Hi:

After going through with basic workflow of microarray analysis in limma package, I am able to some standard practice for microarray data. However, since I am using currently Affymetrix microarrays expression data (data is already preprocessed) for my experiments. Essentially, I have Affymetrix microarrays expression data matrix (Affymetrix probe-sets in rows (32830 probesets), and RNA samples in columns (735 samples)). I also have pheno data which contains metadata information of the above expression matrix (735 in rows (sample identifiers), and 6 description elements in columns). I am trying to add a gene-level annotation to Affymetrix expression set by using given Affymetrix data, but it is not very intuitive for me at the first place.

After learning limma package, I tried as follow:

load("data/HTA20_RMA.RData")
man_threshold <- 5
hist_res <- hist(row_medArray, 100, col = "cornsilk", freq = FALSE,
                 main = "Histogram of the median intensities",
                 border = "antiquewhite4",
                 xlab = "Median intensities")

abline(v = man_threshold, col = "coral4", lwd = 2)

RLE_data <- base::sweep(eset_HTA20,1,row_medArray)
RLE_data <- base::as.data.frame(RLE_data)

RLE_data_gathered <-
    tidyr::gather(RLE_data, patient_array, log2_expression_deviation)

Essentially, I am trying to figure out those from my Affymetrix expression data and phenodata:

1. how to make summarization of Affymetrix microarray expression matrix at gene level?
2. how to list out probesets intensities per gene?
3. how to filter out probsets and genes per sample?
4. how to add a gene-level annotation to Affymetrix expression data?

but I am not quite sure how can I add a gene-level annotation to my Affymetrix expression data. How can I make this happen? how to filter out probsets and genes per sample in R? Is there Bioconductor packages or possible workflow to resolve my doubts? As a newbie to microarray data analysis, I am eager to learn a possible solution from this community. Thanks