getNetwork {BioTIP} | R Documentation

## Building Networks of Nodes

### Description

This function builds one correlation network for each state (sample group) and runs across all states. The network nodes are defined by the context of the input dataset. For transcriptomic network analysis, network nodes can be the expressed transcript IDs and network links can be the correlation coefficients. Using the Pearson Correlation Coefficient (PCC) analysis, this function assembles a correlation network of nodes (e.g., co-expressed transcripts) for each state using the R package igraph.

### Usage

    getNetwork(optimal, fdr = 0.05)

### Arguments

`optimal` | A list of x numeric data frames, where x is the number of states studied. Each data frame consists of loci with high standard deviations. This object can be obtained through `sd_selection` function.
`fdr` | A numeric cutoff value for a Pearson Correlation Coefficient (PCC) analysis. Default is 0.05. Transcripts are linked into a network if their correlations meet this PCC-significance criterion.

### Value

A list of igraph objects whose length is the length of the input object `optimal`. Each object is a network of correlated nodes whose PCCs meet the significant criteria based on the false discovery rate (FDR) control. The length of the list is the number of states with PCC networks. If no PCC meets the significant criteria in a state, the state will be deleted from the output.

### Author(s)

Zhezhen Wang zhezhen@uchicago.edu; Xinan H Yang xyang2@uchicago.edu

### Examples

    test = list('state1' = matrix(sample(1:10, 6), 2, 3),
     'state2'=matrix(sample(1:10, 6), 2, 3),
     'state3' = matrix(sample(1:10, 6), 2, 3))

    for(i in names(test)){
      colnames(test[[i]]) = 1:3
      row.names(test[[i]]) = 1:2}

    igraphL <- getNetwork(test,  fdr=1)
    #[1] "state1:2 nodes"
    #[1] "state2:2 nodes"
    #[1] "state3:2 nodes

[Package BioTIP version 1.1.1 Index]