mainNetFunction: Main Estimation and Evaluation Function

Description

A function that computes the mutual information between all pairs of rows (or specified ones) of matrix counts using all the 10 different estimation methods and evalute their performances.

Usage

mainNetFunction(counts, adjMat, nchips, plotPath = "", tfList = NULL)

Arguments

counts

a numeric matrix (for the reconstruction of gene regulatory networks, genes on rows and samples on columns).

adjMat

the adjacency matrix that encodes the graph structure that is going to be predicted.

nchips

the number of cpu's to be used for making the parallel calculation.

plotPath

the folder in which the plot will be saved.

tfList

the character vector specifying which genes from the rownames of the counts matrix is to be used as transcription factor for network reconstruction.

Value

miEst: a list containing the estimates of all methods.
valMet: a list contatining the performance indices (i.e. "Recall", "FPR", "Precision", "Accuracy", "Fscore") calculated in all methods and usable for creating curves like ROC and PR.
resTable: a matrix with the best performces for each method.

References

Stehman, S.V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment 62 (1): 77-89.

Examples

Run this code

simData <- simulatedData(p = 5, n = 10, mu = 50, sigma = 0.25,
                        ppower = 0.73, noise = FALSE)
counts <- simData$counts
adjMat <- simData$adjMat

#netData <- mainNetFunction(counts, adjMat, nchips = 2)