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

In pathprint: 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 [e-mail]

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")

pathprint documentation built on April 28, 2020, 7:54 p.m.