I have a matrix; on the x axis is samples of say 100 humans of different ages, on the y axis is genes, and the cells in the matrix are log transformed microarray gene expression values.
Age20 Age21 Age30 Age40 ........-> Age90 Gene1 4.3 -2.1 1.2 4.5............. -1.2 Gene2 1.1 1.0 -1.9 2.3............. 3.1 Gene3 1.2 -2.3 3.4 0.4............. 2.3 Gene4 -1.2 -0.2 1.2 1.2............. 1.2 Gene5 -0.9 0.2 0.3 -0.4............ 1.4
So basically, I have a matrix prepared; all I want to do is put this into limma, and get back a set of genes whose expression changes with age; so that I can compare between this method and another method I used.
I wrote this code:
library(limma) table <-read.table("table",header=T) fit <- lmFit(table) # What should the design be? fit <- eBayes(fit) options(digits=3) writefile = topTable(fit,n=Inf,sort="none", p.value=0.01) write.csv(writefile, file="file.csv")
The problem: The code runs, but the file is empty. Is it because I didn't add a design parameter to my model? If so, what should the design be? I can find lots of stuff online, but a lot of it does not start from having a gene expression matrix, or involves adding in extra file info. Is it not possible to simply read in a table like above, and tell me which genes' expression are not remaining constant with age?