Necorr: Necorr
In warelab/NECorr: Gene Regulatory Network Correlations

Necorr

R Documentation

Necorr

Description

NECorr helps discover candidate genes that could be important for specific conditions. The principal inputs are the expression data and the network file. The expression data should start with 3 header columns. The first column describes the conditions. Each condition will be treated separately for the co-expression analysis The output of the program will be generated in a result folder generated in the working path Create the output directory if not existing; generate "./results" dir and "./results/tmp" C.Liseron-Monfils - Ware lab Sept2013 - CSHL partly based on rsgcc package for the GCC, PCC,KCC and SPP Ma et al, 2012, plant Physiology

Usage

Necorr(
  networkFile = "",
  expression = "",
  description.file = "",
  condition = "",
  metadata = "",
  name = "",
  Filelist = "",
  method = "GCC",
  permutation = 1000,
  sigcorr = 0.01,
  fadjacency = "only",
  type = "gene",
  NSockets = 2
)

Arguments

`networkFile`	Molecular network file with source in the first column, targets in the second column
`expression`	Expression file in log2 (ratio expression) with row: gene, first column: type of sample,second column: sample names
`description.file`	genome description
`condition`	Condition from expression to study the network co-expression correlation
`metadata`	dataframe with the metadata
`name`	the name of the
`Filelist`	condition list see if still necessary with metadata
`method`	used for co-expression correlation: GCC, MINE, PCC, SCC or KCC
`permutation`	permutation number used for all significance calculation #param lmiR List of miRNAs
`sigcorr`	significance of the correlation
`fadjacency`	correlation with all combination (all) or network combination only (only)
`type`	Omics comparative expression type: protein or gene
`NSockets`	number of sockets