Last data update: 2014.03.03

 R: Create Boxplots for TCGA Datasets
boxplotTCGAR Documentation

Create Boxplots for TCGA Datasets

Description

Function creates boxplots (geom_boxplot) for TCGA Datasets.

Usage

boxplotTCGA(data, x, y, fill = x, coord.flip = TRUE, facet.names = NULL,
  ylab = y, xlab = x, legend.title = xlab, legend = "top", ...)

Arguments

data

A data.frame from TCGA study containing variables to be plotted.

x

A character name of variable containing groups.

y

A character name of continous variable to be plotted.

fill

A character names of fill variable. By default, the same as x.

coord.flip

Whether to flip coordinates.

facet.names

A character of length maximum 2 containing names of variables to produce facets. See examples.

ylab

The name of y label. Remember about coord.flip.

xlab

The name of x label. Remember about coord.flip.

legend.title

A character with legend's title.

legend

A character specifying legend position. Allowed values are one of c("top", "bottom", "left", "right", "none"). Default is "top" side position. to remove the legend use legend = "none".

...

Further arguments passed to geom_boxplot.

Issues

If you have any problems, issues or think that something is missing or is not clear please post an issue on https://github.com/RTCGA/RTCGA/issues.

Author(s)

Marcin Kosinski, m.p.kosinski@gmail.com

See Also

RTCGA website http://rtcga.github.io/RTCGA/Visualizations.html.

Other RTCGA: RTCGA-package, checkTCGA, convertTCGA, datasetsTCGA, downloadTCGA, expressionsTCGA, heatmapTCGA, infoTCGA, installTCGA, kmTCGA, mutationsTCGA, pcaTCGA, readTCGA, survivalTCGA, theme_RTCGA

Examples

library(RTCGA.rnaseq)
# perfrom plot
library(dplyr)
expressionsTCGA(ACC.rnaseq, BLCA.rnaseq, BRCA.rnaseq, OV.rnaseq,
	extract.cols = "MET|4233") %>%
	rename(cohort = dataset,
	MET = `MET|4233`) %>%  
	#cancer samples
	filter(substr(bcr_patient_barcode, 14, 15) == "01") -> ACC_BLCA_BRCA_OV.rnaseq
	

boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "cohort", "MET")
boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "cohort", "log1p(MET)")
boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "reorder(cohort,log1p(MET), median)", "log1p(MET)")
boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "reorder(cohort,log1p(MET), max)", "log1p(MET)")
boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "reorder(cohort,log1p(MET), median)", "log1p(MET)",
xlab = "Cohort Type", ylab = "Logarithm of MET")
boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "reorder(cohort,log1p(MET), median)", "log1p(MET)", 
xlab = "Cohort Type", ylab = "Logarithm of MET", legend.title = "Cohorts")
boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "reorder(cohort,log1p(MET), median)", "log1p(MET)", 
xlab = "Cohort Type", ylab = "Logarithm of MET", legend.title = "Cohorts", legend = "bottom")

## facet example
library(RTCGA.mutations)
library(dplyr)
mutationsTCGA(BRCA.mutations, OV.mutations, ACC.mutations, BLCA.mutations) %>% 
	filter(Hugo_Symbol == 'TP53') %>%
	filter(substr(bcr_patient_barcode, 14, 15) == "01") %>% # cancer tissue
	mutate(bcr_patient_barcode = substr(bcr_patient_barcode, 1, 12)) -> ACC_BLCA_BRCA_OV.mutations

mutationsTCGA(BRCA.mutations, OV.mutations, ACC.mutations, BLCA.mutations) -> ACC_BLCA_BRCA_OV.mutations_all

ACC_BLCA_BRCA_OV.rnaseq %>%
	mutate(bcr_patient_barcode = substr(bcr_patient_barcode, 1, 15)) %>%
	filter(bcr_patient_barcode %in% 
	substr(ACC_BLCA_BRCA_OV.mutations_all$bcr_patient_barcode, 1, 15)) %>%
	# took patients for which we had any mutation information
	# so avoided patients without any information about mutations
	mutate(bcr_patient_barcode = substr(bcr_patient_barcode, 1, 12)) %>%
	# strin_length(ACC_BLCA_BRCA_OV.mutations$bcr_patient_barcode) == 12
	left_join(ACC_BLCA_BRCA_OV.mutations,
	by = "bcr_patient_barcode") %>% #joined only with tumor patients
	mutate(TP53 = ifelse(!is.na(Variant_Classification), "Mut", "WILD")) %>%
	select(cohort, MET, TP53) -> ACC_BLCA_BRCA_OV.rnaseq_TP53mutations

boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq_TP53mutations,
 "reorder(cohort,log1p(MET), median)", "log1p(MET)", 
xlab = "Cohort Type", ylab = "Logarithm of MET",
 legend.title = "Cohorts", legend = "bottom",
facet.names = c("TP53"))

boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq_TP53mutations,
 "reorder(cohort,log1p(MET), median)", "log1p(MET)", 
xlab = "Cohort Type", ylab = "Logarithm of MET",
 legend.title = "Cohorts", legend = "bottom",
fill = c("TP53"))

