RMA on Exon Array NaN values
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@nicolas-servant-5852
Last seen 10.6 years ago
Dear all, I have downloaded "Affymetrix GeneChip Mouse Exon 1.0 ST" microarrays data from GEO and try to process them. > library(oligo) > library(pd.moex.1.0.st.v1) > exonCELs <- list.celfiles(cel_path, full.names=TRUE) > affyExonFS <- read.celfiles(exonCELs, pkgname = "pd.moex.1.0.st.v1") > exonCore <- oligo::rma(affyExonFS, target = "core") > featureData(exonCore) <- getNetAffx(exonCore, "transcript") > normdata <- exprs(exonCore) > head(exprs(exonCore)) GSM845636.CEL GSM845637.CEL GSM845638.CEL GSM845639.CEL GSM845640.CEL 4305140 NaN NaN NaN NaN NaN 4305147 NaN NaN NaN NaN NaN 4305425 NaN NaN NaN NaN NaN 4305730 NaN NaN NaN NaN NaN 4305790 NaN NaN NaN NaN NaN 4305808 NaN NaN NaN NaN NaN GSM845641.CEL GSM845642.CEL GSM845643.CEL GSM845644.CEL GSM845645.CEL 4305140 NaN NaN NaN NaN NaN 4305147 NaN NaN NaN NaN NaN 4305425 NaN NaN NaN NaN NaN 4305730 NaN NaN NaN NaN NaN 4305790 NaN NaN NaN NaN NaN 4305808 NaN NaN NaN NaN NaN GSM845646.CEL GSM845647.CEL GSM845648.CEL GSM845649.CEL GSM845650.CEL 4305140 NaN NaN NaN NaN NaN 4305147 NaN NaN NaN NaN NaN 4305425 NaN NaN NaN NaN NaN 4305730 NaN NaN NaN NaN NaN 4305790 NaN NaN NaN NaN NaN 4305808 NaN NaN NaN NaN NaN GSM845651.CEL GSM845652.CEL 4305140 NaN NaN 4305147 NaN NaN 4305425 NaN NaN 4305730 NaN NaN 4305790 NaN NaN 4305808 NaN NaN > head(exprs(affyExonFS)) GSM845636.CEL GSM845637.CEL GSM845638.CEL GSM845639.CEL GSM845640.CEL 1 6116 8123 5202 6784 5540 2 296 345 324 392 256 3 5899 8030 5123 6721 5574 4 276 262 252 245 163 5 146 181 200 147 172 6 96 118 111 114 116 GSM845641.CEL GSM845642.CEL GSM845643.CEL GSM845644.CEL GSM845645.CEL 1 6535 8560 5268 6154 5402 2 290 419 318 284 378 3 6800 8286 5163 6511 5514 4 332 348 260 234 311 5 155 211 162 158 150 6 120 148 136 153 89 GSM845646.CEL GSM845647.CEL GSM845648.CEL GSM845649.CEL GSM845650.CEL 1 4597 6446 6260 9346 6683 2 312 294 287 445 309 3 4995 5952 6139 8942 6204 4 212 248 301 282 261 5 403 181 196 182 191 6 95 99 127 146 138 GSM845651.CEL GSM845652.CEL 1 5270 8816 2 410 333 3 6239 7955 4 395 330 5 177 175 6 93 116 I think that the RMA function returns NaN values. It seems that I have some data before normalization. Why ?? Do you think that it can come from the annotation package ?? Many thanks Best Nicolas [[alternative HTML version deleted]]
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@benilton-carvalho-1375
Last seen 5.1 years ago
Brazil/Campinas/UNICAMP
Assuming that you were lucky enough to get GSE34243, try excluding sample 16... In the meantime, will consider how to handle such uncommon samples during background correction. b On Feb 11, 2014 4:06 PM, "Nicolas Servant" <nicolas.servant@gmail.com> wrote: > Dear all, > > I have downloaded "Affymetrix GeneChip Mouse Exon 1.0 ST" microarrays data > from GEO and try to process them. > > > library(oligo) > > library(pd.moex.1.0.st.v1) > > exonCELs <- list.celfiles(cel_path, full.names=TRUE) > > affyExonFS <- read.celfiles(exonCELs, pkgname = "pd.moex.1.0.st.v1") > > exonCore <- oligo::rma(affyExonFS, target = "core") > > featureData(exonCore) <- getNetAffx(exonCore, "transcript") > > normdata <- exprs(exonCore) > > head(exprs(exonCore)) > GSM845636.CEL GSM845637.CEL GSM845638.CEL GSM845639.CEL > GSM845640.CEL > 4305140 NaN NaN NaN NaN > NaN > 4305147 NaN NaN NaN NaN > NaN > 4305425 NaN NaN NaN NaN > NaN > 4305730 NaN NaN NaN NaN > NaN > 4305790 NaN NaN NaN NaN > NaN > 4305808 NaN NaN NaN NaN > NaN > GSM845641.CEL GSM845642.CEL GSM845643.CEL GSM845644.CEL > GSM845645.CEL > 4305140 NaN NaN NaN NaN > NaN > 4305147 NaN NaN NaN NaN > NaN > 4305425 NaN NaN NaN NaN > NaN > 4305730 NaN NaN NaN NaN > NaN > 4305790 NaN NaN NaN NaN > NaN > 4305808 NaN NaN NaN NaN > NaN > GSM845646.CEL GSM845647.CEL GSM845648.CEL GSM845649.CEL > GSM845650.CEL > 4305140 NaN NaN NaN NaN > NaN > 4305147 NaN NaN NaN NaN > NaN > 4305425 NaN NaN NaN NaN > NaN > 4305730 NaN NaN NaN NaN > NaN > 4305790 NaN NaN NaN NaN > NaN > 4305808 NaN NaN NaN NaN > NaN > GSM845651.CEL GSM845652.CEL > 4305140 NaN NaN > 4305147 NaN NaN > 4305425 NaN NaN > 4305730 NaN NaN > 4305790 NaN NaN > 4305808 NaN NaN > > head(exprs(affyExonFS)) > GSM845636.CEL GSM845637.CEL GSM845638.CEL GSM845639.CEL GSM845640.CEL > 1 6116 8123 5202 6784 5540 > 2 296 345 324 392 256 > 3 5899 8030 5123 6721 5574 > 4 276 262 252 245 163 > 5 146 181 200 147 172 > 6 96 118 111 114 116 > GSM845641.CEL GSM845642.CEL GSM845643.CEL GSM845644.CEL GSM845645.CEL > 1 6535 8560 5268 6154 5402 > 2 290 419 318 284 378 > 3 6800 8286 5163 6511 5514 > 4 332 348 260 234 311 > 5 155 211 162 158 150 > 6 120 148 136 153 89 > GSM845646.CEL GSM845647.CEL GSM845648.CEL GSM845649.CEL GSM845650.CEL > 1 4597 6446 6260 9346 6683 > 2 312 294 287 445 309 > 3 4995 5952 6139 8942 6204 > 4 212 248 301 282 261 > 5 403 181 196 182 191 > 6 95 99 127 146 138 > GSM845651.CEL GSM845652.CEL > 1 5270 8816 > 2 410 333 > 3 6239 7955 > 4 395 330 > 5 177 175 > 6 93 116 > > > I think that the RMA function returns NaN values. It seems that I have some > data before normalization. Why ?? Do you think that it can come from the > annotation package ?? > Many thanks > Best > Nicolas > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]
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Thank you very much ! indeed without the GSM845651.CEL it works well. How do you detect the problem ?? Best Nicolas 2014-02-11 19:56 GMT+01:00 Benilton Carvalho <beniltoncarvalho@gmail.com>: > Assuming that you were lucky enough to get GSE34243, try excluding sample > 16... > > In the meantime, will consider how to handle such uncommon samples during > background correction. > > b > On Feb 11, 2014 4:06 PM, "Nicolas Servant" <nicolas.servant@gmail.com> > wrote: > >> Dear all, >> >> I have downloaded "Affymetrix GeneChip Mouse Exon 1.0 ST" microarrays data >> from GEO and try to process them. >> >> > library(oligo) >> > library(pd.moex.1.0.st.v1) >> > exonCELs <- list.celfiles(cel_path, full.names=TRUE) >> > affyExonFS <- read.celfiles(exonCELs, pkgname = "pd.moex.1.0.st.v1") >> > exonCore <- oligo::rma(affyExonFS, target = "core") >> > featureData(exonCore) <- getNetAffx(exonCore, "transcript") >> > normdata <- exprs(exonCore) >> > head(exprs(exonCore)) >> GSM845636.CEL GSM845637.CEL GSM845638.CEL GSM845639.CEL >> GSM845640.CEL >> 4305140 NaN NaN NaN NaN >> NaN >> 4305147 NaN NaN NaN NaN >> NaN >> 4305425 NaN NaN NaN NaN >> NaN >> 4305730 NaN NaN NaN NaN >> NaN >> 4305790 NaN NaN NaN NaN >> NaN >> 4305808 NaN NaN NaN NaN >> NaN >> GSM845641.CEL GSM845642.CEL GSM845643.CEL GSM845644.CEL >> GSM845645.CEL >> 4305140 NaN NaN NaN NaN >> NaN >> 4305147 NaN NaN NaN NaN >> NaN >> 4305425 NaN NaN NaN NaN >> NaN >> 4305730 NaN NaN NaN NaN >> NaN >> 4305790 NaN NaN NaN NaN >> NaN >> 4305808 NaN NaN NaN NaN >> NaN >> GSM845646.CEL GSM845647.CEL GSM845648.CEL GSM845649.CEL >> GSM845650.CEL >> 4305140 NaN NaN NaN NaN >> NaN >> 4305147 NaN NaN NaN NaN >> NaN >> 4305425 NaN NaN NaN NaN >> NaN >> 4305730 NaN NaN NaN NaN >> NaN >> 4305790 NaN NaN NaN NaN >> NaN >> 4305808 NaN NaN NaN NaN >> NaN >> GSM845651.CEL GSM845652.CEL >> 4305140 NaN NaN >> 4305147 NaN NaN >> 4305425 NaN NaN >> 4305730 NaN NaN >> 4305790 NaN NaN >> 4305808 NaN NaN >> > head(exprs(affyExonFS)) >> GSM845636.CEL GSM845637.CEL GSM845638.CEL GSM845639.CEL GSM845640.CEL >> 1 6116 8123 5202 6784 5540 >> 2 296 345 324 392 256 >> 3 5899 8030 5123 6721 5574 >> 4 276 262 252 245 163 >> 5 146 181 200 147 172 >> 6 96 118 111 114 116 >> GSM845641.CEL GSM845642.CEL GSM845643.CEL GSM845644.CEL GSM845645.CEL >> 1 6535 8560 5268 6154 5402 >> 2 290 419 318 284 378 >> 3 6800 8286 5163 6511 5514 >> 4 332 348 260 234 311 >> 5 155 211 162 158 150 >> 6 120 148 136 153 89 >> GSM845646.CEL GSM845647.CEL GSM845648.CEL GSM845649.CEL GSM845650.CEL >> 1 4597 6446 6260 9346 6683 >> 2 312 294 287 445 309 >> 3 4995 5952 6139 8942 6204 >> 4 212 248 301 282 261 >> 5 403 181 196 182 191 >> 6 95 99 127 146 138 >> GSM845651.CEL GSM845652.CEL >> 1 5270 8816 >> 2 410 333 >> 3 6239 7955 >> 4 395 330 >> 5 177 175 >> 6 93 116 >> >> >> I think that the RMA function returns NaN values. It seems that I have >> some >> data before normalization. Why ?? Do you think that it can come from the >> annotation package ?? >> Many thanks >> Best >> Nicolas >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > [[alternative HTML version deleted]]
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@benilton-carvalho-1375
Last seen 5.1 years ago
Brazil/Campinas/UNICAMP
Check: summary(exprs(affyExonFS)) on the original set... Note the minimum for sample 16... It is quite uncommon (actually, I had never seen... but I might have missed other sets) to see a CEL file with intensity identical to zero. I haven't tried, but check the pseudo-images for these samples and compare to sample 16... image(affyExonFS, 1) image(affyExonFS, 16) there might be something funny with that sample. b 2014-02-11 18:12 GMT-02:00 Nicolas Servant <nicolas.servant@gmail.com>: > Thank you very much ! indeed without the GSM845651.CEL it works well. > How do you detect the problem ?? > Best > Nicolas > > > 2014-02-11 19:56 GMT+01:00 Benilton Carvalho <beniltoncarvalho@gmail.com>: > > Assuming that you were lucky enough