plot.pigengene {Pigengene} | R Documentation

## Plots and saves a `pigengene` object

### Description

Plots a couple of heatmaps of expression of the eigengenes, weights (memberships), and so on. Saves the plots in png format.

### Usage

    ## S3 method for class 'pigengene'
    plot(x, saveDir = NULL,
      DiseaseColors="Auto",
      fontsize = 35, doShowColnames = TRUE, fontsizeCol = 25,
      doClusterCols = ncol(pigengene$eigengenes) > 1,
      verbose = 2, doShowRownames = "Auto",
      pngfactor = max(2, ncol(pigengene$eigengenes)/16), do0Mem = FALSE, ...)

### Arguments

`x` | The object from `pigengene-class` computed by `compute.pigengene`.
`saveDir` | The dirctory for saving the plots
`DiseaseColors` | A vector of characters determining color for each disease. Names should match the values in the first column of `x$annotation`.
`fontsize` | Passd to `pheatmap.type`
`doShowColnames` | Boolean
`fontsizeCol` | Numeric
`doClusterCols` | Boolean
`verbose` | The integer level of verbosity. 0 means silent and higher values produce more details of computation.
`doShowRownames` | Boolean
`pngfactor` | A numeric adjusting the size of the png files
`do0Mem` | If `TRUE`, module 0 genes are included in the membership heatmap.
`...` | Passd to `pheatmap.type` function

### Details

Many of the arguments are passed to `pheatmap`.

### Value

A list of:

`heat` | The full output of `pheatmap` functionion
`heatNotRows` | The full output of `pheatmap.type` function

### Author(s)

Habil Zare ad Amir Foroushani

### References

Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia, Foroushani A, Agrahari R, Docking R, Karsan A, and Zare H. In preparation.

### See Also

`Pigengene-package`, `compute.pigengene`, `pheatmap.type`

### Examples

         ## Data:
         data(aml)
         data(mds)
         data(eigengenes33)
         d1 <- rbind(aml,mds)
         Labels <- c(rep("AML",nrow(aml)),rep("MDS",nrow(mds)))
         names(Labels) <- rownames(d1)
         Labels <- c(rep("AML",nrow(eigengenes33$aml)),rep("MDS",nrow(eigengenes33$mds)))
         names(Labels) <- rownames(d1)
         toyModules <- eigengenes33$modules[colnames(d1)]
         ## Computing:
         p1 <- compute.pigengene(Data=d1, Labels=Labels, modules=toyModules,
            saveFile="pigengene.RData", doPlot=TRUE, verbose=3)
         plot(p1,saveDir="plots")

[Package Pigengene version 1.16.0 Index]