RUVIIIC (version 1.0.19)

RUVIII_C_Varying_residual_dimension: Compute amount of replication, assuming varying controls

Description

Compute amount of replication, assuming varying controls

Usage

RUVIII_C_Varying_residual_dimension(Y, M, potentialControls)

Arguments

Y

The input data matrix. Must be a matrix, not a data.frame. It should contain missing (NA) values, rather than zeros.

M

The design matrix containing information about technical replicates. It should not contain an intercept term!

potentialControls

The names of the control variables which are known to be constant across the observations

Value

A vector of integers, one per column of Y.

Details

When applying RUV-III-C with varying controls, the amount of replication available for normalisation depends on the variable being normalised. The amount of replication is measured as the number of technical replicates in which a variable is measured (rows of the design matrix) minus the number of biological samples (columns of the design matrix) in which a variable is measured. This value is useful as a filtering criteria, because variables with a low amount of replication will be poorly normalised by the RUV family of methods.

Examples

# NOT RUN {
data(crossLab)
#Design matrix containing information about which runs are technical replicates of each other. 
#In this case, random pairings of mass-spec runs analysing the same sample, at different sites.
#Note that we specify no intercept term!
M <- model.matrix(~ grouping - 1, data = peptideData)
#Get out the list of peptides, both HEK (control) and peptides of interest.
peptides <- setdiff(colnames(peptideData), c("filename", "site", "mixture", "Date", "grouping"))
#Reduce the data matrix to only the peptide data
onlyPeptideData <- data.matrix(peptideData[, peptides])
#All the human peptides are potential controls. That is, everything that's not an SIS peptides.
potentialControls <- setdiff(peptides, sisPeptides)
#But we want to use controls that are *often* found
potentialControlsOftenFound <- names(which(apply(onlyPeptideData[, potentialControls], 2, 
    function(x) sum(is.na(x))) <= 10))
#Actually run correction
results <- RUVIII_C_Varying_residual_dimension(Y = log10(onlyPeptideData), M = M, 
    potentialControls = potentialControlsOftenFound)

# }