I have preprocessed Affymetrix microarrays expression data matrix (Affymetrix probe-sets in rows (32830 probesets), and RNA samples in columns (735 samples)) as follows:
HTA20_rma <- load("HTA20_RMA.RData")
> eset_HTA20[1:10,1:3]
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
1000_at 3.567779 3.452524 3.316134
10000_at 6.166815 5.678373 6.185059
100009613_at 4.443027 4.773199 4.393488
100009676_at 5.836522 6.143398 5.898364
10001_at 6.330018 5.601745 6.137984
10002_at 4.922339 4.711765 4.628124
10003_at 2.689344 2.771010 2.556756
since I am not able to access raw cell files at the moment, I am experimenting eset_HTA20 Affymetrix expression data for my downstream analysis. However, I am interested to verify the quality and accuracy of this preprocessed eset_HTA20 data once again. To do so, I believe using limma' function such as plotDensities:
## generate plotDensities graph for each sample (a.k.a, RNA sample)
eset_bc <- backgroundCorrect(eset_HTA20, method = "normexp")
plotDensities(eset_bc)
and I got following densities plot which is not intuitive to me to understand:
meanwhile, when I tried to normalize expression data, I got this error:
> eset_MA <- normalizeWithinArrays(eset_bc)
Error: $ operator is invalid for atomic vectors
plotDensities(eset_MA)
Is there any way that I can verify the quality and accuracy of this preprocessed Affymetrix expression data in R? How can I lay out a concrete evaluation procedure for normalization and background correction on this data? Instead of generating densities plot, what else I can do about it? How can I make density plot more meaningful for downstream analysis? Any idea?
