pathprint {pathprint} | R Documentation

## Pathway fingerprinting for analysis of gene expression arrays

### Description

Algorithms to convert a gene expression array provided as an expression table to a 'pathway fingerprint'. The pathway fingerprint provides an unbiased, consistent annotation of expression data as a molecular phenotype, represented by activation status in 633 pathways. This is a vector of discrete ternary scores to represent high (1), low(-1) or insignificant (0) expression in a suite of pathways. Systematic definition of these functional relationships provides a tool for searching a pathway activation map of gene expression spanning species and technologies.

### Details

Package: | pathprint
Type: | Package
Version: | 2.0.0
Date: | 2018-04-15
License: | GPL

### Author(s)

Gabriel Altschuler, Sokratis Kariotis
Maintainer: Sokratis Kariotis s.kariotis@sheffield.ac.uk

### References

Altschuler, G. M., O. Hofmann, I. Kalatskaya, R. Payne, S. J. Ho Sui, U. Saxena, A. V. Krivtsov, S. A. Armstrong, T. Cai, L. Stein and W. A. Hide (2013). "Pathprinting: An integrative approach to understand the functional basis of disease." Genome Med 5(7): 68.

### See Also

`exprs2fingerprint`, `consensusFingerprint`, `consensusDistance`

### Examples

    require(pathprintGEOData)

    # Use fingerprints to analyze the ALL dataset
    require(ALL)
    data(ALL)
    annotation(ALL)
    library(SummarizedExperiment)

    # load the data use
    data(SummarizedExperimentGEO)

    ds = c("chipframe", "genesets","pathprint.Hs.gs",
        "platform.thresholds","pluripotents.frame")
    data(list = ds)

    # extract part of the GEO.fingerprint.matrix and GEO.metadata.matrix
    GEO.fingerprint.matrix = assays(geo_sum_data[,300000:350000])$fingerprint
    GEO.metadata.matrix = colData(geo_sum_data[,300000:350000])

    # free up space by removing the geo_sum_data object
    remove(geo_sum_data)

    # The chip used was the Affymetrix Human Genome U95 Version 2 Array
    # The correspending GEO ID is GPL8300

    # Extract portion of the expression matrix
    ALL.exprs<-exprs(ALL)
    ALL.exprs.sub<-ALL.exprs[,1:5]

    # Process fingerprints
    ALL.fingerprint<-exprs2fingerprint(exprs = ALL.exprs.sub,
    platform = "GPL8300",
    species = "human",
    progressBar = TRUE
    )

    head(ALL.fingerprint)

    ####
    # Construct consensus fingerprint based on pluripotent records
    # Use this consensus to find similar arrays

    # Extract common GSMs since we only loaded part of the geo_sum_data object
    common_GSMs <- intersect(pluripotents.frame$GSM,colnames(GEO.fingerprint.matrix))

    pluripotent.consensus<-consensusFingerprint(
        GEO.fingerprint.matrix[,common_GSMs], threshold=0.9)

    # calculate distance from the pluripotent consensus
    geo.pluripotentDistance<-consensusDistance(
        pluripotent.consensus, GEO.fingerprint.matrix)

    # plot histograms
    par(mfcol = c(2,1), mar = c(0, 4, 4, 2))
    geo.pluripotentDistance.hist<-hist(geo.pluripotentDistance[,"distance"],
        nclass = 50, xlim = c(0,1), main = "Distance from pluripotent consensus")

    par(mar = c(7, 4, 4, 2))
    hist(geo.pluripotentDistance[pluripotents.frame$GSM, "distance"],
        breaks = geo.pluripotentDistance.hist$breaks, xlim = c(0,1),
        main = "", xlab = "above: all GEO, below: pluripotent samples")

[Package pathprint version 1.16.0 Index]