WGCNA (version 1.49)

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,
  tol = NULL,
  verbose = 2, indent = 0)

Arguments

multiExpr

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 s

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. For each set in multiExpr, the default value is 1e-10 * max(abs(multiExpr[[set]]$data), na.rm = TRUE). The reason of comparing the variance to this number, rather than

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.

Value

• A list with the foolowing components:
• goodSamples
  A list with one component per given set. Each component is a logical vector with one entry per sample in the corresponding set 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.

Details

This function iteratively identifies samples and genes with too many missing entries, and genes with zero variance. Iterations may be required since excluding samples effectively changes criteria on genes and vice versa. 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.

See Also

goodGenes, goodSamples, goodSamplesGenes for cleaning individual sets separately;

goodSamplesMS, goodGenesMS for additional cleaning of multiple data sets together.