I am experimenting preprocessed Affymetrix microarrays expression data matrix (Affymetrix probe-sets in rows (32830 probesets), and RNA samples in columns (735 samples)) for my downstream analysis. However, I have
pheno metadata for the experiment observations.
data that I used:
> dim(eset_HTA20)  32830 735 > eset_HTA20[1:3, 1:3] Tarca_001_P1A01 Tarca_003_P1A03 Tarca_004_P1A04 1_at 6.062215 6.125023 5.875502 10_at 3.796484 3.805305 3.450245 100_at 5.849338 6.191562 6.550525 > dim(phenoDat)  735 7 > phenoDat[1:3,] SampleID GA Batch Set Train Platform Tarca_001_P1A01 Tarca_001_P1A01 11.0 1 PRB_HTA 1 HTA20 Tarca_003_P1A03 Tarca_013_P1B01 15.3 1 PRB_HTA 1 HTA20 Tarca_004_P1A04 Tarca_025_P1C01 21.7 1 PRB_HTA 1 HTA20 T Tarca_001_P1A01 Preterm Tarca_003_P1A03 Preterm Tarca_004_P1A04 Preterm
I tried differential expression analysis on
eset_HTA20 data, here is my workflow by using
ano <- phenoDat esetHTA <- HTA20_rma_filt ano$ID <- factor(ano$SampleID) designs <- model.matrix(~0+T+SampleID, ano) esets <- eset_HTA20[, rownames(ano)] colnames(designs)<-substr(colnames(designs),2,100) fit <- lmFit(esets, designs) cont.matrix <- makeContrasts( contrasts="Term-Preterm",levels=designs) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2)
but when I ran
fit2 <- eBayes(fit2), I got the following error:
> fit2 <- eBayes(fit2) Error in .ebayes(fit = fit, proportion = proportion, stdev.coef.lim = stdev.coef.lim, : No residual degrees of freedom in linear model fits
I looked into support site here and Gordon explained several threads that this happened if data doesn't have replicate. I am not quite sure how to proceed with my analysis now. How can I know data have replicates or not? If there is not replicate, what's the possible solution to go through with this issue? can anyone point me out any possible strategy I could try here? any thought?