Package 'WGCNA' - Laboratory Web Sites - UCLA

44 goodSamplesGenesMS
Details
The constants ..minNSamples and ..minNGenes are both set to the value 4. For most data sets,
the fraction of missing samples criterion will be much more stringent than the absolute number of
missing samples criterion.
Value
A logical vector with one entry per gene that is TRUE if the gene is considered good and FALSE
otherwise. Note that all genes excluded by useGenes are automatically assigned FALSE.
Author(s)
Peter Langfelder and Steve Horvath
See Also
goodSamples, goodSamplesGenes
goodSamplesGenesMS Iterative filtering of samples and genes with too many missing entries
across multiple data sets
Description
This function checks data for missing entries and zero variance across multiple data sets and returns
a list of samples and genes that pass criteria maximum number of missing values. If necessary, the
filtering is iterated.
Usage
goodSamplesGenesMS(
multiExpr,
minFraction = 1/2,
minNSamples = ..minNSamples,
minNGenes = ..minNGenes,
verbose = 2, indent = 0)
Arguments
multiExpr
minFraction
minNSamples
minNGenes
verbose
indent
expression data in the multi-set format (see checkSets). A vector of lists, one
per set. Each set must contain a component data that contains the expression
data, with rows corresponding to samples and columns to genes or probes.
minimum fraction of non-missing samples for a gene to be considered good.
minimum number of non-missing samples for a gene to be considered good.
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.
integer level of verbosity. Zero means silent, higher values make the output
progressively more and more verbose.
indentation for diagnostic messages. Zero means no indentation, each unit adds
two spaces.