edgeR/DESeq for ChIP-seq analysis
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@cittaro-davide-5375
Last seen 10.3 years ago
Hi there, I'm writing to the list to have your comment about the possibility of using edgeR or DESeq for the analysis of ChIP-seq samples. Standard approaches to ChIP-seq analysis (relying on external software such as MACS) do not make analysis of replicates easy. I've seen people looking for peaks and then compare the common/differential intervals between replicates in case/control design. I wonder if a more general approach may work (and I'm going to test this anyway...). Since the negative binomial model stands for ChIP-seq analysis, both edgeR and DESeq should work well. One can use external software to identify regions and compute the union of all regions as it was a "gene list". From that point on, the pipeline should not differ from standard gene expression analysis. What do you think? d /* Davide Cittaro, PhD Coordinator of Bioinformatics Core Center for Translational Genomics and Bioinformatics Ospedale San Raffaele Via Olgettina 58 20132 Milano Italy Office: +39 02 26439140 Mail: cittaro.davide at hsr.it Skype: daweonline */
edgeR DESeq edgeR DESeq • 4.7k views
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Mark Robinson ▴ 880
@mark-robinson-4908
Last seen 6.1 years ago
Dear Davide, Indeed, edgeR and DESeq can be (and have been) used in this mode. We published something recently on this: http://www.ncbi.nlm.nih.gov/pubmed/22879430 http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.pdf You can apply that approach regardless of copy number being a factor ... basically, we counted tiled bins of the genome, but yes, you could focus in on regions of interest. The function abcdDNA() is really just a wrapper for the edgeR GLM. As usual, "normalization" can be delicate, depending on the type of data. Also note that the DiffBind package already does something similar, but has a lot more machinery to collect and organize the sets of enriched regions. Hope that helps. Best, Mark On 08.11.2012, at 08:37, Cittaro Davide wrote: > Hi there, I'm writing to the list to have your comment about the possibility of using edgeR or DESeq for the analysis of ChIP-seq samples. > Standard approaches to ChIP-seq analysis (relying on external software such as MACS) do not make analysis of replicates easy. I've seen people looking for peaks and then compare the common/differential intervals between replicates in case/control design. I wonder if a more general approach may work (and I'm going to test this anyway...). > Since the negative binomial model stands for ChIP-seq analysis, both edgeR and DESeq should work well. One can use external software to identify regions and compute the union of all regions as it was a "gene list". From that point on, the pipeline should not differ from standard gene expression analysis. > What do you think? > > d > /* > Davide Cittaro, PhD > > Coordinator of Bioinformatics Core > Center for Translational Genomics and Bioinformatics > Ospedale San Raffaele > Via Olgettina 58 > 20132 Milano > Italy > > Office: +39 02 26439140 > Mail: cittaro.davide at hsr.it > Skype: daweonline > */ > > _______________________________________________ > 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 ---------- Prof. Dr. Mark Robinson Bioinformatics Institute of Molecular Life Sciences University of Zurich Winterthurerstrasse 190 8057 Zurich Switzerland v: +41 44 635 4848 f: +41 44 635 6898 e: mark.robinson at imls.uzh.ch o: Y11-J-16 w: http://tiny.cc/mrobin ---------- http://www.fgcz.ch/Bioconductor2012
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Dear Mark On Nov 8, 2012, at 8:53 AM, Mark Robinson <mark.robinson at="" imls.uzh.ch=""> wrote: > Dear Davide, > > Indeed, edgeR and DESeq can be (and have been) used in this mode. We published something recently on this: > > http://www.ncbi.nlm.nih.gov/pubmed/22879430 > http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.pdf > I've missed that :-( Thanks for the paper > You can apply that approach regardless of copy number being a factor ... basically, we counted tiled bins of the genome, but yes, you could focus in on regions of interest. The function abcdDNA() is really just a wrapper for the edgeR GLM. As usual, "normalization" can be delicate, depending on the type of data. > > Also note that the DiffBind package already does something similar, but has a lot more machinery to collect and organize the sets of enriched regions. I wonder why I've never used DiffBind before :-) > > Hope that helps. It does, thanks! d > > Best, Mark > > > On 08.11.2012, at 08:37, Cittaro Davide wrote: > >> Hi there, I'm writing to the list to have your comment about the possibility of using edgeR or DESeq for the analysis of ChIP-seq samples. >> Standard approaches to