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RTCGA)
Welcome to the RTCGA (version: 1.2.2).
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/RTCGA/boxplotTCGA.Rd_%03d_medium.png", width=480, height=480)
> ### Name: boxplotTCGA
> ### Title: Create Boxplots for TCGA Datasets
> ### Aliases: boxplotTCGA
> 
> ### ** Examples
> 
> library(RTCGA.rnaseq)
> # perfrom plot
> library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

> expressionsTCGA(ACC.rnaseq, BLCA.rnaseq, BRCA.rnaseq, OV.rnaseq,
+ 	extract.cols = "MET|4233") %>%
+ 	rename(cohort = dataset,
+ 	MET = `MET|4233`) %>%  
+ 	#cancer samples
+ 	filter(substr(bcr_patient_barcode, 14, 15) == "01") -> ACC_BLCA_BRCA_OV.rnaseq
> 	
> 
> boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "cohort", "MET")
> boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "cohort", "log1p(MET)")
> boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "reorder(cohort,log1p(MET), median)", "log1p(MET)")
> boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "reorder(cohort,log1p(MET), max)", "log1p(MET)")
> boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "reorder(cohort,log1p(MET), median)", "log1p(MET)",
+ xlab = "Cohort Type", ylab = "Logarithm of MET")
> boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "reorder(cohort,log1p(MET), median)", "log1p(MET)", 
+ xlab = "Cohort Type", ylab = "Logarithm of MET", legend.title = "Cohorts")
> boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq, "reorder(cohort,log1p(MET), median)", "log1p(MET)", 
+ xlab = "Cohort Type", ylab = "Logarithm of MET", legend.title = "Cohorts", legend = "bottom")
> 
> ## facet example
> library(RTCGA.mutations)
> library(dplyr)
> mutationsTCGA(BRCA.mutations, OV.mutations, ACC.mutations, BLCA.mutations) %>% 
+ 	filter(Hugo_Symbol == 'TP53') %>%
+ 	filter(substr(bcr_patient_barcode, 14, 15) == "01") %>% # cancer tissue
+ 	mutate(bcr_patient_barcode = substr(bcr_patient_barcode, 1, 12)) -> ACC_BLCA_BRCA_OV.mutations
> 
> mutationsTCGA(BRCA.mutations, OV.mutations, ACC.mutations, BLCA.mutations) -> ACC_BLCA_BRCA_OV.mutations_all
> 
> ACC_BLCA_BRCA_OV.rnaseq %>%
+ 	mutate(bcr_patient_barcode = substr(bcr_patient_barcode, 1, 15)) %>%
+ 	filter(bcr_patient_barcode %in% 
+ 	substr(ACC_BLCA_BRCA_OV.mutations_all$bcr_patient_barcode, 1, 15)) %>%
+ 	# took patients for which we had any mutation information
+ 	# so avoided patients without any information about mutations
+ 	mutate(bcr_patient_barcode = substr(bcr_patient_barcode, 1, 12)) %>%
+ 	# strin_length(ACC_BLCA_BRCA_OV.mutations$bcr_patient_barcode) == 12
+ 	left_join(ACC_BLCA_BRCA_OV.mutations,
+ 	by = "bcr_patient_barcode") %>% #joined only with tumor patients
+ 	mutate(TP53 = ifelse(!is.na(Variant_Classification), "Mut", "WILD")) %>%
+ 	select(cohort, MET, TP53) -> ACC_BLCA_BRCA_OV.rnaseq_TP53mutations
> 
> boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq_TP53mutations,
+  "reorder(cohort,log1p(MET), median)", "log1p(MET)", 
+ xlab = "Cohort Type", ylab = "Logarithm of MET",
+  legend.title = "Cohorts", legend = "bottom",
+ facet.names = c("TP53"))
> 
> boxplotTCGA(ACC_BLCA_BRCA_OV.rnaseq_TP53mutations,
+  "reorder(cohort,log1p(MET), median)", "log1p(MET)", 
+ xlab = "Cohort Type", ylab = "Logarithm of MET",
+  legend.title = "Cohorts", legend = "bottom",
+ fill = c("TP53"))
> 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO