Nick, Assume I understand you correctly, and rows and columns in you data matrix are genes and samples correspondingly, and the data are log2 transformed. Say you have column indecies like: ref1=1:4 samp1=5:8 samp1.1=9:11 ? If you want to do two-state comparison in pathview plots, you may derive the differential expression (log2 ratios) like: #paired data samp1.d=exp.mat[, samp1]-exp.mat[,ref1] #unpaired data samp1.d=exp.mat[, samp1]-rowMeans(exp.mat[,ref1]) samp1.1.d=exp.mat[, samp1.1]-rowMeans(exp.mat[,ref1]) If you want to do multiple-state comparison in pathview plots, you may derive the differential expression (log2 ratios) like: samp.d=cbind(exp.mat[,c(samp1,samp1.1)]-rowMeans(exp.mat[,ref1]), exp.mat[,c(samp2,samp2.1)]-rowMeans(exp.mat[,ref2])) BTW, the latest versions are pathview 1.4.2 and gage 2.14.4, where you can find all updated features including go.gsets function: library(gage) ?go.gsets Package links: http://bioconductor.org/packages/release/bioc/html/gage.html http://bioconductor.org/packages/release/bioc/html/pathview.html HTHs. Weijun -------------------------------------------- On Fri, 8/8/14, Nick wrote: Hello sir,? As a new user i was wondering about few things listed below. 1) I am using mogene20st arrays and to prepare the data with gage function, this function required gsets and i used kegg.sets.mm and i hope this is the correct gsets i used for mouse genesets , and for GO terms i used go.sets.mm. both located in gageDATA package. ?If you have another/specific option, particular for this chip please do let me know. code for e.g. is below data1.kegg.p.egid <- gage(raw exp signals.egid, gset= kegg.sets.mm, ref= ref1, samp = samp1) 2) ?My expression data consist of multiple arrays for e.g. ref 1* 4arrays, sample 1*4arrays and sample 1.1*3arrays ; ref 2*4arrays, sample 2*4arrays ?and sample 2.1*4arrays ?(means all except sample 1.1 are 4 arrays) , so total 23 arrays.? Now i want to prepare my data sets for kegg pathway and i followed General applicable gene sets/Pathway analysis, but on page 18 it says i need to supply expression changes and target pathway. Target pathway is not a problem but i have difficulties in preparing my data for pathview. ? First, i was wondering how it will calculate the significantly expressed gene/s with only raw expression values for pathway.Secondly, based on the above information how it will plot the kegg pathway.? Technical issue:- i have 23 arrays and so i tried to minus my ref 1 ?from sample 1, 1.1 and ref 2 from sample 2, 2,1 respectively. but i get an error? given code: gse16873.d <- gse16783[, dcis] - gse16873 [,hn] ?starting data sets for kegg pathway analysis as i understood so far. ? mycode:signals.egid.dataforkegg <- signals.egid[,c(ED8,M8,ED12,M12)] - signals.egid[,c(con8,con8,con12,con12)]Error in Ops.data.frame(signals.egid[, c(ED8, M8, ED12, M12)], signals.egid[, ?:? ? - only defined for equally-sized data frames OR > signals.egid.dataforkegg <- gagePrep(signals.egid, ref= c(con8,con8,con12, con12), samp= c(ED8, M8, ED12, M12)) Error in gagePrep(signals.egid, ref = c(con8, con8, con12, con12), samp = c(ED8, ?:?? please make sure 'ref' and 'samp' are comparable and of equal length or compare='unpaired' OR signals.egid.dataforkegg <- gagePrep(signals.egid, compare= 'unpaired')Error in gagePrep(signals.egid, compare = "unpaired") :? ? improper 'compare' argument ?value as i understood so far the error could be because of only 3 arrays in sample 1.1 and rest are 4 each. can you please advice how should i prepare my data for pathway analysis with pathview.? 3) there are more functions in gage package such as gagePrep, gagePipe ; i would like to know how relevant is the output from this functions for pathview. as i look at the pathview manual quick start with demo data the starting data is gse16873.d which is an output of code given on page 21 of pathview manual. does results from gagePrep it gives the same results as subtracting ref from sample? gse16873.d <- gagePrep(gse16873, gsets= kegg.gs, ref= hn, samp= dcis) head(gse16873.d[1:2,]) ?DCIS_1 ? ? DCIS_2 ? ? ?DCIS_3 ? ? ?DCIS_4 ? ? ? DCIS_5 ? ? DCIS_6 10000 -0.3076448 -0.1472277 -0.02378481 -0.07056193 -0.001323087 -0.150268110001 ?0.4158680 -0.3347726 -0.51313691 -0.16653712 ?0.111122223 ?0.1340073