Some days ago I recived a results table from a multiplexed ELISA array. I decided to use limma to work with the data as I could see that the data at the end was quite similar at the scale level to a microarray after checking it with the colleague that has produced the data which has certain expertise in the technique . Nevertheless I do only have a total of 22 cytokines to test whether if they are or not deferentially expressed. At this point I am working with the same code I usually use to perform microarray analysis.
Could anyone tell me wether if there better ways to analyze data in limma when having matrices with that low amount of genes to test ?
## model3, compare the hearning level loss level accounting for the day as a covariate
# design
groups1 <- meta_data$condition
groups2 <- meta_data$day
design_model3 <- model.matrix(~ 0 + groups1 + groups2)
colnames(design_model3) <- gsub("groups[1,2]{1}| days", replacement = "", x = colnames(design_model3))
# model fit
fit_model3 <- lmFit(expression_matrix, design = design_model3)
# prepare contrasts
cnt_model3 <- makeContrasts("Loss vs No loss" = loss - no_loss, levels = design_model3)
# fit the contrasts
fit_model3 <- contrasts.fit(fit = fit_model3, contrasts = cnt_model3)
fit_model3 <- eBayes(fit = fit_model3)
tt_model3 <- topTable(fit = fit_model3, number = Inf)
Just a little comment, the day variable is a two-level factor it is no numerical, just in case anybody has any question about it. Thanks!
Thanks ! Just what I ended up figuring out.