SPIA: Signaling Pathway Impact Analysis (SPIA)
In ToPASeq: Topology-based pathway analysis of RNA-seq data

Description Usage Arguments Value Author(s) References Examples

The function runs SPIA method on microarray or RNA-Seq data. The implementatio includes the identification of differentially expressed genes and transformation of pathways' topologies to an appropriate form. The SPIA method combines two independent p-values. One p-value comes from overrepresentation analysis and the other is so called pertubation factor.

SPIA(x, group, pathways, type, which = "proteins", edgeType = NULL,
  preparePaths = TRUE, norm.method = NULL, test.method = NULL,
  p.th = 0.05, logFC.th = 2, nperm = 1000, combine = "fisher",
  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.
`p.th`	Numeric, threshold for p-values of tests for differential expression of genes. Use `1` if you don't want any threshold to be applied
`logFC.th`	Numeric, threshold for log fold-change of a gene to identify the gene as differentially expressed. Use negative if you don't want any threshold to be applied
`nperm`	Numeric, number of permutations
`combine`	Character, the method to combine p-values. Defaults to `"fisher"` for Fisher's method. The other possible value is `"norminv"` for the normal inversion method.
`both.directions, maxNodes, minEdges, commonTh, filterSPIA, convertTo, convertBy`	Arguments for the `preparePathways()`

A list:

`res`	A matrix with columns as descibed below: pSize - Pathway size, number of genes, NDE - Number of differentially expressed genes, pNDE - P-value of the overrepresentation part of the method, tA - The observed total preturbation accumulation in the pathway, pPERT - P-value of the pertubation part of the method, p - Combined p-value (overrepresentation and pertubation), pFdr - False discovery rate adjusted `p`, pFWER - FWER adjusted `p`, Status - If a pathway was identified as Acivated or Inhibited
`topo.sig`	A list of accumulated pertubation factors and log fold-changes for genes in individual pathways
`degtest`	A numeric vector of gene-level differential expression statistics of all genes in the dataset

Ivana Ihnatova

Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim JS, Kim CJ, Kusanovic JP, Romero R. A novel signaling pathway impact analysis. Bioinformatics. 2009 Jan 1;25(1):75-82.

Adi L. Tarca, Sorin Draghici, Purvesh Khatri, et. al, A Signaling Pathway Impact Analysis for Microarray Experiments, 2008, Bioinformatics, 2009, 25(1):75-82.

Draghici, S., Khatri, P., Tarca, A.L., Amin, K., Done, A., Voichita, C., Georgescu, C., Romero, R.: A systems biology approach for pathway level analysis. Genome Research, 17, 2007. Massa MS, Chiogna M, Romualdi C. Gene set analysis exploiting the topology of a pathway. BMC System Biol. 2010 Sep 1;4:121.

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)),]

SPIA(MAdata, Biobase::pData(vdx)[,"er"][1:10], pathways, type="MA", convertTo="SYMBOL", logFC.th=-1)
}