Question: tagwise parameters for negative binomial distribution in edgeR
0
gravatar for Cittaro Davide
5.4 years ago by
Cittaro Davide240 wrote:
Dear list, I have a DGElist object in edgeR, already processed with calcNormFactors, estimateCommonDispersion and estimateTagWiseDispersion. Now, I would like to identify tagwise outliers in my data, I thought I could estimate NB distribution for each tag. Given that a NB is defined by two parameters (r and p), I assume that r = 1/x$tagwise.dispersion, how can I get tagwise p from DGEList dataframe? Thanks d
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ADD COMMENTlink modified 5.4 years ago by Gordon Smyth38k • written 5.4 years ago by Cittaro Davide240
Answer: tagwise parameters for negative binomial distribution in edgeR
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gravatar for Yunshun Chen
5.4 years ago by
Yunshun Chen390
Australia
Yunshun Chen390 wrote:
Dear Davide, I'm not sure what you meant by "identify tagwise outliers". Are you interested in finding genes having outlier tagwise dispersions? If so, you can use the function estimateDisp() with robust=TRUE. The detected outlier genes will be given smaller prior.df in the output. If you use r and p to parameterize the NB distribution, then r=1/phi and p=(phi*mu)/(1+phi*mu), where phi is the dispersion and mu is the mean. The values of mu can be obtained from the fitted values in glmFit(). Best wishes, Yunshun ------------------------------ Message: 23 Date: Wed, 19 Mar 2014 09:33:45 +0100 From: Davide Cittaro <cittaro.davide@hsr.it> To: "bioconductor at r-project.org list" <bioconductor at="" r-project.org=""> Subject: [BioC] tagwise parameters for negative binomial distribution in edgeR Message-ID: <b6a4308d-647d-4239-b176-1eb4c7e12fc2 at="" hsr.it=""> Content-Type: text/plain; charset=us-ascii Dear list, I have a DGElist object in edgeR, already processed with calcNormFactors, estimateCommonDispersion and estimateTagWiseDispersion. Now, I would like to identify tagwise outliers in my data, I thought I could estimate NB distribution for each tag. Given that a NB is defined by two parameters (r and p), I assume that r = 1/x$tagwise.dispersion, how can I get tagwise p from DGEList dataframe? Thanks d ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
ADD COMMENTlink written 5.4 years ago by Yunshun Chen390
Answer: tagwise parameters for negative binomial distribution in edgeR
0
gravatar for Gordon Smyth
5.4 years ago by
Gordon Smyth38k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth38k wrote:
Dear Davide, Do you want to identify tags (genes) with dispersion values that are so high (relative to other genes with similar count sizes) that they should be considered outliers? The easiest way to do this is to use d <- estimateDisp(d, design, robust=TRUE) and then look at the output values for prior.df: summary(d$prior.df) Any tag with a small prior.df is considered an outlier. You can sort tags by their prior.df values to select the most significant outliers. Note that the methodology used by the estimateDisp() robust procedure is more complicated than simply using NB probabilities, because one has to take into acccount the genome-wide distribution of the dispersion values as well as accounting for the fact that the fitted values (p) have been estimated from the same data. The methodology is mostly explained in: http://www.statsci.org/smyth/pubs/edgeRChapterPreprint.pdf http://www.statsci.org/smyth/pubs/RobustEBayesPreprint.pdf Best wishes Gordon > From: Davide Cittaro <cittaro.davide at="" hsr.it=""> > To: "bioconductor at r-project.org list" <bioconductor at="" r-project.org=""> > Subject: [BioC] tagwise parameters for negative binomial distribution > in edgeR > > Dear list, > I have a DGElist object in edgeR, already processed with > calcNormFactors, estimateCommonDispersion and estimateTagWiseDispersion. > Now, I would like to identify tagwise outliers in my data, I thought I > could estimate NB distribution for each tag. Given that a NB is defined > by two parameters (r and p), I assume that r = 1/x$tagwise.dispersion, > how can I get tagwise p from DGEList dataframe? > Thanks > > d ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
ADD COMMENTlink written 5.4 years ago by Gordon Smyth38k
