heatmapTCGA: Create Heatmaps for TCGA Datasets

In RTCGA: The Cancer Genome Atlas Data Integration

Description

Function creates heatmaps (geom_tile) for TCGA Datasets.

Usage

heatmapTCGA(data, x, y, fill, legend.title = "Expression", legend = "right",
  title = "Heatmap of expression", facet.names = NULL, tile.size = 0.1,
  tile.color = "white", ...)

Arguments

data	A data.frame from TCGA study containing variables to be plotted.
x, y	A character name of variable containing groups.
fill	A character names of fill variable.
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".
title	A character with plot title.
facet.names	A character of length maximum 2 containing names of variables to produce facets. See examples.
tile.size, tile.color	A size and color passed to geom_tile.
...	Further arguments passed to geom_tile.

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.

Note

heatmapTCGA uses scale_fill_viridis from viridis package which is a port of the new matplotlib color maps (viridis - the default -, magma, plasma and inferno) to R. matplotlib http://matplotlib.org/ is a popular plotting library for python. These color maps are designed in such a way that they will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. They are also designed to be perceived by readers with the most common form of color blindness.

Author(s)

Marcin Kosinski, [e-mail]

See Also

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

Other RTCGA: RTCGA-package, boxplotTCGA, checkTCGA, convertTCGA, datasetsTCGA, downloadTCGA, expressionsTCGA, 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 = c("MET|4233", "ZNF500|26048", "ZNF501|115560")) %>%
	rename(cohort = dataset,
				 MET = `MET|4233`) %>%
	#cancer samples
	filter(substr(bcr_patient_barcode, 14, 15) == "01") %>%
	mutate(MET = cut(MET,
	 round(quantile(MET, probs = seq(0,1,0.25)), -2),
	 include.lowest = TRUE,
	 dig.lab = 5)) -> ACC_BLCA_BRCA_OV.rnaseq

ACC_BLCA_BRCA_OV.rnaseq %>%
	select(-bcr_patient_barcode) %>%
	group_by(cohort, MET) %>%
	summarise_each(funs(median)) %>%
	mutate(ZNF500 = round(`ZNF500|26048`),
				 ZNF501 = round(`ZNF501|115560`)) -> ACC_BLCA_BRCA_OV.rnaseq.medians
heatmapTCGA(ACC_BLCA_BRCA_OV.rnaseq.medians,
	"cohort", "MET", "ZNF500", title = "Heatmap of ZNF500 expression")

## 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(-bcr_patient_barcode, -Variant_Classification, -dataset, -Hugo_Symbol) %>% 
	group_by(cohort, MET, TP53) %>% 
	summarise_each(funs(median)) %>% 
	mutate(ZNF501 = round(`ZNF501|115560`)) -> ACC_BLCA_BRCA_OV.rnaseq_TP53mutations_ZNF501medians

heatmapTCGA(ACC_BLCA_BRCA_OV.rnaseq_TP53mutations_ZNF501medians, "cohort", "MET",
	fill = "ZNF501", facet.names = "TP53", title = "Heatmap of ZNF501 expression")
heatmapTCGA(ACC_BLCA_BRCA_OV.rnaseq_TP53mutations_ZNF501medians, "TP53", "MET",
	fill = "ZNF501", facet.names = "cohort", title = "Heatmap of ZNF501 expression")
heatmapTCGA(ACC_BLCA_BRCA_OV.rnaseq_TP53mutations_ZNF501medians, "TP53", "cohort",
	fill = "ZNF501", facet.names = "MET", title = "Heatmap of ZNF501 expression")

RTCGA documentation built on Nov. 8, 2020, 5:11 p.m.