TCGAvisualize_Heatmap {TCGAbiolinks} | R Documentation |
Heatmap with more sensible behavior using heatmap.plus
TCGAvisualize_Heatmap(data, col.metadata, row.metadata, col.colors = NULL, row.colors = NULL, show_column_names = FALSE, show_row_names = FALSE, cluster_rows = FALSE, cluster_columns = FALSE, sortCol, extrems = NULL, rownames.size = 12, title = NULL, color.levels = NULL, values.label = NULL, filename = "heatmap.pdf", width = 10, height = 10, type = "expression", scale = "none", heatmap.legend.color.bar = "continuous")
data |
The object to with the heatmap data (expression, methylation) |
col.metadata |
Metadata for the columns (samples). It should have on of the following columns: barcode (28 characters) column to match with the samples. It will also work with "bcr_patient_barcode"(12 chars),"patient"(12 chars),"sample"(16 chars) columns but as one patient might have more than one sample, this coul lead to errors in the annotation. The code will throw a warning in case two samples are from the same patient. |
row.metadata |
Metadata for the rows genes (expression) or probes (methylation) |
col.colors |
A list of names colors |
row.colors |
A list of named colors |
show_column_names |
Show column names names? Dafault: FALSE |
show_row_names |
Show row names? Dafault: FALSE |
cluster_rows |
Cluster rows ? Dafault: FALSE |
cluster_columns |
Cluster columns ? Dafault: FALSE |
sortCol |
Name of the column to be used to sort the columns |
extrems |
Extrems of colors (vector of 3 values) |
rownames.size |
Rownames size |
title |
Title of the plot |
color.levels |
A vector with the colors (low level, middle level, high level) |
values.label |
Text of the levels in the heatmap |
filename |
Filename to save the heatmap. Default: heatmap.png |
width |
figure width |
height |
figure height |
type |
Select the colors of the heatmap values. Possible values are "expression" (default), "methylation" |
scale |
Use z-score to make the heatmap? If we want to show differences between genes, it is good to make Z-score by samples (force each sample to have zero mean and standard deviation=1). If we want to show differences between samples, it is good to make Z-score by genes (force each gene to have zero mean and standard deviation=1). Possibilities: "row", "col". Default "none" |
heatmap.legend.color.bar |
Heatmap legends values type. Options: "continuous", "disctrete |
Heatmap plotted in the device
row.mdat <- matrix(c("FALSE","FALSE", "TRUE","TRUE", "FALSE","FALSE", "TRUE","FALSE", "FALSE","TRUE" ), nrow = 5, ncol = 2, byrow = TRUE, dimnames = list( c("probe1", "probe2","probe3","probe4","probe5"), c("duplicated", "Enhancer region"))) dat <- matrix(c(0.3,0.2,0.3,1,1,0.1,1,1,0, 0.8,1,0.7,0.7,0.3,1), nrow = 5, ncol = 3, byrow = TRUE, dimnames = list( c("probe1", "probe2","probe3","probe4","probe5"), c("TCGA-DU-6410", "TCGA-DU-A5TS", "TCGA-HT-7688"))) mdat <- data.frame(patient=c("TCGA-DU-6410","TCGA-DU-A5TS","TCGA-HT-7688"), Sex=c("Male","Female","Male"), COCCluster=c("coc1","coc1","coc1"), IDHtype=c("IDHwt","IDHMut-cod","IDHMut-noncod")) TCGAvisualize_Heatmap(dat, col.metadata = mdat, row.metadata = row.mdat, row.colors = list(duplicated = c("FALSE" = "pink", "TRUE"="green"), "Enhancer region" = c("FALSE" = "purple", "TRUE"="grey")), col.colors = list(Sex = c("Male" = "blue", "Female"="red"), COCCluster=c("coc1"="grey"), IDHtype=c("IDHwt"="cyan", "IDHMut-cod"="tomato" ,"IDHMut-noncod"="gold")), type = "methylation", show_row_names=TRUE) if (!(is.null(dev.list()["RStudioGD"]))){dev.off()}