ChIP-seq analysis (relying on external software such as MACS) do not make analysis of replicates easy. I've seen people looking for peaks and then compare the common/differential intervals between replicates in case/control design. I wonder if a more general approach may work (and I'm going to test this anyway...). >> Since the negative binomial model stands for ChIP-seq analysis, both edgeR and DESeq should work well. One can use external software to identify regions and compute the union of all regions as it was a "gene list". From that point on, the pipeline should not differ from standard gene expression analysis. >> What do you think? >> >> d >> /* >> Davide Cittaro, PhD >> >> Coordinator of Bioinformatics Core >> Center for Translational Genomics and Bioinformatics >> Ospedale San Raffaele >> Via Olgettina 58 >> 20132 Milano >> Italy >> >> Office: +39 02 26439140 >> Mail: cittaro.davide at hsr.it >> Skype: daweonline >> */ >> >> _______________________________________________ >> 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 > > ---------- > Prof. Dr. Mark Robinson > Bioinformatics > Institute of Molecular Life Sciences > University of Zurich > Winterthurerstrasse 190 > 8057 Zurich > Switzerland > > v: +41 44 635 4848 > f: +41 44 635 6898 > e: mark.robinson at imls.uzh.ch > o: Y11-J-16 > w: http://tiny.cc/mrobin > > ---------- > http://www.fgcz.ch/Bioconductor2012 > > /* Davide Cittaro, PhD Coordinator of Bioinformatics Core Center for Translational Genomics and Bioinformatics Ospedale San Raffaele Via Olgettina 58 20132 Milano Italy Office: +39 02 26439140 Mail: cittaro.davide at hsr.it Skype: daweonline */
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Hi, I want to continue this discussion. I saw in some papers, people used edgeR and DESeq to analysis differentially bound between different sample groups following ChIP- seq. But most of them are studying transcription factors. Is it the case for histone modifications ChIP-seq (H3K4me1, H3K4me2 or H3k9me3)? Regards, Sheng On Thu, Nov 8, 2012 at 8:59 AM, Cittaro Davide <cittaro.davide@hsr.it>wrote: > Dear Mark > > On Nov 8, 2012, at 8:53 AM, Mark Robinson <mark.robinson@imls.uzh.ch> > wrote: > > > Dear Davide, > > > > Indeed, edgeR and DESeq can be (and have been) used in this mode. We > published something recently on this: > > > > http://www.ncbi.nlm.nih.gov/pubmed/22879430 > > http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.pdf > > > > I've missed that :-( Thanks for the paper > > > You can apply that approach regardless of copy number being a factor ... > basically, we counted tiled bins of the genome, but yes, you could focus in > on regions of interest. The function abcdDNA() is really just a wrapper > for the edgeR GLM. As usual, "normalization" can be delicate, depending on > the type of data. > > > > Also note that the DiffBind package already does something similar, but > has a lot more machinery to collect and organize the sets of enriched > regions. > > I wonder why I've never used DiffBind before :-) > > > > > Hope that helps. > > It does, thanks! > > d > > > > > Best, Mark > > > > > > On 08.11.2012, at 08:37, Cittaro Davide wrote: > > > >> Hi there, I'm writing to the list to have your comment about the > possibility of using edgeR or DESeq for the analysis of ChIP-seq samples. > >> Standard approaches to ChIP-seq analysis (relying on external software > such as MACS) do not make analysis of replicates easy. I've seen people > looking for peaks and then compare the common/differential intervals > between replicates in case/control design. I wonder if a more general > approach may work (and I'm going to test this anyway...). > >> Since the negative binomial model stands for ChIP-seq analysis, both > edgeR and DESeq should work well. One can use external software to identify > regions and compute the union of all regions as it was a "gene list". From > that point on, the pipeline should not differ from standard gene expression > analysis. > >> What do you think? > >> > >> d > >> /* > >> Davide Cittaro, PhD > >> > >> Coordinator of Bioinformatics Core > >> Center for Translational Genomics and Bioinformatics > >> Ospedale San Raffaele > >> Via Olgettina 58 > >> 20132 Milano > >> Italy > >> > >> Office: +39 02 26439140 > >> Mail: cittaro.davide@hsr.it > >> Skype: daweonline > >> */ > >> > >> _______________________________________________ > >> 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 > > > > ---------- > > Prof. Dr. Mark Robinson > > Bioinformatics > > Institute of Molecular Life Sciences > > University of Zurich > > Winterthurerstrasse 190 > > 8057 Zurich > > Switzerland > > > > v: +41 44 635 4848 > > f: +41 44 635 6898 > > e: mark.robinson@imls.uzh.ch > > o: Y11-J-16 > > w: http://tiny.cc/mrobin > > > > ---------- > > http://www.fgcz.ch/Bioconductor2012 > > > > > > /* > Davide Cittaro, PhD > > Coordinator of Bioinformatics Core > Center for Translational Genomics and Bioinformatics > Ospedale San Raffaele > Via Olgettina 58 > 20132 Milano > Italy > > Office: +39 02 26439140 > Mail: cittaro.davide@hsr.it > Skype: daweonline > */ > > _______________________________________________ > 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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On Nov 8, 2012, at 9:29 AM, sheng zhao <harryzs1981 at="" gmail.com=""> wrote: > > Is it the case for histone modifications ChIP-seq (H3K4me1, H3K4me2 or > H3k9me3)? > AFAIK the NB model stands also for those. d > Regards, > Sheng > > > On Thu, Nov 8, 2012 at 8:59 AM, Cittaro Davide <cittaro.davide at="" hsr.it="">wrote: > >> Dear Mark >> >> On Nov 8, 2012, at 8:53 AM, Mark Robinson <mark.robinson at="" imls.uzh.ch=""> >> wrote: >> >>> Dear Davide, >>> >>> Indeed, edgeR and DESeq can be (and have been) used in this mode. We >> published something recently on this: >>> >>> http://www.ncbi.nlm.nih.gov/pubmed/22879430 >>> http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.pdf >>> >> >> I've missed that :-( Thanks for the paper >> >>> You can apply that approach regardless of copy number being a factor ... >> basically, we counted tiled bins of the genome, but yes, you could focus in >> on regions of interest. The function abcdDNA() is really just a wrapper >> for the edgeR GLM. As usual, "normalization" can be delicate, depending on >> the type of data. >>> >>> Also note that the DiffBind package already does something similar, but >> has a lot more machinery to collect and organize the sets of enriched >> regions. >> >> I wonder why I've never used DiffBind before :-) >> >>> >>> Hope that helps. >> >> It does, thanks! >> >> d >> >>> >>> Best, Mark >>> >>> >>> On 08.11.2012, at 08:37, Cittaro Davide wrote: >>> >>>> Hi there, I'm writing to the list to have your comment about the >> possibility of using edgeR or DESeq for the analysis of ChIP-seq samples. >>>> Standard approaches to ChIP-seq analysis (relying on external software >> such as MACS) do not make analysis of replicates easy. I've seen people >> looking for peaks and then compare the common/differential intervals >> between replicates in case/control design. I wonder if a more general >> approach may work (and I'm going to test this anyway...). >>>> Since the negative binomial model stands for ChIP-seq analysis, both >> edgeR and DESeq should work well. One can use external software to identify >> regions and compute the union of all regions as it was a "gene list". From >> that point on, the pipeline should not differ from standard gene expression >> analysis. >>>> What do you think? >>>> >>>> d >>>> /* >>>> Davide Cittaro, PhD >>>> >>>> Coordinator of Bioinformatics Core >>>> Center for Translational Genomics and Bioinformatics >>>> Ospedale San Raffaele >>>> Via Olgettina 58 >>>> 20132 Milano >>>> Italy >>>> >>>> Office: +39 02 26439140 >>>> Mail: cittaro.davide at hsr.it >>>> Skype: daweonline >>>> */ >>>> >>>> _______________________________________________ >>>> 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 >>> >>> ---------- >>> Prof. Dr. Mark Robinson >>> Bioinformatics >>> Institute of Molecular Life Sciences >>> University of Zurich >>> Winterthurerstrasse 190 >>> 8057 Zurich >>> Switzerland >>> >>> v: +41 44 635 4848 >>> f: +41 44 635 6898 >>> e: mark.robinson at imls.uzh.ch >>> o: Y11-J-16 >>> w: http://tiny.cc/mrobin >>> >>> ---------- >>> http://www.fgcz.ch/Bioconductor2012 >>> >>> >> >> /* >> Davide Cittaro, PhD >> >> Coordinator of Bioinformatics Core >> Center for Translational Genomics and Bioinformatics >> Ospedale San Raffaele >> Via Olgettina 58 >> 20132 Milano >> Italy >> >> Office: +39 02 26439140 >> Mail: cittaro.davide at hsr.it >> Skype: daweonline >> */ >> >> _______________________________________________ >> 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 >> > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 /* Davide Cittaro, PhD Coordinator of Bioinformatics Core Center for Translational Genomics and Bioinformatics Ospedale San Raffaele Via Olgettina 58 20132 Milano Italy Office: +39 02 26439140 Mail: cittaro.davide at hsr.it Skype: daweonline */
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Rory Stark ▴ 100
@rory-stark-4919
Last seen 10.3 years ago
