TCGAvisualize_Heatmap {TCGAbiolinks} | R Documentation

## 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)

[Package TCGAbiolinks version 2.18.0 Index]