consensusFingerprint {pathprint} | R Documentation |
Produces a pathway fingerprint that represents the consensus of a series of pathway fingerprints, according to a user-defined threshold
consensusFingerprint(fingerprintframe, threshold)
fingerprintframe |
matrix of fingerprints from which the consensus will be calculated |
threshold |
threshold value (between 0 and 1) |
For each pathway the mean fingerprint score, m, is calculated, and the consensus
defined as
+1 if m > threshold
-1 if m < threshold
0 otherwise
Vector of consensus pathway fingerprint scores with names corresponding to pathways
Gabriel Altschuler
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.
require(pathprintGEOData) library(SummarizedExperiment) # load the data 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])$fingerprint GEO.metadata.matrix = colData(geo_sum_data[,300000:350000]) # free up space by removing the geo_sum_data object remove(geo_sum_data) # Extract common GSMs since we only loaded part of the geo_sum_data object common_GSMs <- intersect(pluripotents.frame$GSM,colnames(GEO.fingerprint.matrix)) # search for pluripotent arrays # load fingerprint matrix and pluripotent reference # create consensus fingerprint 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: curated pluripotent samples")