diffNetAnalysis: Differential network analysis method
In BioNetStat: Biological Network Analysis

Description Usage Arguments Value Examples

Differential network analysis method

diffNetAnalysis(
  method,
  options = list(bandwidth = "Sturges"),
  varFile,
  labels,
  varSets = NULL,
  adjacencyMatrix,
  numPermutations = 1000,
  print = TRUE,
  resultsFile = NULL,
  seed = NULL,
  min.vert = 5,
  BPPARAM = NULL,
  na.rm = NULL
)

`method`	a function that receives two adjacency matrices and returns a list containing a statistic theta that measures the difference between them, and a p-value for the test H0: theta = 0 against H1: theta > 0.
`options`	a list contaning parameters used by 'method'. Used only in degreeDistributionTest, spectralEntropyTest and spectralDistributionTest functions. It can be set to either `list(bandwidth="Sturges")` or `list(bandwidth="Silverman")`.
`varFile`	a numeric matrix contaning variables values data.
`labels`	a vector of -1s, 0s, and 1s associating each sample with a phenotype. The value 0 corresponds to the first phenotype class of interest, 1 to the second phenotype class of interest, and -1 to the other classes, if there are more than two classes in the gene expression data.
`varSets`	a list of gene sets. Each element of the list is a character vector v, where v[1] contains the gene set name, v[2] descriptions about the set, v[3..length(v)] the genes that belong to the set.
`adjacencyMatrix`	a function that receives a numeric matrix containing gene expression data and returns the adjacency matrix of the inferred co-expression graph.
`numPermutations`	the number of permutations for the permutation test.
`print`	a logical. If true, it prints execution messages on the screen. resultsFile: path to a file where the partial results of the analysis will be saved. If NULL, then no partial results are saved.
`resultsFile`	a ".RData" file name to be saved in tha work directory.
`seed`	the seed for the random number generators. If it is not null then the sample permutations are the same for all the gene sets.
`min.vert`	lower number of nodes (variables) that has to be to compare the networks.
`BPPARAM`	An optional BiocParallelParam instance determining the parallel back-end to be used during evaluation, or a list of BiocParallelParam instances, to be applied in sequence for nested calls to BiocParallel functions. #MulticoreParam()
`na.rm`	remove the NA values by excluding the rows ("row") or the columns ("col") that contaings it. If NULL (default) the NA values are not removed.

a data frame containing the name, size, test statistic, nominal p-value and adjusted p-value (q-value) associated with each gene set.

# Glioma data
data("varFile")
gliomaData <- system.file("extdata", "variablesValue_BioNetStat_tutorial_data.csv", package = "BioNetStat")
labels<-doLabels(gliomaData)
adjacencyMatrix1 <- adjacencyMatrix(method="spearman", association="pvalue", threshold="fdr",
 thr.value=0.05, weighted=FALSE)
diffNetAnalysis(method=degreeCentralityTest, varFile=varFile, labels=labels, varSets=NULL,
 adjacencyMatrix=adjacencyMatrix1, numPermutations=1, print=TRUE, resultsFile=NULL,
  seed=NULL, min.vert=5, option=NULL)
# The numPermutations number is 1 to do a faster example, but we advise to use unless 1000 permutations in real analysis
  
# Random data
set.seed(1)
varFile <- as.data.frame(matrix(rnorm(120),40,30))
labels<-data.frame(code=rep(0:3,10),names=rep(c("A","B","C","D"),10))
adjacencyMatrix1 <- adjacencyMatrix(method="spearman", association="pvalue", threshold="fdr",
 thr.value=0.05, weighted=FALSE)
diffNetAnalysis(method=degreeCentralityTest, varFile=varFile, labels=labels, varSets=NULL,
 adjacencyMatrix=adjacencyMatrix1, numPermutations=10, print=TRUE, resultsFile=NULL,
  seed=NULL, min.vert=5, option=NULL)