HI everybody We conducted a two-color microarray experiment using a 19 000-probe home made cDNA array. Our experiment contains 12 arrays. We use LIMMA to do all the normalization and model fitting and stats. Out of the 19 000 probes, several clones are part of the same contig, as annotated by TIGR. We decided to average the M values for these clones that correspond to a single contig to obtain a single M value for a given contig, for each array separately. We also wanted to remove probes that were called empty after sequencing (but they were already on the printed microarray). We exported the MAlist containing the normalised data (called "MAptip.nba.scale") and extracted the M data for each of the 12 slides in Python. We did the averaging and removing of "empty" spots and now have a new file with columns containing information on block, row, column, spot ID, annotation information for the contigs (and singletons) and then data for each slide in the following columns. Each row contains the averaged M values. We looked for a way to convert this file back into a MAlist so we can specify our design and do a fit. We read in the archives about a library called convert (which we did not find on CRAN) and info on how to transform data into an exprSet for affy data. Would someone be willing to help us with this task and give us pointers? Thank you very much Nadia Aubin-Horth Assistant professor Biology Department Institute of Integrative and Systems Biology Universit? Laval Qu?bec, Canada

The convert package is a bioconductor package and I think the function that you want is as(foo, "MAlist") On Wed, Feb 10, 2010 at 12:40 PM, Aubin-Horth Nadia <nadia.aubin-horth at="" bio.ulaval.ca=""> wrote: > HI everybody > > We conducted a two-color microarray experiment using a 19 000-probe home > made cDNA array. Our experiment contains 12 arrays. We use LIMMA to do all > the normalization and model fitting and stats. Out of the 19 000 probes, > several clones are part of the same contig, as annotated by TIGR. We decided > to average the M values for these clones that correspond to a single contig > to obtain a single M value for a given contig, for each array separately. We > also wanted to remove probes that were called empty after sequencing (but > they were already on the printed microarray). We exported the MAlist > containing the normalised data (called "MAptip.nba.scale") and extracted the > M data for each of the 12 slides in Python. We did the averaging and > removing of "empty" spots and now have a new file with columns containing > information on block, row, column, spot ID, annotation information for the > contigs (and singletons) and then data for each slide in the following > columns. Each row contains the averaged M values. > > We looked for a way to convert this file back into a MAlist so we can > specify our design and do a fit. We read in the archives about a library > called convert (which we did not find on CRAN) and info on how to transform > data into an exprSet for affy data. Would someone be willing to help us with > this task and give us pointers? > > Thank you very much > > Nadia Aubin-Horth > Assistant professor > Biology Department > Institute of Integrative and Systems Biology > Universit? Laval > Qu?bec, Canada > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > -- Stephen Sefick Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals, and have not exhausted the annoying little problems of being mammals. -K. Mullis

On Wed, Feb 10, 2010 at 1:40 PM, Aubin-Horth Nadia <nadia.aubin-horth at="" bio.ulaval.ca=""> wrote: > HI everybody > > We conducted a two-color microarray experiment using a 19 000-probe home > made cDNA array. Our experiment contains 12 arrays. We use LIMMA to do all > the normalization and model fitting and stats. Out of the 19 000 probes, > several clones are part of the same contig, as annotated by TIGR. We decided > to average the M values for these clones that correspond to a single contig > to obtain a single M value for a given contig, for each array separately. We > also wanted to remove probes that were called empty after sequencing (but > they were already on the printed microarray). We exported the MAlist > containing the normalised data (called "MAptip.nba.scale") and extracted the > M data for each of the 12 slides in Python. We did the averaging and > removing of "empty" spots and now have a new file with columns containing > information on block, row, column, spot ID, annotation information for the > contigs (and singletons) and then data for each slide in the following > columns. Each row contains the averaged M values. > > We looked for a way to convert this file back into a MAlist so we can > specify our design and do a fit. We read in the archives about a library > called convert (which we did not find on CRAN) and info on how to transform > data into an exprSet for affy data. Would someone be willing to help us with > this task and give us pointers? Hi, Nadia. The limma package will work just fine with a matrix of log ratios. You do not need to convert back to an MAList to use limma. Sean

Quoting Aubin-Horth Nadia <nadia.aubin-horth at="" bio.ulaval.ca="">: > HI everybody > > We conducted a two-color microarray experiment using a 19 000-probe > home made cDNA array. Our experiment contains 12 arrays. We use LIMMA > to do all the normalization and model fitting and stats. Out of the 19 > 000 probes, several clones are part of the same contig, as annotated by > TIGR. We decided to average the M values for these clones that > correspond to a single contig to obtain a single M value for a given > contig, for each array separately. We also wanted to remove probes that > were called empty after sequencing (but they were already on the > printed microarray). We exported the MAlist containing the normalised > data (called "MAptip.nba.scale") and extracted the M data for each of > the 12 slides in Python. We did the averaging and removing of "empty" > spots and now have a new file with columns containing information on > block, row, column, spot ID, annotation information for the contigs > (and singletons) and then data for each slide in the following columns. > Each row contains the averaged M values. > > We looked for a way to convert this file back into a MAlist so we can > specify our design and do a fit. We read in the archives about a > library called convert (which we did not find on CRAN) and info on how > to transform data into an exprSet for affy data. Would someone be > willing to help us with this task and give us pointers? > > Thank you very much > > Nadia Aubin-Horth Hi Nadia, it's actually quite simple, with Limma loaded just create a new MAlist like this: newMA <- new("MAList") and all you have to do is populate it with the appropriate components: newMA$genes could be a matrix or a data frame containing your annotations newMA$M is the matrix with your log2 ratios newMA$A similarly containing the average expression values... newMA$weights if you want to use weights... I believe only the $M component is necessary. In fact, you don't need a MAList to use Limma, you could just feed a matrix of log2 values to lmFit(). But I'd use a fresh MAList with the M values and a $genes component. That way the annotations get passed on to the results, which I find convenient. I hope this helps a bit. Regards, Jose -- Dr. Jose I. de las Heras Email: J.delasHeras at ed.ac.uk The Wellcome Trust Centre for Cell Biology Phone: +44 (0)131 6513374 Institute for Cell & Molecular Biology Fax: +44 (0)131 6507360 Swann Building, Mayfield Road University of Edinburgh Edinburgh EH9 3JR UK ********************************************* NEW EMAIL from July'09: nach.mcnach at gmail.com ********************************************* -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.