Hi Davide, Just to add another option, there is also estimateGLMRobustDisp(), which is a wrapper for an iteratively re-weighted scheme that down weights outliers -- effectively using a constant prior degrees of freedom. This was developed independently of estimateDisp() from a different perspective. But, what this gives is a matrix of weights (so, one for each observation, not just for each tag) where identified outliers should exhibit lower weights. So, you could use this to identify outliers observation-wise or tag-wise (e.g. take column sum of weights). You'd want >= 3 replicates per condition for this one though. In code, you could do something like: d <- estimateGLMRobustDisp(d, design) summary(d$weights) More details can be found at: http://arxiv.org/abs/1312.3382 http://imlspenticton.uzh.ch/robinson_lab/edgeR_robust/supplement.pdf (in particular, Supplementary Figure 8, which shows ROC curves for ability to separate [simulated] outliers by weights/residuals and yet another option is DESeq2's Cook's [observation-wise or max-by-tag] distance; we don't have a curve for estimateDisp!) Best regards, Mark ---------- Prof. Dr. Mark Robinson Bioinformatics, Institute of Molecular Life Sciences University of Zurich http://ow.ly/riRea On 20.03.2014, at 01:04, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > Dear Davide, > > Do you want to identify tags (genes) with dispersion values that are so high (relative to other genes with similar count sizes) that they should be considered outliers? > > The easiest way to do this is to use > > d <- estimateDisp(d, design, robust=TRUE) > > and then look at the output values for prior.df: > > summary(d$prior.df) > > Any tag with a small prior.df is considered an outlier. You can sort tags by their prior.df values to select the most significant outliers. > > Note that the methodology used by the estimateDisp() robust procedure is more complicated than simply using NB probabilities, because one has to take into acccount the genome-wide distribution of the dispersion values as well as accounting for the fact that the fitted values (p) have been estimated from the same data. The methodology is mostly explained in: > > http://www.statsci.org/smyth/pubs/edgeRChapterPreprint.pdf > http://www.statsci.org/smyth/pubs/RobustEBayesPreprint.pdf > > Best wishes > Gordon > >> From: Davide Cittaro <cittaro.davide at="" hsr.it=""> >> To: "bioconductor at r-project.org list" <bioconductor at="" r-project.org=""> >> Subject: [BioC] tagwise parameters for negative binomial distribution >> in edgeR >> >> Dear list, > >> I have a DGElist object in edgeR, already processed with calcNormFactors, estimateCommonDispersion and estimateTagWiseDispersion. Now, I would like to identify tagwise outliers in my data, I thought I could estimate NB distribution for each tag. Given that a NB is defined by two parameters (r and p), I assume that r = 1/x$tagwise.dispersion, how can I get tagwise p from DGEList dataframe? > >> Thanks >> >> d > > ______________________________________________________________________ > The information in this email is confidential and intend...{{dropped:4}} > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD REPLYlink written 5.4 years ago by Mark Robinson870
Hi Mark, Thanks for the comment. On 20/mar/2014, at 14:58, Mark Robinson <mark.robinson at="" imls.uzh.ch=""> wrote: > You'd want >= 3 replicates per condition for this one though. > Unfortunately my dataset is made of >200 unique samples :-( What should I expect (except for a ton of warnings...)? I'm going to read everything you (and Gordon) posted. Thanks d > In code, you could do something like: > > d <- estimateGLMRobustDisp(d, design) > summary(d$weights) > > More details can be found at: > > http://arxiv.org/abs/1312.3382 > > http://imlspenticton.uzh.ch/robinson_lab/edgeR_robust/supplement.pdf > (in particular, Supplementary Figure 8, which shows ROC curves for ability to separate [simulated] outliers by weights/residuals and yet another option is DESeq2's Cook's [observation-wise or max-by-tag] distance; we don't have a curve for estimateDisp!) > > > Best regards, Mark > > > ---------- > Prof. Dr. Mark Robinson > Bioinformatics, Institute of Molecular Life Sciences > University of Zurich > http://ow.ly/riRea > > > > > > > On 20.03.2014, at 01:04, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > >> Dear Davide, >> >> Do you want to identify tags (genes) with dispersion values that are so high (relative to other genes with similar count sizes) that they should be considered outliers? >> >> The easiest way to do this is to use >> >> d <- estimateDisp(d, design, robust=TRUE) >> >> and then look at the output values for prior.df: >> >> summary(d$prior.df) >> >> Any tag with a small prior.df is considered an outlier. You can sort tags by their prior.df values to select the most significant outliers. >> >> Note that the methodology used by the estimateDisp() robust procedure is more complicated than simply using NB probabilities, because one has to take into acccount the genome-wide distribution of the dispersion values as well as accounting for the fact that the fitted values (p) have been estimated from the same data. The methodology is mostly explained in: >> >> http://www.statsci.org/smyth/pubs/edgeRChapterPreprint.pdf >> http://www.statsci.org/smyth/pubs/RobustEBayesPreprint.pdf >> >> Best wishes >> Gordon >> >>> From: Davide Cittaro <cittaro.davide at="" hsr.it=""> >>> To: "bioconductor at r-project.org list" <bioconductor at="" r-project.org=""> >>> Subject: [BioC] tagwise parameters for negative binomial distribution >>> in edgeR >>> >>> Dear list, >> >>> I have a DGElist object in edgeR, already processed with calcNormFactors, estimateCommonDispersion and estimateTagWiseDispersion. Now, I would like to identify tagwise outliers in my data, I thought I could estimate NB distribution for each tag. Given that a NB is defined by two parameters (r and p), I assume that r = 1/x$tagwise.dispersion, how can I get tagwise p from DGEList dataframe? >> >>> Thanks >>> >>> d >> >> ______________________________________________________________________ >> The information in this email is confidential and intend...{{dropped:4}} >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD REPLYlink written 5.4 years ago by Cittaro Davide240
Hi Mark, how would the presence of observation outliers potentially causing dispersion outliers be handled in voom? Many thanks, Ina ----- Original Message ----- From: "Mark Robinson" <mark.robinson@imls.uzh.ch> To: "Davide Cittaro" <cittaro.davide at="" hsr.it=""> Cc: "Bioconductor mailing list" <bioconductor at="" r-project.org="">, "xiaobei.zhou at uzh.ch Zhou" <xiaobei.zhou at="" uzh.ch="">, "Gordon Smyth" <smyth at="" wehi.edu.au=""> Sent: Thursday, March 20, 2014 9:58:05 AM Subject: Re: [BioC] tagwise parameters for negative binomial distribution in edgeR Hi Davide, Just to add another option, there is also estimateGLMRobustDisp(), which is a wrapper for an iteratively re-weighted scheme that down weights outliers -- effectively using a constant prior degrees of freedom. This was developed independently of estimateDisp() from a different perspective. But, what this gives is a matrix of weights (so, one for each observation, not just for each tag) where identified outliers should exhibit lower weights. So, you could use this to identify outliers observation-wise or tag-wise (e.g. take column sum of weights). You'd want >= 3 replicates per condition for this one though. In code, you could do something like: d <- estimateGLMRobustDisp(d, design) summary(d$weights) More details can be found at: http://arxiv.org/abs/1312.3382 http://imlspenticton.uzh.ch/robinson_lab/edgeR_robust/supplement.pdf (in particular, Supplementary Figure 8, which shows ROC curves for ability to separate [simulated] outliers by weights/residuals and yet another option is DESeq2's Cook's [observation-wise or max-by-tag] distance; we don't have a curve for estimateDisp!) Best regards, Mark ---------- Prof. Dr. Mark Robinson Bioinformatics, Institute of Molecular Life Sciences University of Zurich http://ow.ly/riRea On 20.03.2014, at 01:04, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > Dear Davide, > > Do you want to identify tags (genes) with dispersion values that are so high (relative to other genes with similar count sizes) that they should be considered outliers? > > The easiest way to do this is to use > > d <- estimateDisp(d, design, robust=TRUE) > > and then look at the output values for prior.df: > > summary(d$prior.df) > > Any tag with a small prior.df is considered an outlier. You can sort tags by their prior.df values to select the most significant outliers. > > Note that the methodology used by the estimateDisp() robust procedure is more complicated than simply using NB probabilities, because one has to take into acccount the genome-wide distribution of the dispersion values as well as accounting for the fact that the fitted values (p) have been estimated from the same data. The methodology is mostly explained in: > > http://www.statsci.org/smyth/pubs/edgeRChapterPreprint.pdf > http://www.statsci.org/smyth/pubs/RobustEBayesPreprint.pdf > > Best wishes > Gordon > >> From: Davide Cittaro <cittaro.davide at="" hsr.it=""> >> To: "bioconductor at r-project.org list" <bioconductor at="" r-project.org=""> >> Subject: [BioC] tagwise parameters for negative binomial distribution >> in edgeR >> >> Dear list, > >> I have a DGElist object in edgeR, already processed with calcNormFactors, estimateCommonDispersion and estimateTagWiseDispersion. Now, I would like to identify tagwise outliers in my data, I thought I could estimate NB distribution for each tag. Given that a NB is defined by two parameters (r and p), I assume that r = 1/x$tagwise.dispersion, how can I get tagwise p from DGEList dataframe? > >> Thanks >> >> d > > ______________________________________________________________________ > The information in this email is confidential and intend...{{dropped:4}} > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor _______________________________________________ Bioconductor mailing list Bioconductor at r-project.org https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD REPLYlink written 5.4 years ago by Ina Hoeschele610
