pathprint {pathprint} | R Documentation |
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.
Package: | pathprint |
Type: | Package |
Version: | 2.0.0 |
Date: | 2018-04-15 |
License: | GPL |
Gabriel Altschuler, Sokratis Kariotis
Maintainer: Sokratis Kariotis s.kariotis@sheffield.ac.uk
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.
exprs2fingerprint
, consensusFingerprint
,
consensusDistance
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")