>Dear Adi and all members, >I have been trying to analyze and identify unique genes and their association with signal pathways by using SPIA package. Here is the link of one of the pathways that I generated: >http://www.genome.jp/dbget- bin/show_pathway?hsa04530+5578+3993+4629+5590+5728 >In this table, there are several gene products are marked in pinkish. This is Kegg tight junction pathway. They are not in colors if you get this table straight from Kegg website (not use the link, just type tight junction pathway in Kegg website). >I am imaging that these genes are the ones being used to predict the pathway. Then I was trying to find the genes from my differentially- expressed gene list (topTable, limma). I can locate some genes but not all. My questions are: >Am I correct that the genes in pinkish in this table are the genes that were used to predict the pathways? >Does the color differentiate the gene products that are inhibited and the gene products are activated? >Were the others that do not exist in the DE table coming from? Are those genes are the ones impacted by the genes that are in the DE table? >I guess what I really like to know is that how this conversion works. >Many many thanks and have a great holidays for all >Jing Hi Jing, The link that spia creates only highlights (in red) the genes that are DE (passed via the "de" argument to the function SPIA) in a given pathway regardless the direction of change. The pathway drawing made by KEGG does not change from one dataset to another. If you want to use different colors for the genes up and down regulated in you DE vector, you can do so by using a KEGG webpage: http://www.genome.jp/kegg/tool/map_pathway2.html The code below generates a text file. If you copy the content of the file and paste it in the "Enter objects one per line followed by bgcolor, fgcolor:" field (after you choose the organism in question, here has) and hit "exec", you will get a list with all pathway that contain at least one of the genes in the DE list. When you click on those links, the pathways will highlight the dowregulated genes in blue and the unregulated ones in red. I hope this helps, Adi org="hsa" DE=c(-1,-1,1,1); names(DE)<-c("1622", "116519", "4199","7350") up<-names(DE[DE>0]) down<-names(DE[DE<=0]) write.table(cbind(c(paste(org,":",up, " red,black",sep=""),paste(org,":",down, " blue,black",sep=""))), file="colorpath.txt",quote=FALSE,row.names=FALSE,col.names=FALSE) [[alternative HTML version deleted]]