I am running multi-level experiment of a dataset using LIMMA,
contrast_matrix_Grade<- makeContrasts(AdvanceVsEarlyinT = A.T-nA.T,
AdvanceVsEarlyinnT = A.nT-nA.nT,
TVsnTinAdvance = A.T-A.nT, TVsnTinEarly = nA.T-nA.nT,
levels = design_Grade)
fit <- eBayes(contrasts.fit(lmFit(eset,design = design_Grade,block = i,
correlation = corfit_Grade$consensus),
contrast_matrix_Grade))
table_fit <- topTable(fit, number = Inf)
results <- decideTests(fit)
Since I want to see the gene that simultaneously differentially expressed in two groups,
intersection <- results[,"TVsnTinHigh"] & results[,"TVsnTinLow"]
table_Grade <- topTable(fit [intersection,], n=Inf)
DE_genes_Grade <- subset(table_Grade, adj.P.Val < 0.1)$PROBEID
back_genes_idx <- genefinder(eset, as.character(DE_genes_Grade),
method="manhattan", scale="none")
back_genes_idx <- sapply(back_genes_idx, function(x)x$indices)
back_genes <-featureNames(eset)[back_genes_idx]
back_genes <- setdiff(back_genes, DE_genes_Grade)
intersect(back_genes, DE_genes_Grade)
length(back_genes)
gene_IDs <- rownames(table_Grade)
in_universe <- gene_IDs %in% c(DE_genes_Grade, back_genes)
inSelection <- gene_IDs %in% DE_genes_Grade
all_genes <- factor(as.integer(inSelection[in_universe]))
names(all_genes) <- gene_IDs[in_universe]
ont <- "BP"
top_Grade_GO_data <- new("topGOdata", ontology = ont, allGenes = all_genes, nodeSize = 10,
annot=annFUN.db, affyLib = "hugene10sttranscriptcluster.db")
Error in .local(.Object, ...) : allGenes must be a factor with 2 levels
> str(all_genes)
Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
- attr(*, "names")= chr [1:2373] "7924029" "7908072" "8008237" "8037408" ...
But when I used table_fit, there is no problem.
> str(all_genes)
Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
- attr(*, "names")= chr [1:17425] "7924029" "7908072" "8008237" "8037408" ...
So if I only want to see the GO of the intersected genes of both groups, what should I do?
Thanks
