pathprint-package: Pathway fingerprinting for analysis of gene expression arrays
In hidelab/pathprint: Pathway fingerprinting for analysis of gene expression arrays

Description Details Author(s) References See Also Examples

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")