Hi Lucia I am not sure if I completely understand your problem, just want to mention that I routinely apply non-specific filtering based on MAS5 calls with a very good outcome (based on a prior-knowledge training set). I do not like so much the alternative approach - filtering based on variance or IQR - as it jeopardizes my preferred way of defining responders by applying a threshold on the local false discovery rate. Could you extend a bit on how you exactly filter based on MAS5 calls, how you define responders and non-responders in qPCR, how your "FDR disaster" exactly looks like. What is your model system by the way, which arrays you use? best regards T. On Aug 13, 2010, at 7:11 PM, Lucia Peixoto wrote: > Dear All, > I want to set up a non-specific filter to eliminate genes that are juts not > expressed from further statistical analysis. I've previously tried a filter > based on Mas5 presence/absence calls which turned out to be a disaster for > the FDR (as measured by lots of qPCRs), probably because 1/3 of the MM > probes actually hybridize better than PM, who knows. > > In any case, my plan is to set up a filter based both on raw fluorescent > intensity and IQR. I am trying to get as much sensitivity as possible > without increasing my FDR too much. > I was thinking that using the intensity distributions and box plots of the > raw data may be useful to determine what the best cutoffs to obtain the best > sensitivity will be. > Any advise on how to select appropriate cutoffs? > > Thank you very much in advance > Lucia > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 ---------------------------------------------------------------------- Dr. Tobias Straub ++4989218075439 Adolf-Butenandt-Institute, M?nchen D

Hi Lucia! did you already have a look at the nsFilter function in the genefilter package? nsFilter(eset, require.entrez = F, require.symbol = F, require.GOBP = FALSE, require.GOCC = FALSE, require.GOMF = FALSE, remove.dupEntrez = TRUE, var.func = IQR, var.cutoff = 0.5, var.filter = TRUE) would simply remove half of the genes based on low IQR across the arrays. assuming that a) non-expressed genes have a low IQR and b) that frequently not more than 50% of genes are expressed in any given tissue. this is far from a "present" filter but I have no other suggestion in case you don't trust MAS calls. out of curiosity: did you quality check your arrays, could there be batch effects, what's your pre- processing? best regards T. On Aug 16, 2010, at 4:50 PM, Lucia Peixoto wrote: > Thanks Tobias for your response > > I am processing data obtained with Affymetrix mouse chips (430_2, previous > version) > The first filterning was done based on presence/absence calls, so only genes > present in 2/17 samples were used. It is a 2 condition set up, with 8 and 9 > replicates for each condition. My definition of FDR in my previous question > was strictly limited to validation in 8+ independent qPCRs of 40+ randomly > selected genes obtained using a SAM cutoff of 5% FDR. So I am talking about > independently re-testing the reproducibility of gene expression, which is > the only way to really know your FDR. Using the Mas5 presence absence calls > filter leads to about 50% of the tested genes not being reproducible. > > If I remove the filtering and redo the analysis at 5% FDR, I get all the the > previous "false positives" to become true positives. Which was not a > surprise to me since about 1/3 of MM probes are known to hybridize better > than PM probes, so I don't know what Mas5 presence/absence really means, but > definitely cannot reflect accurately the presence of a transcript if the MM > probe hybridizes better. > > The problem is that I have a great loss of sensitivity (I have a lot of > positive controls so I know that), and I would like to increase that using a > filter that can come closer to really defining "present", because MM/PM does > not. > any ideas? > thanks > > Lucia > > > On Mon, Aug 16, 2010 at 8:34 AM, Tobias Straub > <tstraub at="" med.uni-muenchen.de="">wrote: > >> Hi Lucia >> >> I am not sure if I completely understand your problem, just want to mention >> that I routinely apply non-specific filtering based on MAS5 calls with a >> very good outcome (based on a prior-knowledge training set). I do not like >> so much the alternative approach - filtering based on variance or IQR - as >> it jeopardizes my preferred way of defining responders by applying a >> threshold on the local false discovery rate. >> >> Could you extend a bit on how you exactly filter based on MAS5 calls, how >> you define responders and non-responders in qPCR, how your "FDR disaster" >> exactly looks like. >> >> What is your model system by the way, which arrays you use? >> >> best regards >> T. >> >> >> On Aug 13, 2010, at 7:11 PM, Lucia Peixoto wrote: >> >>> Dear All, >>> I want to set up a non-specific filter to eliminate genes that are juts >> not >>> expressed from further statistical analysis. I've previously tried a >> filter >>> based on Mas5 presence/absence calls which turned out to be a disaster >> for >>> the FDR (as measured by lots of qPCRs), probably because 1/3 of the MM >>> probes actually hybridize better than PM, who knows. >>> >>> In any case, my plan is to set up a filter based both on raw fluorescent >>> intensity and IQR. I am trying to get as much sensitivity as possible >>> without increasing my FDR too much. >>> I was thinking that using the intensity distributions and box plots of >> the >>> raw data may be useful to determine what the best cutoffs to obtain the >> best >>> sensitivity will be. >>> Any advise on how to select appropriate cutoffs? >>> >>> Thank you very much in advance >>> Lucia >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> 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 >> >> ---------------------------------------------------------------------- >> Dr. Tobias Straub ++4989218075439 Adolf-Butenandt-Institute, M?nchen D >> >> > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 ---------------------------------------------------------------------- Dr. Tobias Straub ++4989218075439 Adolf-Butenandt-Institute, M?nchen D

