Usage
collapseRows(datET, symbolVector, probeVector,
method="maxRowVariance",
connectivityBasedCollapsing=TRUE,
methodFunction=NULL,
connectivityPower=1,
selectFewestMissing=TRUE)Arguments
datET
Matrix (or data frame) of numeric values (typically expression data) where rows are probes and columns are samples.
symbolVector
A character vector of gene symbols which correspond to the probes in "probeVector"
probeVector
A character vector of probes. This should include all the probes from rownames(datET), but can include other probes.
method
Built-in options for possible methods for determining the "best" probe to keep. These are the options:
"maxRowVariance" (default) = choose the probe with the highest variance;
"Max" / "Min" = choose the probe with the highest / lowest mean expression lev
connectivityBasedCollapsing
If TRUE (default), genes with 3+ corresponding probes will be collapsed such that values corresponding to the probe with the highest connectivity (signed adjacency raised to a power of connectivityPower) will be used.
methodFunction
Should only be set if method="function". Must be a function that takes a Nr x Nc matrix of numbers
as input and outputs a vector with the length Nc (ie, colMeans). This will then be the method used for
collapsing values for multiple probes
connectivityPower
Only used if connectivityBasedCollapsing=TRUE (see connectivityBasedCollapsing parameter). Value must be a number > 0.
selectFewestMissing
If TRUE (default), the input expression matrix is trimmed such that only the probes for each gene with the
fewest number of missing probes are retained. In situations where an equal number of probes are missing (or
where there is no missing data), all pr