runSPIA {graphite} | R Documentation

## Run SPIA analysis

### Description

Run a topological analysis on an expression dataset using SPIA.

### Usage

      runSPIA(de, all, pathwaySetName, ...)

### Arguments

`de` | A named vector containing log2 fold-changes of the differentially expressed genes. The names of this numeric vector are Entrez gene IDs.
`all` | A vector with the Entrez IDs in the reference set. If the data was obtained from a microarray experiment, this set will contain all genes present on the specific array used for the experiment. This vector should contain all names of the 'de' argument.
`pathwaySetName` | The name of a pathway set created with `prepareSPIA`.
`...` | Additional options to pass to `spia`.

### Details

The spia option "organism" is internally used. It is an error use it in the additional options.

### Value

The same of spia, without KEGG links. A data frame containing the ranked pathways and various statistics: pSize is the number of genes on the pathway; NDE is the number of DE genes per pathway; tA is the observed total preturbation accumulation in the pathway; pNDE is the probability to observe at least NDE genes on the pathway using a hypergeometric model; pPERT is the probability to observe a total accumulation more extreme than tA only by chance; pG is the p-value obtained by combining pNDE and pPERT; pGFdr and pGFWER are the False Discovery Rate and respectively Bonferroni adjusted global p-values; and the Status gives the direction in which the pathway is perturbed (activated or inhibited).

### References

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.

### See Also

`spia`

### Examples

    if (require(SPIA) && require(hgu133plus2.db)) {
      data(colorectalcancer)

      top$ENTREZ <- mapIds(hgu133plus2.db, top$ID, "ENTREZID", "PROBEID", multiVals = "first")
      top <- top[!is.na(top$ENTREZ) & !duplicated(top$ENTREZ), ]
      top$ENTREZ <- paste("ENTREZID", top$ENTREZ, sep = ":")
      tg1 <- top[top$adj.P.Val < 0.05, ]

      DE_Colorectal = tg1$logFC
      names(DE_Colorectal) <- tg1$ENTREZ
      ALL_Colorectal <- top$ENTREZ

      biocarta <- pathways("hsapiens", "biocarta")[1:20]
      biocarta <- convertIdentifiers(biocarta, "ENTREZID")
      prepareSPIA(biocarta, "biocartaEx")
      runSPIA(de = DE_Colorectal, all = ALL_Colorectal, "biocartaEx")
    }

[Package graphite version 1.26.3 Index]