Thanks Sam, Unfortunately, we do not have an appropriate set of negative controls on the arrays. We had problems with spike-in controls which we had hoped to use to calibrate the arrays. But it does seem like a good idea. We are going to compare with the background, which is not ideal but gives us some idea of the situation. --Naomi At 08:55 AM 1/31/2009, Samuel Wuest wrote: >Hi Naomi, > >I am doing work on AffyChips, but my samples are from amplified >material with inputs from around 500pico - few nanogram range; I found >that the Affy-Algorithmus "MAS5 calls" was not very happy with this >type of data and thus compared expression values on the chip with an >empirical negative-distribution (background-distribution), please >refer to people.brandeis.edu/~dtaylor/Taylor_Papers/BIBE07_PANP.pdf >for details (or the Bioconductor-package panp)... I worked on the >Arabidopsis chip, and there are "negative" probes on the chip: probes >that do not match DNA sequences from newer genome releases anymore... >I found that by combining the panp-strategy and the information on >negative probes on the chip, I could generate precise and relatively >accurate predictions on the expression state of a gene (I can't give >you all the details so far). > >So if there are negative controls/negative probes on the array, you >could use them to generate an empirical background-distribution for >each array and then compare your other signals to this.... Depends on >how many negative probes you'd have... The method works well with the >Affy HGU133-series, please refer to the above mentioned sources... > >Hope this helps?? > >Best, Sam > >2009/1/30 Naomi Altman <naomi at="" stat.psu.edu="">: > > I have been having an on-going discussion with a colleague about whether he > > can say that some genes are "absent" in some tissues based on two- color > > microarrays - most recently, Agilent arrays. There are a number of reasons > > that he would like to do this which are a mix of biology and QC. > > > > He wants to use some (arbitrary) normalized expression level, or > > unnormalized level above local background or a percentile of the > whole array > > background or ... > > > > Any suggestions for papers about this? (We can both think of a dozen ways > > to do it, but without experiments to see if they are valid methods, or at > > least a paper to > > cite, I am reluctant to put the statistical seal of approval on any of > > them.) > > > > Thanks, Naomi > > > > p.s. In case anyone thinks that high-throughput sequencing is going to end > > this type of discussion, have a look at the interesting paper by 't Hoen > > comparing sequencing and microarray results. > > > http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&cmd=Retrieve&list _uids=18927111 > > > > Naomi S. Altman 814-865-3791 (voice) > > Associate Professor > > Dept. of Statistics 814-863-7114 (fax) > > Penn State University 814-865-1348 (Statistics) > > University Park, PA 16802-2111 > > > > _______________________________________________ > > 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 > > > > > >_______________________________________________ >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 Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111

In my experience, on two color arrays where you have a single probe per thing you are measuring, it does not work very well to just say "above background". I have data where we had negative controls and positive controls and sometimes the positive controls were below the negative controls. Granted, we did not have many control probes and this array was not run many times, but still. This might be different for affy style arrays where you have multiple different probes measuring the same thing. Kasper On Feb 1, 2009, at 11:49 , Naomi Altman wrote: > Thanks Sam, > Unfortunately, we do not have an appropriate set of negative > controls on the arrays. We had problems with spike-in controls > which we had hoped to use to calibrate > the arrays. But it does seem like a good idea. We are going to > compare with the background, which is not ideal but gives us some > idea of the situation. > > --Naomi > > At 08:55 AM 1/31/2009, Samuel Wuest wrote: >> Hi Naomi, >> >> I am doing work on AffyChips, but my samples are from amplified >> material with inputs from around 500pico - few nanogram range; I >> found >> that the Affy-Algorithmus "MAS5 calls" was not very happy with this >> type of data and thus compared expression values on the chip with an >> empirical negative-distribution (background-distribution), please >> refer to people.brandeis.edu/~dtaylor/Taylor_Papers/BIBE07_PANP.pdf >> for details (or the Bioconductor-package panp)... I worked on the >> Arabidopsis chip, and there are "negative" probes on the chip: probes >> that do not match DNA sequences from newer genome releases anymore... >> I found that by combining the panp-strategy and the information on >> negative probes on the chip, I could generate precise and relatively >> accurate predictions on the expression state of a gene (I can't give >> you all the details so far). >> >> So if there are negative controls/negative probes on the array, you >> could use them to generate an empirical background-distribution for >> each array and then compare your other signals to this.... Depends on >> how many negative probes you'd have... The method works well with the >> Affy HGU133-series, please refer to the above mentioned sources... >> >> Hope this helps?? >> >> Best, Sam >> >> 2009/1/30 Naomi Altman <naomi at="" stat.psu.edu="">: >> > I have been having an on-going discussion with a colleague about >> whether he >> > can say that some genes are "absent" in some tissues based on two- >> color >> > microarrays - most recently, Agilent arrays. There are a number >> of reasons >> > that he would like to do this which are a mix of biology and QC. >> > >> > He wants to use some (arbitrary) normalized expression level, or >> > unnormalized level above local background or a percentile of the >> whole array >> > background or ... >> > >> > Any suggestions for papers about this? (We can both think of a >> dozen ways >> > to do it, but without experiments to see if they are valid >> methods, or at >> > least a paper to >> > cite, I am reluctant to put the statistical seal of approval on >> any of >> > them.) >> > >> > Thanks, Naomi >> > >> > p.s. In case anyone thinks that high-throughput sequencing is >> going to end >> > this type of discussion, have a look at the interesting paper by >> 't Hoen >> > comparing sequencing and microarray results. >> > http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&cmd=Retrieve&l ist_uids=18927111 >> > >> > Naomi S. Altman 814-865-3791 (voice) >> > Associate Professor >> > Dept. of Statistics 814-863-7114 (fax) >> > Penn State University 814-865-1348 >> (Statistics) >> > University Park, PA 16802-2111 >> > >> > _______________________________________________ >> > 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 >> > >> > >> >> _______________________________________________ >> 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 > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 > (Statistics) > University Park, PA 16802-2111 > > _______________________________________________ > 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

Dear JP, This is extremely helpful. Since my email below, I did the analysis. All the foreground spots were higher than the 95th percentile of the background, and almost all were higher than the 99th percentile. So, I have to confirm your finding, at least qualitatively. Naomi At 09:45 AM 2/2/2009, you wrote: >Hello Naomi, >I am working working with long-oligo data (70-mer) and our array >contains negatives and PM/MM sets for selected oligos. In looking at >intensity of oligos against background it is always higher on the >spot no matter what is spotted... Even in cases where the >corresponding gene is known to be not expressed in one tissue. > >Negatives and MM with 10 mismatches have much higher intensity than >their local background. We have discussed it with colleagues. They >attribute this to two things (maybe both). >1) The materials spotted on the array produce some fluorescence even >when nothing hybridized to them >2) There is (limited) cross-hyb. >Or a combination of both. > >For this reason I am skeptical of using BKG as a criteria to declare >something expressed, unless it is evident that for non-expressed >oligos the intensity is in the order of background. > >Hope this helps >JP > > > > > > >Naomi Altman wrote: >>Thanks Sam, >>Unfortunately, we do not have an appropriate set of negative >>controls on the arrays. We had problems with spike-in controls >>which we had hoped to use to calibrate >>the arrays. But it does seem like a good idea. We are going to >>compare with the background, which is not ideal but gives us some >>idea of the situation. >> >>--Naomi >> >>At 08:55 AM 1/31/2009, Samuel Wuest wrote: >>>Hi Naomi, >>> >>>I am doing work on AffyChips, but my samples are from amplified >>>material with inputs from around 500pico - few nanogram range; I found >>>that the Affy-Algorithmus "MAS5 calls" was not very happy with this >>>type of data and thus compared expression values on the chip with an >>>empirical negative-distribution (background-distribution), please >>>refer to people.brandeis.edu/~dtaylor/Taylor_Papers/BIBE07_PANP.pdf >>>for details (or the Bioconductor-package panp)... I worked on the >>>Arabidopsis chip, and there are "negative" probes on the chip: probes >>>that do not match DNA sequences from newer genome releases anymore... >>>I found that by combining the panp-strategy and the information on >>>negative probes on the chip, I could generate precise and relatively >>>accurate predictions on the expression state of a gene (I can't give >>>you all the details so far). >>> >>>So if there are negative controls/negative probes on the array, you >>>could use them to generate an empirical background-distribution for >>>each array and then compare your other signals to this.... Depends on >>>how many negative probes you'd have... The method works well with the >>>Affy HGU133-series, please refer to the above mentioned sources... >>> >>>Hope this helps?? >>> >>>Best, Sam >>> >>>2009/1/30 Naomi Altman <naomi at="" stat.psu.edu="">: >>> > I have been having an on-going discussion with a colleague >>> about whether he >>> > can say that some genes are "absent" in some tissues based on two-color >>> > microarrays - most recently, Agilent arrays. There are a >>> number of reasons >>> > that he would like to do this which are a mix of biology and QC. >>> > >>> > He wants to use some (arbitrary) normalized expression level, or >>> > unnormalized level above local background or a percentile of >>> the whole array >>> > background or ... >>> > >>> > Any suggestions for papers about this? (We can both think of a >>> dozen ways >>> > to do it, but without experiments to see if they are valid methods, or at >>> > least a paper to >>> > cite, I am reluctant to put the statistical seal of approval on any of >>> > them.) >>> > >>> > Thanks, Naomi >>> > >>> > p.s. In case anyone thinks that high-throughput sequencing is >>> going to end >>> > this type of discussion, have a look at the interesting paper by 't Hoen >>> > comparing sequencing and microarray results. >>> > >>> http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&cmd=Retrieve&li st_uids=18927111 >>> >>> > >>> > Naomi S. Altman 814-865-3791 (voice) >>> > Associate Professor >>> > Dept. of Statistics 814-863-7114 (fax) >>> > Penn State University 814-865-1348 (Statistics) >>> > University Park, PA 16802-2111 >>> > >>> > _______________________________________________ >>> > 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 >>> > >>> > >>> >>>_______________________________________________ >>>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 >> >>Naomi S. Altman 814-865-3791 (voice) >>Associate Professor >>Dept. of Statistics 814-863-7114 (fax) >>Penn State University 814-865-1348 (Statistics) >>University Park, PA 16802-2111 >> >>_______________________________________________ >>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 >> > > >-- >============================= >Juan Pedro Steibel > >Assistant Professor >Statistical Genetics and Genomics > >Department of Animal Science & Department of Fisheries and Wildlife > >Michigan State University >1205-I Anthony Hall >East Lansing, MI >48824 USA >Phone: 1-517-353-5102 >E-mail: steibelj at msu.edu >============================= > Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111