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.
goodSamplesGenes(
datExpr,
weights = NULL,
minFraction = 1/2,
minNSamples = ..minNSamples,
minNGenes = ..minNGenes,
tol = NULL,
minRelativeWeight = 0.1,
verbose = 1, indent = 0)
A list with the foolowing components:
A logical vector with one entry per sample that is TRUE if the sample is
considered good and FALSE otherwise.
A logical vector with one entry per gene that is TRUE if the gene is
considered good and FALSE otherwise.
expression data. A matrix or data frame in which columns are genes and rows ar samples.
optional observation weights in the same format (and dimensions) as datExpr.
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.
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'.
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).
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.
Peter Langfelder
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.
goodSamples, goodGenes