pluripotents.frame {pathprint} | R Documentation |
A manually compiled list of pluripotent arrays (induced pluripotent cells and embryonic stem cells) together with their GEO IDs and descriptions
pluripotents.frame
A data frame with 278 observations on the following 5 variables.
GSM
GEO sample ID
GSE
GEO series ID
GPL
GEO platform ID
source
GEO description - Source
Characteristics
GEO description - Characteristic
http://www.ncbi.nlm.nih.gov/geo/
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.
consensusDistance
, consensusFingerprint
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])$fingerprint GEO.metadata.matrix = colData(geo_sum_data[,300000:350000]) # Extract common GSMs since we only loaded part of the geo_sum_data object common_GSMs <- intersect(pluripotents.frame$GSM,colnames(GEO.fingerprint.matrix)) # free up space by removing the geo_sum_data object remove(geo_sum_data) head(pluripotents.frame) # Use pathway fingerprints to search for # additional pluripotent arrays across GEO # create consensus pluripotent 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: pluripotent samples")