goodSamplesGenes: Iterative filtering of samples and genes with too many...

In WGCNA: Weighted Correlation Network Analysis

Iterative filtering of samples and genes with too many missing entries

Description

This function checks data for missing entries, entries with weights below a threshold, and zero-variance genes, and returns a list of samples and genes that pass criteria on maximum number of missing or low weight values. If necessary, the filtering is iterated.

Usage

goodSamplesGenes(
  datExpr, 
  weights = NULL,
  minFraction = 1/2, 
  minNSamples = ..minNSamples, 
  minNGenes = ..minNGenes, 
  tol = NULL,
  minRelativeWeight = 0.1,
  verbose = 1, indent = 0)

Arguments

datExpr	expression data. A matrix or data frame in which columns are genes and rows ar samples.
weights	optional observation weights in the same format (and dimensions) as datExpr.
minFraction	minimum fraction of non-missing samples for a gene to be considered good.
minNSamples	minimum number of non-missing samples for a gene to be considered good.
minNGenes	minimum number of good genes for the data set to be considered fit for analysis. If the actual number of good genes falls below this threshold, an error will be issued.
tol	an optional 'small' number to compare the variance against. Defaults to the square of 1e-10 * max(abs(datExpr), na.rm = TRUE). The reason of comparing the variance to this number, rather than zero, is that the fast way of computing variance used by this function sometimes causes small numerical overflow errors which make variance of constant vectors slightly non-zero; comparing the variance to tol rather than zero prevents the retaining of such genes as 'good genes'.
minRelativeWeight	observations whose relative weight is below this threshold will be considered missing. Here relative weight is weight divided by the maximum weight in the column (gene).
verbose	integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose.
indent	indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.

Details

This function iteratively identifies samples and genes with too many missing entries and genes with zero variance. If weights are given, entries with relative weight (weight divided by maximum weight in the column) below minRelativeWeight will be considered missing. The process is repeated until the lists of good samples and genes are stable. The constants ..minNSamples and ..minNGenes are both set to the value 4.

Value

A list with the foolowing components:

goodSamples	A logical vector with one entry per sample that is TRUE if the sample is considered good and FALSE otherwise.
goodGenes	A logical vector with one entry per gene that is TRUE if the gene is considered good and FALSE otherwise.

Author(s)

Peter Langfelder

See Also

goodSamples, goodGenes

WGCNA documentation built on Sept. 18, 2024, 5:08 p.m.