This function checks data for missing entries and returns a list of genes that have non-zero variance in all sets and pass two criteria on maximum number of missing values in each given set: the fraction of missing values must be below a given threshold and the total number of missing samples must be below a given threshold. If weights are given, entries whose relative weight is below a threshold will be considered missing.
goodGenesMS(
multiExpr,
multiWeights = NULL,
useSamples = NULL,
useGenes = NULL,
minFraction = 1/2,
minNSamples = ..minNSamples,
minNGenes = ..minNGenes,
tol = NULL,
minRelativeWeight = 0.1,
verbose = 1, indent = 0)A logical vector with one entry per gene that is TRUE if the gene is considered good and
FALSE otherwise. Note that all genes excluded by useGenes are automatically assigned
FALSE.
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 samples and columns to genes or probes.
optional observation weights in the same format (and dimensions) as multiExpr.
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 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. 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
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
The constants ..minNSamples and ..minNGenes are both set to the value 4.
If weights are given, entries whose relative weight (i.e., weight divided by maximum weight in the column or gene) will be considered missing.
For most data sets, the fraction of missing samples criterion will be much more stringent than the absolute number of missing samples criterion.
goodGenes, goodSamples, goodSamplesGenes for cleaning
individual sets separately;
goodSamplesMS, goodSamplesGenesMS for additional cleaning of multiple data
sets together.