Suggestions for Chip-seq differential expression analysis
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@vishal-thapar-3427
Last seen 10.2 years ago
Hi, I wanted some suggestions for a Chip-Seq analysis that I am working on. I have ChIP-Seq data for 3 cell types, control, treatment1, treatment2. I have 2 replicates of each. I would like suggestions on how I could compare the 2 treatments to *each* other. One way to do this is to do a diff expression for S1= treatment1 vs control and S2=treatment2 vs control and then take the regions that occur in S1 and S2, get the read counts and do a Chi Square test for each region. Another way is to take S1 and S2 regions, get their read counts and use EdgeR from there on to do a second differential expression analysis. I am not sure which one is better. Any suggestions with justifications are welcome! Thanks, Sincerely, Vishal -- *Vishal Thapar, Ph.D.* *Scientific informatics Analyst Cold Spring Harbor Lab Quick Bldg, Lowe Lab 1 Bungtown Road Cold Spring Harbor, NY - 11724* [[alternative HTML version deleted]]
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@michael-lawrence-3846
Last seen 3.0 years ago
United States
Just a note: you might be interested in the diffPeakSummary function in the chipseq package. On Wed, Nov 10, 2010 at 7:16 AM, Vishal Thapar <vishalthapar@gmail.com>wrote: > Hi, > > I wanted some suggestions for a Chip-Seq analysis that I am working on. I > have ChIP-Seq data for 3 cell types, control, treatment1, treatment2. I > have > 2 replicates of each. I would like suggestions on how I could compare the 2 > treatments to *each* other. > One way to do this is to do a diff expression for S1= treatment1 vs control > and S2=treatment2 vs control and then take the regions that occur in S1 and > S2, get the read counts and do a Chi Square test for each region. Another > way is to take S1 and S2 regions, get their read counts and use EdgeR from > there on to do a second differential expression analysis. I am not sure > which one is better. Any suggestions with justifications are welcome! > > Thanks, > > Sincerely, > > Vishal > > -- > *Vishal Thapar, Ph.D.* > *Scientific informatics Analyst > Cold Spring Harbor Lab > Quick Bldg, Lowe Lab > 1 Bungtown Road > Cold Spring Harbor, NY - 11724* > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > 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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Mark Robinson ★ 1.1k
@mark-robinson-2171
Last seen 10.2 years ago
Hi Vishal. I often do differential comparisons of ChIP-seq (or similar) data between experimental conditions using edgeR. Basically, its 1 step, as opposed to the 2-stage analysis you suggest below. Basically, you compute read counts for regions of interest across your samples -- for you, this would be a count table of N ROIs by 6 samples. Then, make your comparison of interest. Hope that helps. Cheers, Mark On 2010-11-11, at 2:16 AM, Vishal Thapar wrote: > Hi, > > I wanted some suggestions for a Chip-Seq analysis that I am working on. I > have ChIP-Seq data for 3 cell types, control, treatment1, treatment2. I have > 2 replicates of each. I would like suggestions on how I could compare the 2 > treatments to *each* other. > One way to do this is to do a diff expression for S1= treatment1 vs control > and S2=treatment2 vs control and then take the regions that occur in S1 and > S2, get the read counts and do a Chi Square test for each region. Another > way is to take S1 and S2 regions, get their read counts and use EdgeR from > there on to do a second differential expression analysis. I am not sure > which one is better. Any suggestions with justifications are welcome! > > Thanks, > > Sincerely, > > Vishal > > -- > *Vishal Thapar, Ph.D.* > *Scientific informatics Analyst > Cold Spring Harbor Lab > Quick Bldg, Lowe Lab > 1 Bungtown Road > Cold Spring Harbor, NY - 11724* > > [[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 ------------------------------ Mark Robinson, PhD (Melb) Epigenetics Laboratory, Garvan Bioinformatics Division, WEHI e: mrobinson at wehi.edu.au e: m.robinson at garvan.org.au p: +61 (0)3 9345 2628 f: +61 (0)3 9347 0852 ------------------------------ ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:6}}
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Hi Mark, Thanks for your reply. I have used EdgeR for a paired comparison without a control but I haven't used it with one. Would you point me to a simple tutorial or just even the function in EdgeR that can help me do that? The way I understood edgeR was, that its for a paired comparison with replicates. Here, I have 3 comparison. I did look at one tutorial online but it doesn't mention anything about a 3 comparison with 1 being a control. May be I am missing something here. Thanks again for your help. Sincerely, Vishal On Wed, Nov 10, 2010 at 5:41 PM, Mark Robinson <mrobinson@wehi.edu.au>wrote: > Hi Vishal. > > I often do differential comparisons of ChIP-seq (or similar) data between > experimental conditions using edgeR. Basically, its 1 step, as opposed to > the 2-stage analysis you suggest below. > > Basically, you compute read counts for regions of interest across your > samples -- for you, this would be a count table of N ROIs by 6 samples. > Then, make your comparison of interest. > > Hope that helps. > > Cheers, > Mark > > On 2010-11-11, at 2:16 AM, Vishal Thapar wrote: > > > Hi, > > > > I wanted some suggestions for a Chip-Seq analysis that I am working on. I > > have ChIP-Seq data for 3 cell types, control, treatment1, treatment2. I > have > > 2 replicates of each. I would like suggestions on how I could compare the > 2 > > treatments to *each* other. > > One way to do this is to do a diff expression for S1= treatment1 vs > control > > and S2=treatment2 vs control and then take the regions that occur in S1 > and > > S2, get the read counts and do a Chi Square test for each region. Another > > way is to take S1 and S2 regions, get their read counts and use EdgeR > from > > there on to do a second differential expression analysis. I am not sure > > which one is better. Any suggestions with justifications are welcome! > > > > Thanks, > > > > Sincerely, > > > > Vishal > > > > -- > > *Vishal Thapar, Ph.D.* > > *Scientific informatics Analyst > > Cold Spring Harbor Lab > > Quick Bldg, Lowe Lab > > 1 Bungtown Road > > Cold Spring Harbor, NY - 11724* > > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > ------------------------------ > Mark Robinson, PhD (Melb) > Epigenetics Laboratory, Garvan > Bioinformatics Division, WEHI > e: mrobinson@wehi.edu.au > e: m.robinson@garvan.org.au > p: +61 (0)3 9345 2628 > f: +61 (0)3 9347 0852 > ------------------------------ > > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:20}}
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