> # `700102367` , `700101358` #> class_data: -#> # A tibble: 5,922 x 5 -#> taxon_id input_index tax_rank tax_name regex_match -#> -#> 1 ab 1 r Root r__Root -#> 2 ac 1 p Proteobacteria p__Proteobacteria -#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacteria -#> # ... with 5,919 more rows +#> # A tibble: 5,903 x 5 +#> taxon_id input_index tax_rank tax_name regex_match +#> +#> 1 ab 1 r Root r__Root +#> 2 ac 1 p Proteobacteria p__Proteobacteria +#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacter… +#> # ... with 5,900 more rows #> 0 functions: ``` @@ -259,48 +281,58 @@ print(obj) #> 3 data sets: #> tax_data: #> # A tibble: 789 x 51 -#> taxon… `7000… `7000… `7001… `7001… `7000… `700… `700… `700… `700… `700… `700… -#> -#> 1 dm 0 0 0 0 0 0 0 0 0 0 0 -#> 2 dn 0 0 0 0 0 0 0 0 0 0 0 -#> 3 do 0 0 0 0 0 0 0 0 0 0 0 -#> # ... with 786 more rows, and 39 more variables: `700024855` , -#> # `700103488` , `700096869` , `700107379` , `700096422` , -#> # `700102417` , `700114168` , `700037540` , `700106397` , -#> # `700113498` , `700033743` , `700105205` , `700024238` , -#> # `700034183` , `700038390` , `700015973` , `700038124` , -#> # `700107206` , `700037403` , `700098429` , `700101224` , -#> # `700114615` , `700024234` , `700108596` , `700101076` , -#> # `700105882` , `700016902` , `700102242` , `700038231` , -#> # `700109394` , `700102530` , `700108229` , `700099013` , -#> # `700098680` , `700106938` , `700014916` , `700095535` , +#> taxon_id `700035949` `700097855` `700100489` `700111314` `700033744` +#> +#> 1 dm 0. 0. 0. 0. 0. +#> 2 dn 0. 0. 0. 0. 0. +#> 3 do 0. 0. 0. 0. 0. +#> # ... with 786 more rows, and 45 more variables: `700109581` , +#> # `700111044` , `700101365` , `700100431` , +#> # `700016050` , `700032425` , `700024855` , +#> # `700103488` , `700096869` , `700107379` , +#> # `700096422` , `700102417` , `700114168` , +#> # `700037540` , `700106397` , `700113498` , +#> # `700033743` , `700105205` , `700024238` , +#> # `700034183` , `700038390` , `700015973` , +#> # `700038124` , `700107206` , `700037403` , +#> # `700098429` , `700101224` , `700114615` , +#> # `700024234` , `700108596` , `700101076` , +#> # `700105882` , `700016902` , `700102242` , +#> # `700038231` , `700109394` , `700102530` , +#> # `700108229` , `700099013` , `700098680` , +#> # `700106938` , `700014916` , `700095535` , #> # `700102367` , `700101358` #> class_data: -#> # A tibble: 5,922 x 5 -#> taxon_id input_index tax_rank tax_name regex_match -#> -#> 1 ab 1 r Root r__Root -#> 2 ac 1 p Proteobacteria p__Proteobacteria -#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacteria -#> # ... with 5,919 more rows +#> # A tibble: 5,903 x 5 +#> taxon_id input_index tax_rank tax_name regex_match +#> +#> 1 ab 1 r Root r__Root +#> 2 ac 1 p Proteobacteria p__Proteobacteria +#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacter… +#> # ... with 5,900 more rows #> tax_abund: #> # A tibble: 155 x 51 -#> taxon… `7000… `700097… `7001… `70011… `7000… `700109… `70011… `70010… `70010… -#> * -#> 1 ab 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 -#> 2 ac 0.206 0.0262 0 0.252 0.225 0.00187 0 0.00736 0.0938 -#> 3 ad 0 0.00269 0 0.0417 0 0 0.00439 0 0.00708 -#> # ... with 152 more rows, and 41 more variables: `700016050` , -#> # `700032425` , `700024855` , `700103488` , `700096869` , -#> # `700107379` , `700096422` , `700102417` , `700114168` , -#> # `700037540` , `700106397` , `700113498` , `700033743` , -#> # `700105205` , `700024238` , `700034183` , `700038390` , -#> # `700015973` , `700038124` , `700107206` , `700037403` , -#> # `700098429` , `700101224` , `700114615` , `700024234` , -#> # `700108596` , `700101076` , `700105882` , `700016902` , -#> # `700102242` , `700038231` , `700109394` , `700102530` , -#> # `700108229` , `700099013` , `700098680` , `700106938` , -#> # `700014916` , `700095535` , `700102367` , `700101358` +#> taxon_id `700035949` `700097855` `700100489` `700111314` `700033744` +#> * +#> 1 ab 1.00 1.00 1. 1.00 1.00 +#> 2 ac 0.206 0.0262 0. 0.252 0.225 +#> 3 ad 0. 0.00269 0. 0.0417 0. +#> # ... with 152 more rows, and 45 more variables: `700109581` , +#> # `700111044` , `700101365` , `700100431` , +#> # `700016050` , `700032425` , `700024855` , +#> # `700103488` , `700096869` , `700107379` , +#> # `700096422` , `700102417` , `700114168` , +#> # `700037540` , `700106397` , `700113498` , +#> # `700033743` , `700105205` , `700024238` , +#> # `700034183` , `700038390` , `700015973` , +#> # `700038124` , `700107206` , `700037403` , +#> # `700098429` , `700101224` , `700114615` , +#> # `700024234` , `700108596` , `700101076` , +#> # `700105882` , `700016902` , `700102242` , +#> # `700038231` , `700109394` , `700102530` , +#> # `700108229` , `700099013` , `700098680` , +#> # `700106938` , `700014916` , `700095535` , +#> # `700102367` , `700101358` #> 0 functions: ``` @@ -312,6 +344,7 @@ We can also easily calculate the number of samples have reads for each taxon: obj$data$tax_occ <- calc_n_samples(obj, "tax_abund", groups = hmp_samples$body_site) #> No `cols` specified, so using all numeric columns: #> 700035949, 700097855, 700100489 ... 700095535, 700102367, 700101358 +#> Calculating number of samples with non-zero counts from 50 columns in 5 groups for 155 observations print(obj) #> #> 155 taxa: ab. Root, ac. Proteobacteria ... gs. Clostridium @@ -319,48 +352,58 @@ print(obj) #> 4 data sets: #> tax_data: #> # A tibble: 789 x 51 -#> taxon… `7000… `7000… `7001… `7001… `7000… `700… `700… `700… `700… `700… `700… -#> -#> 1 dm 0 0 0 0 0 0 0 0 0 0 0 -#> 2 dn 0 0 0 0 0 0 0 0 0 0 0 -#> 3 do 0 0 0 0 0 0 0 0 0 0 0 -#> # ... with 786 more rows, and 39 more variables: `700024855` , -#> # `700103488` , `700096869` , `700107379` , `700096422` , -#> # `700102417` , `700114168` , `700037540` , `700106397` , -#> # `700113498` , `700033743` , `700105205` , `700024238` , -#> # `700034183` , `700038390` , `700015973` , `700038124` , -#> # `700107206` , `700037403` , `700098429` , `700101224` , -#> # `700114615` , `700024234` , `700108596` , `700101076` , -#> # `700105882` , `700016902` , `700102242` , `700038231` , -#> # `700109394` , `700102530` , `700108229` , `700099013` , -#> # `700098680` , `700106938` , `700014916` , `700095535` , +#> taxon_id `700035949` `700097855` `700100489` `700111314` `700033744` +#> +#> 1 dm 0. 0. 0. 0. 0. +#> 2 dn 0. 0. 0. 0. 0. +#> 3 do 0. 0. 0. 0. 