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collapseRowsUsingKME

Selects one representative row per group based on kME


Description

This function selects only the most informative probe for each gene in a kME table, only keeping the probe which has the highest kME with respect to any module in the module membership matrix. This function is a special case of the function collapseRows.

Usage

collapseRowsUsingKME(MM, Gin, Pin = NULL, kMEcols = 1:dim(MM)[2])

Arguments

MM

A module membership (kME) table with at least a subset of the columns corresponding to kME values.

Gin

Genes labels in a 1 to 1 correspondence with the rows of MM.

Pin

If NULL (default), rownames of MM are assumed to be probe IDs. If entered, Pin must be the same length as Gin and correspond to probe IDs for MM.

kMEcols

A numeric vector showing which columns in MM correspond to kME values. The default is all of them.

Value

datETcollapsed

A numeric matrix with the same columns as the input matrix MM, but with rows corresponding to the genes rather than the probes.

group2row

A matrix whose rows correspond to the unique gene labels and whose 2 columns report which gene label (first column called group) is represented by what probe (second column called selectedRowID)

selectedRow

A logical vector whose components are TRUE for probes selected as representatives and FALSE otherwise. It has the same length as the vector Pin.

Author(s)

Jeremy Miller

See Also

Examples

# Example: first simulate some data
set.seed(100)
ME.A = sample(1:100,50);  ME.B = sample(1:100,50)
ME.C = sample(1:100,50);  ME.D = sample(1:100,50)  
ME1     = data.frame(ME.A, ME.B, ME.C, ME.D)
simDatA = simulateDatExpr(ME1,1000,c(0.2,0.1,0.08,0.05,0.3), signed=TRUE)
simDatB = simulateDatExpr(ME1,1000,c(0.2,0.1,0.08,0.05,0.3), signed=TRUE)
Gin     = c(colnames(simDatA$datExpr),colnames(simDatB$datExpr))
Pin     = paste("Probe",1:length(Gin),sep=".")
datExpr = cbind(simDatA$datExpr, simDatB$datExpr)
MM      = corAndPvalue(datExpr,ME1)$cor

# Now run the function and see some example output
results = collapseRowsUsingKME(MM, Gin, Pin)
head(results$MMcollapsed)
head(results$group2Row)
head(results$selectedRow)

WGCNA

Weighted Correlation Network Analysis

v1.70-3
GPL (>= 2)
Authors
Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath <SHorvath@mednet.ucla.edu> with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang
Initial release
2021-02-17

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