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Hello, My name is Mahes Muniandy and I am a doctoral student. I have been analysing Affymetrix HGU133Plus2 cel files to determine differential expressions in twin pairs (within pair differences). I have used affy, gcrma, nsfilter and limma to do my analysis. I have run my analysis using the HGU133plus2 CDF available in biocondutor and then tried the whole analysis again using the HGU133plus2 cdf from Brainarray. The limma results differ significantly (2351 differentially expressed genes for the former and 2700 genes for the latter analysis). 630 genes (about 30%) from the 2351 genes do not exist in the list of 2700 genes. I have read "Evolving Gene/Transcript Definitions Significantly Alter the Interpretation of GeneChip Data M. Dai et al." and see some convincing arguments there. But, I am confused with which limma results to go with. Could you advise me on the guiding principles that I should follow in order to decide which cdf to use. I do realise that the onus is on me to decide but sadly, I am quite lost in this matter. I would appreciate any help available. Many Thanks, Mahes Muniandy, MSc, MBA, MCPM, PMP Uni. Helsinki -- output of sessionInfo(): > sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-unknown-linux-gnu (64-bit) locale:  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C  LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8  LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8  LC_PAPER=en_US.UTF-8 LC_NAME=C  LC_ADDRESS=C LC_TELEPHONE=C  LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages:  parallel stats graphics grDevices utils datasets methods  base other attached packages:  genefilter_1.44.0 limma_3.18.13 gcrma_2.34.0 affy_1.40.0  Biobase_2.22.0 BiocGenerics_0.8.0 loaded via a namespace (and not attached):  affyio_1.30.0 annotate_1.40.1 AnnotationDbi_1.24.0  BiocInstaller_1.12.1 Biostrings_2.30.1 DBI_0.2-7  IRanges_1.20.7 preprocessCore_1.24.0 RSQLite_0.11.4  splines_3.0.2 stats4_3.0.2 survival_2.37-7  XML_3.98-1.1 xtable_1.7-3 XVector_0.2.0  zlibbioc_1.8.0 -- Sent via the guest posting facility at bioconductor.org.