Hi Wolfgang, just an experience. in some of my analyses applying variance filtering resulted in problems fitting N(0,1) to the limma t statistic. now that i had a quick look at your paper I get an idea that combining limma with the variance filter is anyway not a good idea. the performance of mas call-based filtering/limma t as compared to variance filter/standard t is however (slightly) better as estimated by ROC curve analysis on my prior-knowledge data (3 arrays/condition). this is probably not unexpected? anyway thanks for pointing to the paper, apparently a must-read before applying the nsFilter function. best regards Tobias On Aug 17, 2010, at 9:36 AM, Wolfgang Huber wrote: > Hi Tobias, > you said you were worried about "filtering based on variance or IQR - as it jeopardizes ... applying a threshold on the local false discovery rate." I am not sure I understand what you mean, but the effect (or, if properly applied, non-effect) of filtering on type-I error is also discussed in [1] in some detail. > > > > [1] Richard Bourgon et al. Independent filtering increases detection power for high-throughput experiments. PNAS, 107(21):9546-9551, 2010. > [2] Talloen et al. I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data. > Bioinformatics, doi:10.1093/bioinformatics/btm478 > > Best wishes > Wolfgang > > > On 16/08/10 16:50, Lucia Peixoto wrote: >> Thanks Tobias for your response >> >> I am processing data obtained with Affymetrix mouse chips (430_2, previous >> version) >> The first filterning was done based on presence/absence calls, so only genes >> present in 2/17 samples were used. It is a 2 condition set up, with 8 and 9 >> replicates for each condition. My definition of FDR in my previous question >> was strictly limited to validation in 8+ independent qPCRs of 40+ randomly >> selected genes obtained using a SAM cutoff of 5% FDR. So I am talking about >> independently re-testing the reproducibility of gene expression, which is >> the only way to really know your FDR. Using the Mas5 presence absence calls >> filter leads to about 50% of the tested genes not being reproducible. >> >> If I remove the filtering and redo the analysis at 5% FDR, I get all the the >> previous "false positives" to become true positives. Which was not a >> surprise to me since about 1/3 of MM probes are known to hybridize better >> than PM probes, so I don't know what Mas5 presence/absence really means, but >> definitely cannot reflect accurately the presence of a transcript if the MM >> probe hybridizes better. >> >> The problem is that I have a great loss of sensitivity (I have a lot of >> positive controls so I know that), and I would like to increase that using a >> filter that can come closer to really defining "present", because MM/PM does >> not. >> any ideas? >> thanks >> >> Lucia >> >> >> On Mon, Aug 16, 2010 at 8:34 AM, Tobias Straub >> <tstraub at="" med.uni-muenchen.de="">wrote: >> >>> Hi Lucia >>> >>> I am not sure if I completely understand your problem, just want to mention >>> that I routinely apply non-specific filtering based on MAS5 calls with a >>> very good outcome (based on a prior-knowledge training set). I do not like >>> so much the alternative approach - filtering based on variance or IQR - as >>> it jeopardizes my preferred way of defining responders by applying a >>> threshold on the local false discovery rate. >>> >>> Could you extend a bit on how you exactly filter based on MAS5 calls, how >>> you define responders and non-responders in qPCR, how your "FDR disaster" >>> exactly looks like. >>> >>> What is your model system by the way, which arrays you use? >>> >>> best regards >>> T. >>> >>> >>> On Aug 13, 2010, at 7:11 PM, Lucia Peixoto wrote: >>> >>>> Dear All, >>>> I want to set up a non-specific filter to eliminate genes that are juts >>> not >>>> expressed from further statistical analysis. I've previously tried a >>> filter >>>> based on Mas5 presence/absence calls which turned out to be a disaster >>> for >>>> the FDR (as measured by lots of qPCRs), probably because 1/3 of the MM >>>> probes actually hybridize better than PM, who knows. >>>> >>>> In any case, my plan is to set up a filter based both on raw fluorescent >>>> intensity and IQR. I am trying to get as much sensitivity as possible >>>> without increasing my FDR too much. >>>> I was thinking that using the intensity distributions and box plots of >>> the >>>> raw data may be useful to determine what the best cutoffs to obtain the >>> best >>>> sensitivity will be. >>>> Any advise on how to select appropriate cutoffs? >>>> >>>> Thank you very much in advance >>>> Lucia >>>> >>>> [[alternative HTML version deleted]] >>>> >>>> _______________________________________________ >>>> 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 >>> >>> ---------------------------------------------------------------------- >>> Dr. Tobias Straub ++4989218075439 Adolf-Butenandt-Institute, M?nchen D >>> >>> >> >> [[alternative HTML version deleted]] >> >> >> >> >> _______________________________________________ >> 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 > > -- > > > Wolfgang Huber > EMBL > http://www.embl.de/research/units/genome_biology/huber > > _______________________________________________ > 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 ---------------------------------------------------------------------- Dr. Tobias Straub ++4989218075439 Adolf-Butenandt-Institute, M?nchen D