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Last data update: 2014.03.03

R: Create Heatmaps for TCGA Datasets

heatmapTCGA

R Documentation

Create Heatmaps for TCGA Datasets

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, m.p.kosinski@gmail.com

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")

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/heatmapTCGA.Rd_%03d_medium.png", width=480, height=480)
> ### Name: heatmapTCGA
> ### Title: Create Heatmaps for TCGA Datasets
> ### Aliases: heatmapTCGA
> 
> ### ** 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 = 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")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>