I am using limma/voom for differential expression analysis and I would like to find out which genomic feature is represented by each point in the mean-variance modelling plot which voom generates. There are some weird trends happening in the data and I'd like to see which genomic features these correspond to. I tried using the save.plot option in the call to voom but it doesn't produce the voom.xy value in the output object?
Old thread, I know, but Biostar bumped it.
v <- voom( x, design, plot = T, save.plot = T ) identify( v$voom.xy, labels = row.names( v ) )
I could click on points and, upon pressing
esc, the names of the points clicked were printed on the graph, and their index at row.names( v ) returned. You can also assign the indexes to a variable:
z <- identify( v$voom.xy, labels = row.names( v ) ) z # 634 3097 4041 9483 rownames(v)[z] # "C16H1orf115" "POC1A" "PLPP2" "FADD"
You also (probably) can accomplish this with the Glimma package, which I never used but seems really great.