As Mark said, DiffBind provides a straightforward workflow for this type of ChIP-seq analysis, with all the statistical heavy lifting done by edgeR and/or DESeq. Regarding histone marks, we have had success using DiffBind to analyze wider regions of enrichment. For example, in Chandra et al (Mol Cell 2012 47:2), we found biologically meaningful differences in five histone marks over enrichment regions as wide as 500Kb (cf Figure 3d). The tricky part with this type of enrichment is in peak calling, as the most popular peak callers (esp. ones that rely on strand information) assume that the enriched area (peak) is shorter than the sequenced fragment length. There are a number of peak callers that are designed to find wider areas of enrichment. We have used these peak callers, or avoided peak calling all together using general windowing schemes or genomic annotations (eg windows oriented around transcription start sites to capture binding profiles in promoter regions). Cheers- Rory ---------------------------------------------------------------------- ------ Dr. Rory Stark Principal Bioinformatics Analyst Cancer Research UK Cambridge Research Institute Robinson Way Cambridge CB2 0RE United Kingdom +44 1223 404 311 rory.stark@cancer.org.uk ---------------------------------------------------------------------- ------ > Hi, > I want to continue this discussion. > I saw in some papers, people used edgeR and DESeq to analysis > differentially bound between different sample groups following ChIP- seq. > But most of them are studying transcription factors. > Is it the case for histone modifications ChIP-seq (H3K4me1, H3K4me2 or> H3k9me3)? > Regards, > Sheng On Thu, Nov 8, 2012 at 8:59 AM, Cittaro Davide <cittaro.davide@hsr.it>wrote: > Dear Mark > > On Nov 8, 2012, at 8:53 AM, Mark Robinson <mark.robinson@imls.uzh.ch> > wrote: > > > Dear Davide, > > > > Indeed, edgeR and DESeq can be (and have been) used in this mode. We > published something recently on this: > > > > http://www.ncbi.nlm.nih.gov/pubmed/22879430 > > http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.pdf > > > > I've missed that :-( Thanks for the paper > > > You can apply that approach regardless of copy number being a factor ... > basically, we counted tiled bins of the genome, but yes, you could focus in > on regions of interest. The function abcdDNA() is really just a wrapper > for the edgeR GLM. As usual, "normalization" can be delicate, depending on > the type of data. > > > > Also note that the DiffBind package already does something similar, but > has a lot more machinery to collect and organize the sets of enriched > regions. > > I wonder why I've never used DiffBind before :-) > > > > > Hope that helps. > > It does, thanks! > > d NOTICE AND DISCLAIMER This e-mail (including any attachments) is intended for ...{{dropped:19}}
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Hi Rory, Mark, and others - sorry if this is an ignorant question, is there are already something published where the peak calling was side-stepped, and edgeR or DESeq were simply called on the counts from all possible windows (i.e. all possible mid-points, all plausible widths), and useful results were gotten? Best wishes Wolfgang PS Rory - very nice paper of yours, congratulations! Il giorno Nov 8, 2012, alle ore 1:15 PM, Rory Stark <rory.stark at="" cancer.org.uk=""> ha scritto: > > As Mark said, DiffBind provides a straightforward workflow for this type of ChIP-seq analysis, with all the statistical heavy lifting done by edgeR and/or DESeq. > > Regarding histone marks, we have had success using DiffBind to analyze wider regions of enrichment. For example, in Chandra et al (Mol Cell 2012 47:2), we found biologically meaningful differences in five histone marks over enrichment regions as wide as 500Kb (cf Figure 3d). The tricky part with this type of enrichment is in peak calling, as the most popular peak callers (esp. ones that rely on strand information) assume that the enriched area (peak) is shorter than the sequenced fragment length. There are a number of peak callers that are designed to find wider areas of enrichment. We have used these peak callers, or avoided peak calling all together using general windowing schemes or genomic annotations (eg windows oriented around transcription start sites to capture binding profiles in promoter regions). > > Cheers- > Rory > > -------------------------------------------------------------------- -------- > Dr. Rory Stark > > Principal Bioinformatics Analyst > > Cancer Research UK > Cambridge Research Institute > Robinson Way > Cambridge CB2 0RE > United Kingdom > +44 1223 404 311 > > rory.stark at cancer.org.uk > -------------------------------------------------------------------- -------- > >> Hi, > > >> I want to continue this discussion. > >> I saw in some papers, people used edgeR and DESeq to analysis >> differentially bound between different sample groups following ChIP-seq. > >> But most of them are studying transcription factors. > >> Is it the case for histone modifications ChIP-seq (H3K4me1, H3K4me2 or> H3k9me3)? > >> Regards, >> Sheng > > > On Thu, Nov 8, 2012 at 8:59 AM, Cittaro Davide <cittaro.davide at="" hsr.it="">wrote: > >> Dear Mark >> >> On Nov 8, 2012, at 8:53 AM, Mark Robinson <mark.robinson