thresholdFingerprint: Apply threshold values to produce a Pathway Fingerprint

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

Description

Function to produce ternary threshold values, Pathway Fingerprint scores, from continuous geneset enrichment values. Returns ternary scores for each pathway, high (1), mid (0), low (-1)

Usage

thresholdFingerprint(SCE, platform)

Arguments

SCE	Pathway enrichment matrix from single.chip.enrichment
platform	GEO platform ID for array used

Details

The thresholds have been pre-calculated and optimized against a panel of tissue samples (see ref).

Value

Matrix containing ternary scores for each sample in the SCE input matrix. Rownames are genesets and colnames are the columns of the SCE matrix.

Author(s)

Gabriel Altshuler

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, platform.thresholds

Examples

require(pathprintGEOData)
library(SummarizedExperiment)

# load  the data
data(SummarizedExperimentGEO)

# Comparing workflows

# 1. Pathway Fingerprint scores from exprs2fingerprint

# Use ALL dataset as an example

require(ALL)
data(ALL)
annotation(ALL)

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

# Analyze patients with ALL1/AF4 and BCR/ABL translocations
ALL.eset <- ALL[,1:5]
ALL.exprs<-exprs(ALL.eset)
# Process fingerprints
ALL.fingerprint<-exprs2fingerprint(exprs = ALL.exprs,
    platform = "GPL8300",
    species = "human",
    progressBar = TRUE
    )

# 2. Thresholded pathway enrichment values

# Annotate
ALL.exprs.entrez <- customCDFAnn(ALL.exprs, chipframe$GPL8300$ann)

# Pathway enrichment
ALL.enrichment <- single.chip.enrichment(exprs = ALL.exprs.entrez,
    geneset = pathprint.Hs.gs,
    transformation = "squared.rank",
    statistic = "mean",
    normalizedScore = FALSE,
    progressBar = TRUE
    )

# Threshold
ALL.enrichment.threshold <- thresholdFingerprint(
    ALL.enrichment, "GPL8300")

# Compare 1. and 2.
all.equal(ALL.enrichment.threshold, ALL.fingerprint)

hidelab/pathprint documentation built on May 17, 2019, 3:57 p.m.