ToPASeq (version 1.6.0)

TopologyGSA: Function to use TopologyGSA method on microarray or RNA-Seq data

Description

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

Usage

TopologyGSA(x, group, pathways, type, preparePaths=TRUE, norm.method=NULL, test.method=NULL , method="mean", 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

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 TMM-normalization is performed. Possible values are: "TMM", "DESeq2", "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.

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.

...

Other arguments to be passed to the method. See details for better explanation

both.directions, maxNodes, minEdges, commonTh, filterSPIA, convertTo, convertBy

Arguments for the preparePathways()

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

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. pairedlogical flag. If TRUE Hotelling test for paired samples is calculated and the test on the variances is not performed

Or for the variance test:

1. variancelogical flag. If TRUE the estimates of the covariance matrices are included in the result.
2. s1First group covariance matrix estimation.
3. s2Second group covariance matrix estimation.

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(DEGraph)) {
#   data("Loi2008_DEGraphVignette")
#   pathways<-pathways("hsapiens","biocarta")[1:10]
# 
#   
#   TopologyGSA(exprLoi2008, classLoi2008, pathways, type="MA", method="mean", alpha=0.05, perms=200)
#     TopologyGSA(exprLoi2008, classLoi2008, pathways, type="MA", method="mean", alpha=0.05, perms=200, testCliques=TRUE)
#   }
#   
# if (require(gageData)) {
# 
#  data(hnrnp.cnts)
#  group<-c(rep("sample",4), rep("control",4))
#  hnrnp.cnts<-hnrnp.cnts[rowSums(hnrnp.cnts)>0,]
#  pathways<-pathways("hsapiens","biocarta")[1:10]
#  TopologyGSA(hnrnp.cnts, group,pathways, type="RNASeq",method="mean", alpha=0.05, 
#    perms=200, norm.method="TMM")
# }
# ## End(Not run)