gapsInterPattern: `gapsInterPattern` calculates statistics for measuring the distance between patterns based on genes associated with the patterns

Description

gapsInterPattern calculates the overlap in significant genes between patterns according to the set standard deviation threshold from the mean. Returns the names of significant genes, a matrix of overlap fractions, and the mean of this matrix. A warning is issued if a column of ${\bf{A}}$ does not have significant genes at a given threshold.

Usage

gapsInterPattern(Amean, Asd, sdThreshold = 3)

Arguments

Amean

Sampled mean value of the amplitude matrix ${\bf{A}}$ from a run of CoGAPS.

Asd

Sampled standard deviation of the amplitude matrix ${\bf{A}}$ from a run of CoGAPS.

sdThreshold

How many standard deviations a gene's sampled mean needs to be above 0 to be considered significantly expressed in a pattern.

Value

SignificantGeneNames: A list in which each element holds a vector of the name of the significant genes in each column of ${\bf{A}}.$
SignificantGeneTotals: A vector of significant genes in each respective column.
SeparationMatrix: The matrix containing the overlap fractions for the significant genes in each column of ${\bf{A}}$.
InterPatternValue: The final value of the Interpattern measure.

Details

This calculates a statistic for comparing different patterns based on the genes associated with them.

Examples

Run this code

## Not run: 
# ## Load data
# data('SimpSim')
# 
# ## Run GAPS matrix decomposition
# nIter <- 10
# results <- gapsTestRun(SimpSim.D, SimpSim.S, nFactor=3,
#                    nEquil=nIter, nSample=nIter)
# 				   
# ## Execute the InterPattern function
# InterPatternStats <- gapsInterPattern(results$Amean, results$Asd, sdThreshold = 3)
# ## End(Not run)