goodSamples: Filter samples with too many missing entries

Description Usage Arguments Details Value Author(s) See Also

View source: R/Functions.R

Description

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.

Usage

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goodSamples(
  datExpr, 
  weights = NULL,
  useSamples = NULL, 
  useGenes = NULL, 
  minFraction = 1/2, 
  minNSamples = ..minNSamples, 
  minNGenes = ..minNGenes, 
  minRelativeWeight = 0.1,
  verbose = 1, indent = 0)

Arguments

datExpr

expression data. A data frame in which columns are genes and rows ar samples.

weights

optional observation weights in the same format (and dimensions) as datExpr.

useSamples

optional specifications of which samples to use for the check. Should be a logical vector; samples whose entries are FALSE will be ignored for the missing value counts. Defaults to using all samples.

useGenes

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.

minFraction

minimum fraction of non-missing samples for a gene to be considered good.

minNSamples

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.

minNGenes

minimum number of non-missing samples for a sample to be considered good.

minRelativeWeight

observations whose weight divided by the maximum weight is below this threshold will be considered missing.

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

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 sample that is TRUE if the sample is considered good and FALSE otherwise. Note that all samples excluded by useSamples are automatically assigned FALSE.

Author(s)

Peter Langfelder and Steve Horvath

See Also

goodSamples, goodSamplesGenes


WGCNA documentation built on Oct. 24, 2018, 5:04 p.m.