plotRegTarExpr {RegEnrich} | R Documentation

## Plot regulator and its targets expression

### Description

Plot regulator and its targets expression

### Usage

    plotRegTarExpr(
      object,
      reg,
      n = 1000,
      scale = TRUE,
      tarCol = "black",
      tarColAlpha = 0.1,
      regCol = "#ffaa00",
      xlab = "Samples",
      ylab = "Z-scores",
      ...
    )

### Arguments

`object` | a RegenrichSet object, to which at least `regenrich_diffExpr` and `regenrich_network` functions have been applied.
`reg` | a regulator to plot.
`n` | the maximun number of targets to plot.
`scale` | logical, whether gene expression is z-score normalized.
`tarCol` | the color of the lines for the targets of the regulator.
`tarColAlpha` | numeric, ranging from 0 to 1, indicating transparancy of target lines.
`regCol` | the color of the line for the 'reg'.
`xlab` | x label of plot.
`ylab` | y label of plot.
`...` | other parameters in `ggplot` function.

### Value

a ggplot object.

### Examples

    # constructing a RegenrichSet object
    colData = data.frame(patientID = paste0('Sample_', seq(50)),
                         week = rep(c('0', '1'), each = 25),
                         row.names = paste0('Sample_', seq(50)),
                         stringsAsFactors = TRUE)
    design = ~week
    reduced = ~1
    set.seed(123)
    cnts = matrix(as.integer(rnbinom(n=1000*50, mu=100, size=1/0.1)), ncol=50,
                  dimnames = list(paste0('gene', seq(1000)), rownames(colData)))

    cnts[5,26:50] = cnts[5,26:50] + 50L # add reads to gene5 in some samples.
    id = sample(31:1000, 20) # randomly select 20 rows, and assign reads.
    cnts[id,] = vapply(cnts[5,], function(x){
      as.integer(rnbinom(n = 20, size = 1/0.02, mu = x))},
      FUN.VALUE = rep(1L, 20))

    object = RegenrichSet(expr = cnts,
                          colData = colData,
                          method = 'LRT_DESeq2', minMeanExpr = 0,
                          design = design, reduced = reduced, fitType = 'local',
                          networkConstruction = 'COEN',
                          enrichTest = 'FET',
                          reg = paste0('gene', seq(30)))

    ## RegEnrich analysis
    object = regenrich_diffExpr(object)

    # Set a random softPower, otherwise it is difficult to achive a
    # scale-free network because of a randomly generated count data.
    object = regenrich_network(object, softPower = 3)
    object = regenrich_enrich(object)
    object = regenrich_rankScore(object)

    ## plot expression of a regulator and its targets.
    plotRegTarExpr(object, reg = 'gene5')
    plotRegTarExpr(object, reg = 'gene27')

[Package RegEnrich version 1.0.1 Index]