RTCGA (version 1.2.2)

boxplotTCGA: 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.

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"))