thresholdFingerprint {pathprint} | R Documentation |
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)
thresholdFingerprint(SCE, platform)
SCE |
Pathway enrichment matrix from |
platform |
GEO platform ID for array used |
The thresholds have been pre-calculated and optimized against a panel of tissue samples (see ref).
Matrix containing ternary scores for each sample in the SCE input matrix. Rownames are genesets and colnames are the columns of the SCE matrix.
Gabriel Altshuler
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
, platform.thresholds
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)