RNentropy (version 1.2.3)

RN_calc: RN_calc

Description

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)).

Usage

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)

Value

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

Examples

# NOT RUN {
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)
}
# }