0. +#> # ... with 786 more rows, and 45 more variables: `700109581` , +#> # `700111044` , `700101365` , `700100431` , +#> # `700016050` , `700032425` , `700024855` , +#> # `700103488` , `700096869` , `700107379` , +#> # `700096422` , `700102417` , `700114168` , +#> # `700037540` , `700106397` , `700113498` , +#> # `700033743` , `700105205` , `700024238` , +#> # `700034183` , `700038390` , `700015973` , +#> # `700038124` , `700107206` , `700037403` , +#> # `700098429` , `700101224` , `700114615` , +#> # `700024234` , `700108596` , `700101076` , +#> # `700105882` , `700016902` , `700102242` , +#> # `700038231` , `700109394` , `700102530` , +#> # `700108229` , `700099013` , `700098680` , +#> # `700106938` , `700014916` , `700095535` , #> # `700102367` , `700101358` #> class_data: -#> # A tibble: 5,922 x 5 -#> taxon_id input_index tax_rank tax_name regex_match -#> -#> 1 ab 1 r Root r__Root -#> 2 ac 1 p Proteobacteria p__Proteobacteria -#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacteria -#> # ... with 5,919 more rows +#> # A tibble: 5,903 x 5 +#> taxon_id input_index tax_rank tax_name regex_match +#> +#> 1 ab 1 r Root r__Root +#> 2 ac 1 p Proteobacteria p__Proteobacteria +#> 3 aj 1 c Gammaproteobacteria c__Gammaproteobacter… +#> # ... with 5,900 more rows #> tax_abund: #> # A tibble: 155 x 51 -#> taxon… `7000… `700097… `7001… `70011… `7000… `700109… `70011… `70010… `70010… -#> * -#> 1 ab 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 -#> 2 ac 0.206 0.0262 0 0.252 0.225 0.00187 0 0.00736 0.0938 -#> 3 ad 0 0.00269 0 0.0417 0 0 0.00439 0 0.00708 -#> # ... with 152 more rows, and 41 more variables: `700016050` , -#> # `700032425` , `700024855` , `700103488` , `700096869` , -#> # `700107379` , `700096422` , `700102417` , `700114168` , -#> # `700037540` , `700106397` , `700113498` , `700033743` , -#> # `700105205` , `700024238` , `700034183` , `700038390` , -#> # `700015973` , `700038124` , `700107206` , `700037403` , -#> # `700098429` , `700101224` , `700114615` , `700024234` , -#> # `700108596` , `700101076` , `700105882` , `700016902` , -#> # `700102242` , `700038231` , `700109394` , `700102530` , -#> # `700108229` , `700099013` , `700098680` , `700106938` , -#> # `700014916` , `700095535` , `700102367` , `700101358` +#> taxon_id `700035949` `700097855` `700100489` `700111314` `700033744` +#> * +#> 1 ab 1.00 1.00 1. 1.00 1.00 +#> 2 ac 0.206 0.0262 0. 0.252 0.225 +#> 3 ad 0. 0.00269 0. 0.0417 0. +#> # ... with 152 more rows, and 45 more variables: `700109581` , +#> # `700111044` , `700101365` , `700100431` , +#> # `700016050` , `700032425` , `700024855` , +#> # `700103488` , `700096869` , `700107379` , +#> # `700096422` , `700102417` , `700114168` , +#> # `700037540` , `700106397` , `700113498` , +#> # `700033743` , `700105205` , `700024238` , +#> # `700034183` , `700038390` , `700015973` , +#> # `700038124` , `700107206` , `700037403` , +#> # `700098429` , `700101224` , `700114615` , +#> # `700024234` , `700108596` , `700101076` , +#> # `700105882` , `700016902` , `700102242` , +#> # `700038231` , `700109394` , `700102530` , +#> # `700108229` , `700099013` , `700098680` , +#> # `700106938` , `700014916` , `700095535` , +#> # `700102367` , `700101358` #> tax_occ: #> # A tibble: 155 x 6 #> taxon_id Nose Saliva Skin Stool Throat @@ -408,18 +451,18 @@ obj$data$diff_table <- compare_groups(obj, dataset = "tax_abund", groups = hmp_samples$sex) print(obj$data$diff_table) #> # A tibble: 155 x 7 -#> taxon_id treatment_1 treatment_2 log2_median_ratio median_diff mean_diff wilcox_p_val… -#> -#> 1 ab female male 0 0 0 NaN -#> 2 ac female male 0.380 0.0229 0.0379 0.470 -#> 3 ad female male -0.434 -0.0449 -0.0199 0.907 -#> 4 ae female male -1.68 -0.0475 -0.0753 0.335 -#> 5 af female male 0.649 0.116 0.0614 0.386 -#> 6 ag female male 0 0 -0.00275 0.732 -#> 7 ah female male 0 0 -0.00140 0.602 -#> 8 aj female male 1.24 0.0162 -0.0129 0.680 -#> 9 ak female male 0 0 0.00121 0.416 -#> 10 al female male -0.542 -0.0541 -0.0211 0.969 +#> taxon_id treatment_1 treatment_2 log2_median_rat… median_diff mean_diff wilcox_p_value +#> +#> 1 ab female male 0. 