Package 'WGCNA' - Laboratory Web Sites - UCLA

50 goodSamples
useGenes
minFraction
minNSamples
minNGenes
verbose
indent
optional specifications of genes for which to perform the check. Should be a
logical vector; genes whose entries are FALSE will be ignored. Defaults to
using all genes.
minimum fraction of non-missing samples for a gene to be considered good.
minimum number of good samples for the data set to be considered fit for analysis.
If the actual number of good samples falls below this threshold, an error
will be issued.
minimum number of non-missing samples for a sample to be considered good.
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.
Details
Value
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.
A list with one component per input set. Each component is a logical vector with one entry per
sample in the corresponding set, indicating whether the sample passed the missing value criteria.
Author(s)
See Also
Peter Langfelder and Steve Horvath
goodGenes, goodSamples, goodSamplesGenes for cleaning individual sets separately;
goodGenesMS, goodSamplesGenesMS for additional cleaning of multiple data sets together.
goodSamples
Filter samples with too many missing entries
Description
Usage
This function checks data for missing entries and returns a list of samples that pass two criteria on
maximum number of missing values: the fraction of missing values must be below a given threshold
and the total number of missing genes must be below a given threshold.
goodSamples(datExpr,
useSamples = NULL,
useGenes = NULL,
minFraction = 1/2,
minNSamples = ..minNSamples,
minNGenes = ..minNGenes,
verbose = 1, indent = 0)