TCGAbiolinks (version 1.2.5)

TCGAvisualize_Heatmap: Heatmap with more sensible behavior using heatmap.plus

Description

Heatmap with more sensible behavior using heatmap.plus

Usage

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, title, type = "expression", scale = "none", heatmap.legend.color.bar = "continuous")

Arguments

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

title

Title of the plot

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

Value

Heatmap plotted in the device

Examples

 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()}