TCGAvisualize_Heatmap: Heatmap with more sensible behavior using heatmap.plus

In BioinformaticsFMRP/TCGAbiolinks: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

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,
  extremes = 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"
)

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? Default: FALSE
show_row_names	Show row names? Default: FALSE
cluster_rows	Cluster rows ? Default: FALSE
cluster_columns	Cluster columns ? Default: FALSE
sortCol	Name of the column to be used to sort the columns
extremes	Extremes 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", "discrete"

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)

BioinformaticsFMRP/TCGAbiolinks documentation built on April 12, 2024, 2:08 a.m.