plotCoexpression {GeneRegionScan} | R Documentation

## Plot Coexpression of probes in a ProbeLevelSet

### Description

Function that will investigate all possible pairings in a set of probes, calculate the Pearson correlation coefficient and plot them in a meaningful way

### Usage

        plotCoexpression(object, gene, probeData=NULL, verbose=TRUE, directions="all", correlationCutoff=0.5,
        probeLevelInfo=c("probeid"))

### Arguments

`object` | A ProbeLevelSet object or a regular ExpressionSet object (in which case a probeData argument is required). See getLocalProbeIntensities and related functions on how to create a ProbeLevelSet.
`gene` | A number of gene sequences as DNAstring, DNAStringSets or character vectors with sequence.
`probeData` | Optional if a ProbeLevelSet is submitted as object argument. Otherwise it must be a data frame with rownames corresponding to the featureNames of the ExpressionSet and a column named "sequence" with the probe sequences as character strings
`verbose` | TRUE or FALSE
`directions` | A character vector of the matching-directions that should be scanned (which combinations of complementary and reverse). Defaults to "all" which is shorthand for all possible directions, but can take anything from: c("matchForwardSense", "matchForwardAntisense", "matchReverseSense", "matchReverseAntisense")
`correlationCutoff` | A number between 0 and 1. The limit at which Pearson correlation (in absolute values) should not be plotted below. Defaults to 0.5
`probeLevelInfo` | The information about each probe to include in the plot. Should be a vector of one or more of the following elements: probeid, probesetid, sequence. Default is only probeid.

### Details

This function takes a ProbeLevelSet or an ExpressionSet + probeData and the sequence of a gene. It then calculates pairwise Pearson correlation coefficients between all possible combinations of probes. Then it assigns each probe to a location along the length of the gene and plots a relational graph showing which probes has high correlation coefficients. The correlation coefficients are sorted by absolute values meaning that it will also include the negative correlations.

### Value

No value, but plots a hapmap style plot of correlation values between all probes

### Author(s)

Lasse Folkersen

### See Also

`geneRegionScan`, `plotOnGene`

### Examples

    	data(exampleProbeLevelSet)
        plotCoexpression(exampleProbeLevelSet, mrna, correlationCutoff=0.7, probeLevelInfo=c("probeid","sequence"))

[Package GeneRegionScan version 1.50.0 Index]