RN_pmi: Compute point mutual information matrix for the experimental...

In RNentropy: Entropy Based Method for the Detection of Significant Variation in Gene Expression Data

Compute point mutual information matrix for the experimental conditions.

Description

Compute point mutual information for experimental conditions from the overxexpressed genes identified by RN_select.

Usage

RN_pmi(Results)

Arguments

Results

The output of RNentropy, RN_calc or RN_select. If RN_select has not already run on the results it will be invoked by RN_pmi using default arguments.

Value

The original input containing

gpv	-log10 of the Global p-values
lpv	-log10 of the Local p-values
c_like	a table similar to the one you obtain running the C++ implementation of RNentropy.
res	The results data.frame containing the original expression values together with the -log10 of Global and Local p-values.
design	The experimental design matrix.
selected	Transcripts/genes with a corrected Global p-value lower than gpv_t. Each condition N gets a condition_N column which values can be -1,0,1 or NA. 1 means that all the replicates of this condition seems to be consistently over-expressed w.r.t the overall expression of the transcript in all the conditions (that is, all the replicates of condition N have a positive Local p-value <= lpv_t). -1 means that all the replicates of this condition seems to be consistently under-expressed w.r.t the overall expression of the transcript in all the conditions (that is, all the replicates of condition N have a negative Local p-value and abs(Local p-values) <= lpv_t). 0 means that one or more replicates have an abs(Local p-value) > lpv_t. NA means that the Local p-values of the replicates are not consistent for this condition.

And two new matrices:

pmi	Point mutual information matrix of the conditions.
npmi	Normalized point mutual information matrix of the conditions.

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)
Results <- RN_select(Results)
Results <- RN_pmi(Results)

## The function is currently defined as
RN_pmi <- function(Results)
{
  if(is.null(Results$selected))
    Results <- RN_select(Results)
  
  Results$pmi <- matrix(nrow = ncol(Results$design), 
                        ncol = ncol(Results$design))
  
  colnames(Results$pmi) <- colnames(Results$design)
  rownames(Results$pmi) <- colnames(Results$design)
  
  Results$npmi <- Results$pmi
  
  colshift <- ncol(Results$selected) - ncol(Results$design) 
  
  for(x in 1:nrow(Results$pmi))
  {
    for(y in 1:nrow(Results$pmi))
    {
      if(x > y)
      {
        Results$pmi[x,y] <- Results$pmi[y,x]
        Results$npmi[x,y] <- Results$npmi[y,x]
        next
      } else
      {
        sum_x <- sum(Results$selected[,x+colshift] == 1, na.rm = TRUE)
        sum_y <- sum(Results$selected[,y+colshift] == 1, na.rm = TRUE)
        sum_xy <- sum(Results$selected[,x+colshift] == 1 & 
                        Results$selected[,y+colshift] == 1, na.rm = TRUE)
        freq_x <- sum_x / nrow(Results$selected)
        freq_y <- sum_y / nrow(Results$selected)
        freq_xy <- sum_xy / nrow(Results$selected)
        
        h_xy <- log2(1/freq_xy)
        
        Results$pmi[x,y] <- log2(freq_xy / (freq_x * freq_y))
        Results$npmi[x,y] <- Results$pmi[x,y] / h_xy
      }
    }
  }
  
  return (Results)
  }

RNentropy documentation built on April 13, 2022, 5:22 p.m.