heatmapTCGA: Create Heatmaps for TCGA Datasets

In RTCGA/RTCGA: The Cancer Genome Atlas Data Integration

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 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.

Author(s)

Marcin Kosinski, [e-mail]

See Also

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()

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/RTCGA documentation built on Nov. 1, 2022, 8:15 p.m.