consensusScores {piano} | R Documentation

## Top consensus gene sets and boxplot

### Description

Calculates the consensus scores for the gene sets using multiple gene set analysis methods (with `runGSA()`). Optionally also produces a boxplot to visualize the results.

### Usage

    consensusScores(resList, class, direction, n = 50, adjusted = FALSE,
      method = "median", plot = TRUE, cexLabel = 0.8, cexLegend = 1,
      showLegend = TRUE, rowNames = "names", logScale = FALSE, main)

### Arguments

`resList` | a list where each element is an object of class `GSAres`, as returned by the `runGSA` function.
`class` | a character string determining the p-values of which directionality class that should be used as significance information for the plot. Can be one of `"distinct"`, `"mixed"`, `"non"`.
`direction` | a character string giving the direction of regulation, can be either `"up"` or `"down"`.
`n` | consensus rank cutoff. All gene sets with consensus rank (see details below) `<=n` will be included in the plot. Defaults to 50.
`adjusted` | a logical, whether to use adjusted p-values or not. Note that if `runGSA` was run with the argument `adjMethod="none"`, the adjusted p-values will be equal to the original p-values.
`method` | a character string selecting the method, either "mean", "median", "max", "Borda" or "Copeland".
`plot` | a logical, whether or not to draw the boxplot.
`cexLabel` | the x- and y-axis label sizes.
`cexLegend` | the legend text size.
`showLegend` | a logical, whether or not to show the legend and the indivual method ranks as points in the plot.
`rowNames` | a character string determining which rownames to use, set to either `"ranks"` for the consensus rank, `"names"` for the gene set names, or `"none"` to omit rownames.
`logScale` | a logical, whether or not to use log-scale for the x-axis.
`main` | a character vector giving an alternative title of the plot.

### Details

Based on the results given by the elements of `resList`, preferably representing similar runs with `runGSA` but with different methods, this function ranks the gene sets for each `GSAres` object, based on the selected directionality class. Next, the median rank for each gene set is taken as a score for top-ranking gene sets. The highest scoring gene-sets (with consensus rank, i.e. `rank(rankScore,ties.method="min")`, smaller or equal to `n`) are selected and depicted in a boxplot, showing the distribution of individual ranks (shown as colored points), as well as the median rank (shown as a red line). As an alternative of using the median rank as consensus score, it is possible to choose the mean or using the Borda or Copeland method, through the `method` argument. A more conservative approach can also be taken using the maximum rank as a consensus score, prioritizing gene-sets that are consistently ranked high across all GSA runs.

All elements of `resList` have to be objects containing results for the same number of gene-sets. The ranking procedure handles ties by giving them their minimum rank.

### Value

A list containing a matrix of the ranks for the top `n` gene sets, given by each run, as well as the corresponding matrix of p-values, given by each run.

### Author(s)

Leif Varemo piano.rpkg@gmail.com and Intawat Nookaew piano.rpkg@gmail.com

### See Also

piano, `runGSA`

### Examples

       # Load some example GSA results:
       data(gsa_results)

       # Consensus scores for the top 50 gene sets (in the non-directional class):
       cs <- consensusScores(resList=gsa_results,class="non")

       # Access the ranks given to gene set s7 by each individual method:
       cs$rankMat["s7",]

[Package piano version 2.6.0 Index]