to get GSE34243, try excluding sample >> 16... >> >> In the meantime, will consider how to handle such uncommon samples during >> background correction. >> >> b >> On Feb 11, 2014 4:06 PM, "Nicolas Servant" <nicolas.servant@gmail.com> >> wrote: >> >>> Dear all, >>> >>> I have downloaded "Affymetrix GeneChip Mouse Exon 1.0 ST" microarrays >>> data >>> from GEO and try to process them. >>> >>> > library(oligo) >>> > library(pd.moex.1.0.st.v1) >>> > exonCELs <- list.celfiles(cel_path, full.names=TRUE) >>> > affyExonFS <- read.celfiles(exonCELs, pkgname = "pd.moex.1.0.st.v1") >>> > exonCore <- oligo::rma(affyExonFS, target = "core") >>> > featureData(exonCore) <- getNetAffx(exonCore, "transcript") >>> > normdata <- exprs(exonCore) >>> > head(exprs(exonCore)) >>> GSM845636.CEL GSM845637.CEL GSM845638.CEL GSM845639.CEL >>> GSM845640.CEL >>> 4305140 NaN NaN NaN NaN >>> NaN >>> 4305147 NaN NaN NaN NaN >>> NaN >>> 4305425 NaN NaN NaN NaN >>> NaN >>> 4305730 NaN NaN NaN NaN >>> NaN >>> 4305790 NaN NaN NaN NaN >>> NaN >>> 4305808 NaN NaN NaN NaN >>> NaN >>> GSM845641.CEL GSM845642.CEL GSM845643.CEL GSM845644.CEL >>> GSM845645.CEL >>> 4305140 NaN NaN NaN NaN >>> NaN >>> 4305147 NaN NaN NaN NaN >>> NaN >>> 4305425 NaN NaN NaN NaN >>> NaN >>> 4305730 NaN NaN NaN NaN >>> NaN >>> 4305790 NaN NaN NaN NaN >>> NaN >>> 4305808 NaN NaN NaN NaN >>> NaN >>> GSM845646.CEL GSM845647.CEL GSM845648.CEL GSM845649.CEL >>> GSM845650.CEL >>> 4305140 NaN NaN NaN NaN >>> NaN >>> 4305147 NaN NaN NaN NaN >>> NaN >>> 4305425 NaN NaN NaN NaN >>> NaN >>> 4305730 NaN NaN NaN NaN >>> NaN >>> 4305790 NaN NaN NaN NaN >>> NaN >>> 4305808 NaN NaN NaN NaN >>> NaN >>> GSM845651.CEL GSM845652.CEL >>> 4305140 NaN NaN >>> 4305147 NaN NaN >>> 4305425 NaN NaN >>> 4305730 NaN NaN >>> 4305790 NaN NaN >>> 4305808 NaN NaN >>> > head(exprs(affyExonFS)) >>> GSM845636.CEL GSM845637.CEL GSM845638.CEL GSM845639.CEL GSM845640.CEL >>> 1 6116 8123 5202 6784 5540 >>> 2 296 345 324 392 256 >>> 3 5899 8030 5123 6721 5574 >>> 4 276 262 252 245 163 >>> 5 146 181 200 147 172 >>> 6 96 118 111 114 116 >>> GSM845641.CEL GSM845642.CEL GSM845643.CEL GSM845644.CEL GSM845645.CEL >>> 1 6535 8560 5268 6154 5402 >>> 2 290 419 318 284 378 >>> 3 6800 8286 5163 6511 5514 >>> 4 332 348 260 234 311 >>> 5 155 211 162 158 150 >>> 6 120 148 136 153 89 >>> GSM845646.CEL GSM845647.CEL GSM845648.CEL GSM845649.CEL GSM845650.CEL >>> 1 4597 6446 6260 9346 6683 >>> 2 312 294 287 445 309 >>> 3 4995 5952 6139 8942 6204 >>> 4 212 248 301 282 261 >>> 5 403 181 196 182 191 >>> 6 95 99 127 146 138 >>> GSM845651.CEL GSM845652.CEL >>> 1 5270 8816 >>> 2 410 333 >>> 3 6239 7955 >>> 4 395 330 >>> 5 177 175 >>> 6 93 116 >>> >>> >>> I think that the RMA function returns NaN values. It seems that I have >>> some >>> data before normalization. Why ?? Do you think that it can come from the >>> annotation package ?? >>> Many thanks >>> Best >>> Nicolas >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor@r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> > [[alternative HTML version deleted]]
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