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,
weights = NULL,
useSamples = NULL,
useGenes = NULL,
minFraction = 1/2,
minNSamples = ..minNSamples,
minNGenes = ..minNGenes,
minRelativeWeight = 0.1,
verbose = 1, indent = 0)expression data. A data frame in which columns are genes and rows ar samples.
optional observation weights in the same format (and dimensions) as datExpr.
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.
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.
observations whose weight divided by the maximum weight is below this threshold will be considered missing.
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.
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.
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.