0. 0. NaN +#> 2 ac female male 0.380 0.0229 0.0379 0.470 +#> 3 ad female male -0.434 -0.0449 -0.0199 0.907 +#> 4 ae female male -1.68 -0.0475 -0.0753 0.335 +#> 5 af female male 0.649 0.116 0.0614 0.386 +#> 6 ag female male 0. 0. -0.00275 0.732 +#> 7 ah female male 0. 0. -0.00140 0.602 +#> 8 aj female male 1.24 0.0162 -0.0129 0.680 +#> 9 ak female male 0. 0. 0.00121 0.416 +#> 10 al female male -0.542 -0.0541 -0.0211 0.969 #> # ... with 145 more rows ``` @@ -431,6 +474,7 @@ heat_tree(obj, node_size = n_obs, node_color = log2_median_ratio, node_color_interval = c(-2, 2), + edge_color_interval = c(-2, 2), node_color_range = c("cyan", "gray", "tan"), node_size_axis_label = "OTU count", node_color_axis_label = "Log 2 ratio of median proportions") @@ -465,18 +509,18 @@ obj$data$diff_table <- compare_groups(obj, dataset = "tax_abund", groups = hmp_samples$body_site) print(obj$data$diff_table) #> # A tibble: 1,550 x 7 -#> taxon_id treatment_1 treatment_2 log2_median_ratio median_diff mean_diff wilcox_p_val… -#> -#> 1 ab Nose Saliva 0 0 0 NaN -#> 2 ac Nose Saliva - 2.62 -0.167 -0.128 0.0172 -#> 3 ad Nose Saliva - 7.68 -0.274 -0.265 0.000163 -#> 4 ae Nose Saliva 5.36 0.616 0.595 0.0000108 -#> 5 af Nose Saliva - 1.23 -0.260 -0.159 0.0433 -#> 6 ag Nose Saliva -Inf -0.0228 -0.0436 0.0000874 -#> 7 ah Nose Saliva 0 0 0 NaN -#> 8 aj Nose Saliva - 3.83 -0.103 -0.0803 0.00707 -#> 9 ak Nose Saliva -Inf -0.0174 -0.0174 0.00156 -#> 10 al Nose Saliva -Inf -0.258 -0.248 0.000149 +#> taxon_id treatment_1 treatment_2 log2_median_rat… median_diff mean_diff wilcox_p_value +#> +#> 1 ab Nose Saliva 0. 0. 0. NaN +#> 2 ac Nose Saliva -2.62 -0.167 -0.128 0.0172 +#> 3 ad Nose Saliva -7.68 -0.274 -0.265 0.000163 +#> 4 ae Nose Saliva 5.36 0.616 0.595 0.0000108 +#> 5 af Nose Saliva -1.23 -0.260 -0.159 0.0433 +#> 6 ag Nose Saliva -Inf -0.0228 -0.0436 0.0000874 +#> 7 ah Nose Saliva 0. 0. 0. NaN +#> 8 aj Nose Saliva -3.83 -0.103 -0.0803 0.00707 +#> 9 ak Nose Saliva -Inf -0.0174 -0.0174 0.00156 +#> 10 al Nose Saliva -Inf -0.258 -0.248 0.000149 #> # ... with 1,540 more rows ``` diff --git a/man/figures/README-unnamed-chunk-15-1.png b/man/figures/README-unnamed-chunk-15-1.png index 926b882b6de018dd75495bddea8a785c733d9bb5..42af7f9ac37e45d7f689e491a13f5c31e1440383 100644 GIT binary patch literal 69850 zcmeEu^;=Y5`0db04&AAMA|(h&cc*}~ASp=q(A^EvB?2NMBGTP04blzL49(DY`}uzF zpKyP>Jj^p^nBhG8?6dd#u6M2VPNcf394;0m76bypRgjm~fIyHx{d-^{gTD+K=_Y}v zM~?D(E)WP_=f4M1uftb!2!tM@AT6N<%idq~gi)R6h#i%y?)|b?5`D565NGoe#KJ|g*59MSR# z({k;GK`NWIpTF`LmR?fPm^@{yXnAOvZp{G6Sy}K75{D)>f#x!MN4DNTN1;l8Ut|(?jz>AgXLB+@E-wc94Q~G zw$Bm%*4EZM!>#@ge%1QE$7?eA@-OdZKYwPdoXyV6+!a1vsCP+8Nl|9jEYafQjB$K+wYH}lIq4*~)LS2JeuIj@}7qB&dSRaNcn z?P)0~zx^noprs{!6ska}qM|aCBjoW>(Zaz2e(knLNl6Knq@kgC{P;0tY*_m{$9ls^ z>61|@{8jA9y>k%}k!BH*Q!K164*5pq(|hOmB4-Ik!1S?h|-&(C+; z`}0OiOZcSk@%El8e0zJ;>Lubq$zfr8dwb^e4(_ct+~j$>ySKMBk{5469%j<7Zx?OU 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