使用bioconductor和r标准化data.frame

Question

I see several methods to normalize data in the form of affyBatch objects. Some of the methods used are: threestep , mas5calls , mascallsfilter , justMAS and rma . However my data is in the data.frame format as I have read my expression data from a .txt file.

Can you please let me know what normalization and filtration methods I could use on data.frame? Or is it possible to convert data.frame into an affyBatch object?

When I tried some of the normalization methods, I got the following error:

dat.eset <- threestep(dat.fp, background.method="RMA.2",
                      normalize.method="quantile", summary.method="median.polish")
Error in threestep(dat, background.method = "RMA.2", normalize.method = "quantile", :
argument is data.frame threestep requires AffyBatch dat.mas5 <- mas5calls(dat)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘mas5calls’ for signature ‘"data.frame"’

Answer 1

Better to ask Bioconductor questions on the Bioconductor mailing list. Many of the methods you mention are not commonly used, and are present more or less for historical reasons. Likely your 'data.frame' has already been summarized in some way, eliminating additional methods. You'll really need to provide more information (about what is actually in your data frame) to get a useful answers; you might check out the extensive vignette for the limma package and the microarray work flow as a starting point.

Answer 2

Create a "fake" AffyBatch using the CEL files of the same type as in your experiment (get one from GEO and replicate it by copying), then

my.AB <- readAffy(filenames)

or by read.affy from the simpleaffy package, with the covdesc file

then replace the data by

exprs(my.AB) <- my.numeric.data

where my.numeric.data is your data frame, previously converted to a numeric matrix

Then do MAS/RMA etc as needed