TopologyGSA: Gene set analysis exploiting the topology of a pathway...

In ToPASeq: Topology-based pathway analysis of RNA-seq data

Description

TopologyGSA method uses graphical models to test the differential expression of a pathway. It also highlights pathway components involved in the deregulation.

Usage

TopologyGSA(x, group, pathways, type, which = "proteins",
  edgeType = NULL, preparePaths = TRUE, norm.method = NULL,
  test.method = NULL, method = "mean", nperm = 1000, alpha = 0.05,
  testCliques = FALSE, both.directions = TRUE, maxNodes = 150,
  minEdges = 0, commonTh = 2, filterSPIA = FALSE,
  convertTo = "none", convertBy = NULL)

Arguments

x	An ExpressionSet object or a gene expression data matrix or count matrix, rows refer to genes, columns to samples
group	Name or number of the phenoData column or a character vector or factor that contains required class assigments
pathways	A list of pathways in a form from graphite package or created by preparePathways()
type	Type of the input data, "MA" for microarray and "RNASeq" for RNA-Seq
which	Character, which type of nodes is preserved in a pathway. Possible values are "proteins","metabolites","mixed"
edgeType	Character, which type of edges is preserved in a pathway. If NULL, all edges are kept.
preparePaths	Logical, by default the pathways are transformed with preparePathways(). Use FALSE, if you have done this transformation separately
norm.method	Character, the method to normalize RNAseq data. If NULL then vst-normalization is performed. Possible values are: "edgeR", "vst", "rLog", "none"
test.method	Character, the method for differentiall expression analysis of RNAseq data. If NULL then "voomlimma" is used. Possible values are: "DESeq2", "voomlimma", "vstlimma", "edgeR". This analysis is needed only for the visualization.
method	Either "var" and "mean". Determine the type of test used by topologyGSA.
nperm	Numeric, number of permutations.
alpha	Numeric, threshold for statistical significance of variance test. It influences the method for the mean test
testCliques	Logical, if TRUE, then the test is also performed on individual cliques. It can be very computationally complex.
both.directions, maxNodes, minEdges, commonTh, filterSPIA, convertTo, convertBy	Arguments for the preparePathways()

Details

The method requires a Directed Acyclic Graph (DAG). Therefore if a pathway contain also undirected or bidirected edges and error is thrown.

The user can further specify for the mean test:

1. perms number of permutations of the test,

2. paired logical, if TRUE Hotelling test for paired samples is calculated and the test on the variances is not performed

Or for the variance test:

1. variance logical, if TRUE the estimates of the covariance matrices are included in the result.

2. s1 First group covariance matrix estimation.

3. s2 Second group covariance matrix estimation.

Value

A list

res	a list with one entry for each successfully analyzed pathway
topo.sig	if testCliques=TRUE, a list where each slot contains the pvalues and a list of cliques in one pathway. NULL otherwise
degtest	A numeric vector of gene-level differential expression statistics

Author(s)

Ivana Ihnatova

References

Massa MS, Chiogna M, Romualdi C. Gene set analysis exploiting the topology of a pathway. BMC System Biol. 2010 Sep 1;4:121.

Examples

## Not run: 
if (require(breastCancerVDX)) {
data("vdx")
pathways<-pathways("hsapiens","biocarta")[1:3]
MAdata<-Biobase::exprs(vdx)[,1:10]
rownames(MAdata)<-Biobase::fData(vdx)[,"Gene.symbol"]
MAdata<-MAdata[!duplicated(rownames(MAdata)),]

TopologyGSA(MAdata, Biobase::pData(vdx)[,"er"][1:10], pathways, type="MA", convertTo="SYMBOL", nperm=10)
}

## End(Not run)

ToPASeq documentation built on Nov. 8, 2020, 4:59 p.m.