at="" imls.uzh.ch=""> >> wrote: >> >>> Dear Davide, >>> >>> Indeed, edgeR and DESeq can be (and have been) used in this mode. We >> published something recently on this: >>> >>> http://www.ncbi.nlm.nih.gov/pubmed/22879430 >>> http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.pdf >>> >> >> I've missed that :-( Thanks for the paper >> >>> You can apply that approach regardless of copy number being a factor ... >> basically, we counted tiled bins of the genome, but yes, you could focus in >> on regions of interest. The function abcdDNA() is really just a wrapper >> for the edgeR GLM. As usual, "normalization" can be delicate, depending on >> the type of data. >>> >>> Also note that the DiffBind package already does something similar, but >> has a lot more machinery to collect and organize the sets of enriched >> regions. >> >> I wonder why I've never used DiffBind before :-) >> >>> >>> Hope that helps. >> >> It does, thanks! >> >> d > > NOTICE AND DISCLAIMER > This e-mail (including any attachments) is intended for ...{{dropped:19}} > > _______________________________________________ > 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
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@gordon-smyth
Last seen 4 minutes ago
WEHI, Melbourne, Australia
Dear Davide, My understanding is that the edgeR glm functionality was used to evaluate statistical significance of differential binding in Chandra et al (2012), although edgeR is not mentioned in the paper. In other words, DiffBind organized the counts and edgeR did the parameter estimation and statistical tests. Best wishes Gordon > Date: Thu, 8 Nov 2012 12:15:35 +0000 > From: Rory Stark <rory.stark at="" cancer.org.uk=""> > To: "bioconductor at r-project.org" <bioconductor at="" r-project.org=""> > Subject: Re: [BioC] edgeR/DESeq for ChIP-seq analysis > > > As Mark said, DiffBind provides a straightforward workflow for this type > of ChIP-seq analysis, with all the statistical heavy lifting done by > edgeR and/or DESeq. > > Regarding histone marks, we have had success using DiffBind to analyze > wider regions of enrichment. For example, in Chandra et al (Mol Cell > 2012 47:2), we found biologically meaningful differences in five histone > marks over enrichment regions as wide as 500Kb (cf Figure 3d). The > tricky part with this type of enrichment is in peak calling, as the most > popular peak callers (esp. ones that rely on strand information) assume > that the enriched area (peak) is shorter than the sequenced fragment > length. There are a number of peak callers that are designed to find > wider areas of enrichment. We have used these peak callers, or avoided > peak calling all together using general windowing schemes or genomic > annotations (eg windows oriented around transcription start sites to > capture binding profiles in promoter regions). > > Cheers- > Rory > > -------------------------------------------------------------------- -------- > Dr. Rory Stark > > Principal Bioinformatics Analyst > > Cancer Research UK > Cambridge Research Institute > Robinson Way > Cambridge CB2 0RE > United Kingdom > +44 1223 404 311 > > rory.stark at cancer.org.uk > -------------------------------------------------------------------- -------- > >> Hi, > > >> I want to continue this discussion. > >> I saw in some papers, people used edgeR and DESeq to analysis >> differentially bound between different sample groups following ChIP-seq. > >> But most of them are studying transcription factors. > >> Is it the case for histone modifications ChIP-seq (H3K4me1, H3K4me2 or> H3k9me3)? > >> Regards, >> Sheng > > > On Thu, Nov 8, 2012 at 8:59 AM, Cittaro Davide <cittaro.davide at="" hsr.it="">wrote: > >> Dear Mark >> >> On Nov 8, 2012, at 8:53 AM, Mark Robinson <mark.robinson at="" imls.uzh.ch=""> >> wrote: >> >>> Dear Davide, >>> >>> Indeed, edgeR and DESeq can be (and have been) used in this mode. We >> published something recently on this: >>> >>> http://www.ncbi.nlm.nih.gov/pubmed/22879430 >>> http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.pdf >>> >> >> I've missed that :-( Thanks for the paper >> >>> You can apply that approach regardless of copy number being a factor ... >> basically, we counted tiled bins of the genome, but yes, you could focus in >> on regions of interest. The function abcdDNA() is really just a wrapper >> for the edgeR GLM. As usual, "normalization" can be delicate, depending on >> the type of data. >>> >>> Also note that the DiffBind package already does something similar, but >> has a lot more machinery to collect and organize the sets of enriched >> regions. >> >> I wonder why I've never used DiffBind before :-) >> >>> >>> Hope that helps. >> >> It does, thanks! >> >> d > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
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