Necorr | R Documentation |
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
Necorr(
networkFile = "",
expression = "",
description.file = "",
condition = "",
metadata = "",
name = "",
Filelist = "",
method = "GCC",
permutation = 1000,
sigcorr = 0.01,
fadjacency = "only",
type = "gene",
NSockets = 2
)
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 |
res
Christophe Liseron-Monfils, Andrew Olson
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