Hi Ina, I don't think voom has any special consideration for observation outliers, but limma's 'eBayes' function has a 'robust' argument which I believe has the same effect as the corresponding argument in edgeR's 'estimateDisp', i.e. dealing with outlier tags that have abnormally high (or low) variance. -Ryan On Fri 21 Mar 2014 08:48:23 AM PDT, Ina Hoeschele wrote: > Hi Mark, > how would the presence of observation outliers potentially causing dispersion outliers be handled in voom? > Many thanks, Ina > > > ----- Original Message ----- > From: "Mark Robinson" <mark.robinson at="" imls.uzh.ch=""> > To: "Davide Cittaro" <cittaro.davide at="" hsr.it=""> > Cc: "Bioconductor mailing list" <bioconductor at="" r-project.org="">, "xiaobei.zhou at uzh.ch Zhou" <xiaobei.zhou at="" uzh.ch="">, "Gordon Smyth" <smyth at="" wehi.edu.au=""> > Sent: Thursday, March 20, 2014 9:58:05 AM > Subject: Re: [BioC] tagwise parameters for negative binomial distribution in edgeR > > Hi Davide, > > Just to add another option, there is also estimateGLMRobustDisp(), which is a wrapper for an iteratively re-weighted scheme that down weights outliers -- effectively using a constant prior degrees of freedom. This was developed independently of estimateDisp() from a different perspective. But, what this gives is a matrix of weights (so, one for each observation, not just for each tag) where identified outliers should exhibit lower weights. So, you could use this to identify outliers observation-wise or tag-wise (e.g. take column sum of weights). You'd want >= 3 replicates per condition for this one though. > > In code, you could do something like: > > d <- estimateGLMRobustDisp(d, design) > summary(d$weights) > > More details can be found at: > > http://arxiv.org/abs/1312.3382 > > http://imlspenticton.uzh.ch/robinson_lab/edgeR_robust/supplement.pdf > (in particular, Supplementary Figure 8, which shows ROC curves for ability to separate [simulated] outliers by weights/residuals and yet another option is DESeq2's Cook's [observation-wise or max-by-tag] distance; we don't have a curve for estimateDisp!) > > > Best regards, Mark > > > ---------- > Prof. Dr. Mark Robinson > Bioinformatics, Institute of Molecular Life Sciences > University of Zurich > http://ow.ly/riRea > > > > > > > On 20.03.2014, at 01:04, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > >> Dear Davide, >> >> Do you want to identify tags (genes) with dispersion values that are so high (relative to other genes with similar count sizes) that they should be considered outliers? >> >> The easiest way to do this is to use >> >> d <- estimateDisp(d, design, robust=TRUE) >> >> and then look at the output values for prior.df: >> >> summary(d$prior.df) >> >> Any tag with a small prior.df is considered an outlier. You can sort tags by their prior.df values to select the most significant outliers. >> >> Note that the methodology used by the estimateDisp() robust procedure is more complicated than simply using NB probabilities, because one has to take into acccount the genome-wide distribution of the dispersion values as well as accounting for the fact that the fitted values (p) have been estimated from the same data. The methodology is mostly explained in: >> >> http://www.statsci.org/smyth/pubs/edgeRChapterPreprint.pdf >> http://www.statsci.org/smyth/pubs/RobustEBayesPreprint.pdf >> >> Best wishes >> Gordon >> >>> From: Davide Cittaro <cittaro.davide at="" hsr.it=""> >>> To: "bioconductor at r-project.org list" <bioconductor at="" r-project.org=""> >>> Subject: [BioC] tagwise parameters for negative binomial distribution >>> in edgeR >>> >>> Dear list, >> >>> I have a DGElist object in edgeR, already processed with calcNormFactors, estimateCommonDispersion and estimateTagWiseDispersion. Now, I would like to identify tagwise outliers in my data, I thought I could estimate NB distribution for each tag. Given that a NB is defined by two parameters (r and p), I assume that r = 1/x$tagwise.dispersion, how can I get tagwise p from DGEList dataframe? >> >>> Thanks >>> >>> d >> >> ______________________________________________________________________ >> The information in this email is confidential and intend...{{dropped:4}} >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD REPLYlink written 5.4 years ago by Ryan C. Thompson7.3k
