rank.enrichment {epiNEM} | R Documentation

## Rank enrichment

### Description

Infers a signalling pathway from peerturbation experiments.

### Usage

    rank.enrichment(
      data,
      list,
      list2 = NULL,
      n = 1000,
      main = NULL,
      col1 = "RdBu",
      col2 = rgb(1, 0, 0, 0.75),
      col3 = rgb(0, 0, 1, 0.75),
      blim = NULL,
      p = NULL,
      lwd = 3,
      test = wilcox.test,
      vis = "matrix",
      verbose = FALSE,
      ...
    )

### Arguments

`data` | m times l matrix with m observed genes and l variables with numeric values to rank the genes
`list` | list of of vectors of genes
`list2` | optional list with same length as list
`n` | length of the gradient (maximum: m)
`main` | character string for main header; if NULL uses the column names of data by default
`col1` | color of the gradient
`col2` | color of the first list
`col3` | color of the second list2
`blim` | numeric vector of length two with the lower and upper bounds for the gradient
`p` | numeric adjustment (length four) of the left side of the gradient (low means more to the left, high more to the right) the right side of the enrichment lines and the top positions of the additional matrices in case of vis='matrices'
`lwd` | line width of the enrichment lines
`test` | test function for the enrichment p-value; must have input argument and output values same as perm.rank.test; e.g., wilcox.test or ks.test (here 'less' and 'greater' are switched!)
`vis` | method for visualisation: 'matrix' uses one matrix heatmap for; 'matrices' uses several matrices (experimental), 'colside' uses the colSideColors argument for the ticks of genes in list/list2 (can use a lot of memory; experimental)
`verbose` | if TRUE gives prints additional output
`...` | additional arguments for epiNEM::HeatmapOP

### Value

transitively closed matrix or graphNEL

### Author(s)

Martin Pirkl

### Examples

    data <- matrix(rnorm(100*2),100,2)
    rownames(data) <- 1:100
    colnames(data) <- LETTERS[1:2]
    list <- list(first = as.character(sample(1:100, 10)), second = as.character(sample(1:100, 20)))
    rank.enrichment(data,list)

[Package epiNEM version 1.16.0 Index]