heatmapTCGA | R Documentation |
Function creates heatmaps (geom_tile) for TCGA Datasets.
heatmapTCGA( data, x, y, fill, legend.title = "Expression", legend = "right", title = "Heatmap of expression", facet.names = NULL, tile.size = 0.1, tile.color = "white", ... )
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. |
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.
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
https://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.
Marcin Kosinski, m.p.kosinski@gmail.com
RTCGA website http://rtcga.github.io/RTCGA/articles/Visualizations.html.
Other RTCGA:
RTCGA-package
,
boxplotTCGA()
,
checkTCGA()
,
convertTCGA()
,
datasetsTCGA
,
downloadTCGA()
,
expressionsTCGA()
,
infoTCGA()
,
installTCGA()
,
kmTCGA()
,
mutationsTCGA()
,
pcaTCGA()
,
readTCGA()
,
survivalTCGA()
,
theme_RTCGA()
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")
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