R/boxplotTCGA.R

In RTCGA: The Cancer Genome Atlas Data Integration

## RTCGA package for R
#' @title Create Boxplots for TCGA Datasets
#'
#' @description Function creates boxplots (\link{geom_boxplot}) for TCGA Datasets.
#'  
#' @param data A data.frame from TCGA study containing variables to be plotted. 
#' @param ... Further arguments passed to \link{geom_boxplot}.
#' @param coord.flip Whether to flip coordinates.
#' @param facet.names A character of length maximum 2 containing names of variables to produce facets. See examples.
#' @param x A character name of variable containing groups.
#' @param y A character name of continous variable to be plotted.
#' @param fill A character names of fill variable. By default, the same as \code{x}.
#' @param ylab The name of y label. Remember about \code{coord.flip}.
#' @param xlab The name of x label. Remember about \code{coord.flip}.
#' @param legend.title A character with legend's title.
#' @param 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". 
#' 
#' @section Issues:
#' 
#' If you have any problems, issues or think that something is missing or is not
#' clear please post an issue on 
#' \href{https://github.com/RTCGA/RTCGA/issues}{https://github.com/RTCGA/RTCGA/issues}.
#' 
#' @author 
#' Marcin Kosinski, \email{m.p.kosinski@@gmail.com}
#' @seealso 
#' 
#' \pkg{RTCGA} website \href{http://rtcga.github.io/RTCGA/Visualizations.html}{http://rtcga.github.io/RTCGA/Visualizations.html}.
#' @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"))
#' 
#' 
#' @family RTCGA
#' @rdname boxplotTCGA
#' @export
boxplotTCGA <- function(data, x, y, fill = x, coord.flip = TRUE, 
												facet.names = NULL, ylab = y, xlab = x, legend.title = xlab,  
												legend = "top", ...){
	assert_that(is.null(facet.names) | 
								(is.character(facet.names) & length(facet.names) %in% c(1,2)))
	
	ggplot(data, aes_string(y = y,
													x = x,
													fill = fill)) + 
		geom_boxplot(...) +
		theme_RTCGA() -> gplot
	
	if (coord.flip)
		gplot <- gplot + coord_flip()
	if (is.character(facet.names) & length(facet.names) == 2) {
		gplot <- gplot +
			facet_grid(reformulate(facet.names[1], facet.names[2]))
	}
	if (is.character(facet.names) & length(facet.names) == 1) {
		gplot <- gplot +
			facet_grid(reformulate(facet.names[1]))
	}
	gplot + labs(y = ylab, x = xlab, color = legend.title, fill = legend.title) +
		theme(legend.position = legend)
	
}