RN_calc function

RN_calc

Computes both global and local p-values, and returns the results in a list containing for each gene the original expression values and the associated global and local p-values (as -log10(p-value)).

RN_calc(X, design)

Arguments

• X: data.frame with expression values. It may contain additional non numeric columns (eg. a column with gene names).
• design: The RxC design matrix where R (rows) corresponds to the number of numeric columns (samples) in 'file' and C (columns) to the number of conditions. It must be a binary matrix with one and only one '1' for every row, corresponding to the condition (column) for which the sample corresponding to the row has to be considered a biological ot technical replicate. See the example 'RN_Brain_Example_design' for the design matrix of 'RN_Brain_Example_tpm' which has three replicates for three conditions (three rows) for a total of nine samples (nine rows). design defaults to a square matrix of independent samples (diagonal = 1, everything else = 0)

Returns

• gpv: -log10 of the global p-values

• lpv: -log10 of the local p-values

• c_like: results formatted as in the output of the C++ implementation of RNentropy.

• res: The results data.frame with the original expression values and the associated -log10 of global and local p-values.

• design: the experimental design matrix

Author(s)

Giulio Pavesi - Dep. of Biosciences, University of Milan

Federico Zambelli - Dep. of Biosciences, University of Milan

Examples

data("RN_Brain_Example_tpm", "RN_Brain_Example_design")
#compute statistics and p-values (considering only a subset of genes due to
#examples running time limit of CRAN)
Results <- RN_calc(RN_Brain_Example_tpm[1:10000,], RN_Brain_Example_design)

## The function is currently defined as

function(X, design = NULL)
{
  if(is.null(design))
  {
    design <- .RN_default_design(sum(sapply(X, is.numeric)))
  }
  
        Results <- list(expr = X, design = design)

        GPV <- RN_calc_GPV(X, bind = FALSE)
        LPV <- RN_calc_LPV(X, design = design, bind = FALSE)    

        TABLE = cbind(X,'---',GPV,'---',LPV)    

        Results$gpv <- GPV
        Results$lpv <- LPV
        Results$c_like <- TABLE
        Results$res <- cbind(X, GPV, LPV)
        
        return(Results)
}