TopologyGSA {ToPASeq} | R Documentation

## Gene set analysis exploiting the topology of a pathway (TopologyGSA)

### 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)

[Package ToPASeq version 1.20.0 Index]