TAPPA: Topological Analysis of Pathway Phenotype Association (TAPPA)
In ToPASeq: Topology-based pathway analysis of RNA-seq data

Description Usage Arguments Value Author(s) References Examples

The functions analyses the differential expression of pathways via TAPPA method. Expression is compared between two groups of samples by Mann-Whitney test. P-values are later adjusted for multiple hypothesis testing by Benjamini-Hochberg's FDR method.

TAPPA(x, group, pathways, type, which = "proteins", edgeType = NULL,
  preparePaths = TRUE, norm.method = NULL, test.method = NULL,
  test = t.test, normalize = TRUE, verbose = FALSE,
  both.directions = TRUE, maxNodes = 150, minEdges = 0,
  commonTh = 2, filterSPIA = FALSE, convertTo = "none",
  convertBy = NULL)

`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.
`test`	Function implementing a statistical test comparing PCI scores between groups. It is employed as `test(PCI~group)$p.value`, where `PCI` is a numeric vector of the same length as `group`
`normalize`	Logical, should data be normalized?
`verbose`	Logical, if `TRUE` names of the pathways are printed as they are analysed
`both.directions, maxNodes, minEdges, commonTh, filterSPIA, convertTo, convertBy`	Arguments for the `preparePathways()`

A list,

`res`	A data frame, rows refer to pathways. Columns contain: number of valid PCI-scores, median, min and max of the PCI scores for each group of samples, p-value of the `test` (`p.val`) and adjusted p-value (`p.adj`). If less than two nodes are present in the data, the function puts `NA`'s in all columns.
`topo.sig`	`NULL`, it is preserved for the compatibility with other methods implemented in this package
`degtest`	A numeric vector of gene-level differential expression statistics

Ivana Ihnatova

Gao, S. and Wang, X. (2007) TAPPA: topological analysis of pathway phenotype association. Bioinformatics, 23, pages 3100-3102

if (require(breastCancerVDX)) {
data("vdx")
pathways<-pathways("hsapiens","biocarta")[1:10]
MAdata<-Biobase::exprs(vdx)[,1:10]
rownames(MAdata)<-Biobase::fData(vdx)[,"Gene.symbol"]
MAdata<-MAdata[!duplicated(rownames(MAdata)),]

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