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?