Dear Concern,
This is Deb from Hong Kong. I would like to bring your kind attention in connection with executing microarray data analysis with "mogene10stv1probe" .CEL files) using following command lines:
> probepkgname="mogene10stv1probe"
> seq1=get(probepkgname)
> prlen=nchar(seq1$sequence)
> range(prlen)
[1] 21 25
> celfiles.gcrma <- gcrma(celfiles)
Adjusting for optical effect...............................................................................................................................................................................................................................................................................................Done.
Computing affinitiesError: length(prlen) == 1 is not TRUE
## showing the above error message while computing affinities
However, I tried running one test run with human data, supported with "hgu133plus2". It was working perfectly. And,
> probepkgname="hgu133plus2 probe"
> seq1=get(probepkgname)
> prlen=nchar(seq1$sequence)
> range(prlen)
[1] 25 25
## this range returned with 25x25 # thus, next commands for "adjusting optical effects" and "computing affinities" etc. are working smoothly.
Please suggest me how to deal with this execution issue. Thanks in advance for your kind concern.
Thank you.
Best,
Deb
Dear James,
Thank you for your response. However, I used "oligo" packages specifically. But still no good returns. I used "digest", "affy", "simpleaffy", "genefilter". But no specific difference, I noticed. Can you please help me to specifically use some "affy" package that has been modified to accommodate these arrays as you mentioned above? If so, I will grateful. And, maybe further execution guideline! I am just stuck with this step and eagerly hunting for the solution.
Thank you.
Simply saying that you did something and didn't get 'good' results is not really meaningful, as what you think are 'good' results may not conform to what somebody else thinks that might mean, and it's not clear at all what you have done.
The basic paradigm is to do something like
library(oligo)
dat <- read.celfiles(list.celfiles())
eset <- rma(dat)
assuming of course that you have started R in the directory that contains the celfiles you wish to analyze. There is a vignette for the oligo package that you can consult, and for downstream analyses there is the limma User's Guide which is pretty comprehensive.
Thanks James for your kind note. Yes, I agree your viewpoint of course. Anyway, I am gonna try this way that you have indicated. Thanks.