Dear Gordon, thanks for the answer. On 20/mar/2014, at 01:04, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > Dear Davide, > > Do you want to identify tags (genes) with dispersion values that are so > high (relative to other genes with similar count sizes) that they should > be considered outliers? Mmm, actually I would like to identify the sample that is an outlier for a specific gene, that's why I thought I could focus on tagwise distribution. > > The easiest way to do this is to use > > d <- estimateDisp(d, design, robust=TRUE) > > and then look at the output values for prior.df: > > summary(d$prior.df) > > Any tag with a small prior.df is considered an outlier. You can sort tags > by their prior.df values to select the most significant outliers. Does this identify a tag that is an outlier over all samples? > > Note that the methodology used by the estimateDisp() robust procedure is > more complicated than simply using NB probabilities, because one has to > take into acccount the genome-wide distribution of the dispersion values > as well as accounting for the fact that the fitted values (p) have been > estimated from the same data. The methodology is mostly explained in: > > http://www.statsci.org/smyth/pubs/edgeRChapterPreprint.pdf > http://www.statsci.org/smyth/pubs/RobustEBayesPreprint.pdf > I have a lot to read :-) Thanks d > Best wishes > Gordon > >> From: Davide Cittaro <cittaro.davide at="" hsr.it=""> >> To: "bioconductor at r-project.org list" <bioconductor at="" r-project.org=""> >> Subject: [BioC] tagwise parameters for negative binomial distribution >> in edgeR >> >> Dear list, > >> I have a DGElist object in edgeR, already processed with >> calcNormFactors, estimateCommonDispersion and estimateTagWiseDispersion. >> Now, I would like to identify tagwise outliers in my data, I thought I >> could estimate NB distribution for each tag. Given that a NB is defined >> by two parameters (r and p), I assume that r = 1/x$tagwise.dispersion, >> how can I get tagwise p from DGEList dataframe? > >> Thanks >> >> d > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:6}}
ADD REPLYlink written 5.4 years ago by Cittaro Davide240
On Thu, 20 Mar 2014, Davide Cittaro wrote: > On 20/mar/2014, at 01:04, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > >> Do you want to identify tags (genes) with dispersion values that are so >> high (relative to other genes with similar count sizes) that they should >> be considered outliers? > > Mmm, actually I would like to identify the sample that is an outlier for > a specific gene, that's why I thought I could focus on tagwise > distribution. See Mark Robinson's post. It depends on your purpose however. Do you want to downweight/ignore outliers, or do you want to identify them because they are interesting? >> The easiest way to do this is to use >> >> d <- estimateDisp(d, design, robust=TRUE) >> >> and then look at the output values for prior.df: >> >> summary(d$prior.df) >> >> Any tag with a small prior.df is considered an outlier. You can sort tags >> by their prior.df values to select the most significant outliers. > > Does this identify a tag that is an outlier over all samples? Basically yes. We distinguish dispersion outliers and observation outliers. An observation outlier is an individual count that is an outlier (relative to other counts for the same gene). A dispersion outlier is a gene that shows much more variability between replicates than other genes at the same cpm level. A dispersion outlier may arise from one or more observation outliers, but not necessarily. It could also arise from systematically larger variability. Gordon ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
ADD REPLYlink written 5.4 years ago by Gordon Smyth38k
On 21/mar/2014, at 01:47, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >> >> Mmm, actually I would like to identify the sample that is an outlier for >> a specific gene, that's why I thought I could focus on tagwise >> distribution. > > See Mark Robinson's post. > > It depends on your purpose however. Do you want to downweight/ignore > outliers, or do you want to identify them because they are interesting? In this case outliers may be relevant, especially the less represented. I'm running the estimateGLMRobustDisp approach (although it takes a loong time) >>> Any tag with a small prior.df is considered an outlier. You can sort tags >>> by their prior.df values to select the most significant outliers. >> >> Does this identify a tag that is an outlier over all samples? > > Basically yes. We distinguish dispersion outliers and observation > outliers. An observation outlier is an individual count that is an > outlier (relative to other counts for the same gene). A dispersion > outlier is a gene that shows much more variability between replicates than > other genes at the same cpm level. A dispersion outlier may arise from > one or more observation outliers, but not necessarily. It could also > arise from systematically larger variability. Thanks for the explanation. Best, d
ADD REPLYlink written 5.4 years ago by Cittaro Davide240
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