Question: Paired data for DEseq2: Multifactorial design
0
gravatar for Sindre Lee
5.7 years ago by
Sindre Lee100
Sindre Lee100 wrote:
Hi! I have two groups at two time points. And the samples are the same in both time points. I have run this in DESeq2: sampleFiles <- list.files(path="/Volumes/timemachine/HTseq_DEseq2",pattern="*.txt"); status <- factor(c(rep("Healthy",26), rep("Diabetic",22))) timepoints = as.factor(c(1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1, 1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2)); sampleTable <- data.frame(sampleName = sampleFiles, fileName = sampleFiles, status=status, timepoints=timepoints); directory <- c("/Volumes/timemachine/HTseq_DEseq2/"); des <- formula(~timepoints+status); ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design= des); ddsHTSeq This is however not correct. When looking at the Limma manual page 49: http://www.bioconductor.org/packages/2.12/bioc/vignettes/limma/inst/do c/usersguide.pdf This example is perfect for my experiment, but "Tissue = A or B" should be "Timepoint = 1 or 2". So timepoint = 1 or 2 = Paired data, and Disease vs Normal = unpaired data. I want to compare both within and between samples, so how can I do this in DESeq2? > sampleTable sampleName fileName status timepoints 1 D104.txt D104.txt Healthy 1 2 D121.txt D121.txt Healthy 1 3 D153.txt D153.txt Healthy 1 4 D155.txt D155.txt Healthy 1 5 D161.txt D161.txt Healthy 1 6 D162.txt D162.txt Healthy 1 7 D167.txt D167.txt Healthy 1 8 D173.txt D173.txt Healthy 1 9 D176.txt D176.txt Healthy 1 10 D177.txt D177.txt Healthy 1 11 D179.txt D179.txt Healthy 1 12 D204.txt D204.txt Healthy 1 13 D221.txt D221.txt Healthy 1 14 D253.txt D253.txt Healthy 2 15 D255.txt D255.txt Healthy 2 16 D261.txt D261.txt Healthy 2 17 D262.txt D262.txt Healthy 2 18 D267.txt D267.txt Healthy 2 19 D273.txt D273.txt Healthy 2 20 D276.txt D276.txt Healthy 2 21 D277.txt D277.txt Healthy 2 22 D279.txt D279.txt Healthy 2 23 N101.txt N101.txt Healthy 2 24 N108.txt N108.txt Healthy 2 25 N113.txt N113.txt Healthy 2 26 N170.txt N170.txt Healthy 2 27 N171.txt N171.txt Diabetic 1 28 N172.txt N172.txt Diabetic 1 29 N175.txt N175.txt Diabetic 1 30 N181.txt N181.txt Diabetic 1 31 N182.txt N182.txt Diabetic 1 32 N183.txt N183.txt Diabetic 1 33 N186.txt N186.txt Diabetic 1 34 N187.txt N187.txt Diabetic 1 35 N188.txt N188.txt Diabetic 1 36 N201.txt N201.txt Diabetic 1 37 N208.txt N208.txt Diabetic 1 38 N213.txt N213.txt Diabetic 2 39 N270.txt N270.txt Diabetic 2 40 N271.txt N271.txt Diabetic 2 41 N272.txt N272.txt Diabetic 2 42 N275.txt N275.txt Diabetic 2 43 N281.txt N281.txt Diabetic 2 44 N282.txt N282.txt Diabetic 2 45 N283.txt N283.txt Diabetic 2 46 N286.txt N286.txt Diabetic 2 47 N287.txt N287.txt Diabetic 2 48 N288.txt N288.txt Diabetic 2 The commands used in Limma (still at page 49): targets <- readTargets("/Volumes/timemachine/HTseq_DEseq2/Targets.rtf"); Treat <- factor(paste(targets$status,targets$timepoints,sep=".")); design <- model.matrix(~0+Treat); colnames(design) <- levels(Treat) So how can I create the "Targets.rtf" file? And is these commands the same when using DESeq2? Thank you so much!
limma • 932 views
ADD COMMENTlink modified 5.7 years ago by Michael Love24k • written 5.7 years ago by Sindre Lee100
Answer: Paired data for DEseq2: Multifactorial design
0
gravatar for Michael Love
5.7 years ago by
Michael Love24k
United States
Michael Love24k wrote:
hi Sindree, The section you refer to in the limma guide is treating the patient effect as a random effect, which is not the approach of DESeq2. If you want to use a particular limma approach on RNA-Seq data, you should check out the ?voom function in the limma package. Mike On Fri, Oct 18, 2013 at 1:20 PM, Sindre Lee <sindre.lee@studmed.uio.no>wrote: > Hi! > I have two groups at two time points. And the samples are the same in both > time points. I have run this in DESeq2: > > sampleFiles <- list.files(path="/Volumes/**timemachine/HTseq_DEseq2",** > pattern="*.txt"); > status <- factor(c(rep("Healthy",26), rep("Diabetic",22))) > timepoints = as.factor(c(1,1,1,1,1,1,1,1,1,** > 1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,**2,2,1,1,1,1,1,1,1,1,1,1,1,2,2,** > 2,2,2,2,2,2,2,2,2)); > sampleTable <- data.frame(sampleName = sampleFiles, fileName = > sampleFiles, status=status, timepoints=timepoints); > directory <- c("/Volumes/timemachine/HTseq_**DEseq2/"); > des <- formula(~timepoints+status); > ddsHTSeq <- DESeqDataSetFromHTSeqCount(**sampleTable = sampleTable, > directory = directory, design= des); > ddsHTSeq > > This is however not correct. > > When looking at the Limma manual page 49: http://www.bioconductor.org/** > packages/2.12/bioc/vignettes/**limma/inst/doc/usersguide.pdf<http: www.bioconductor.org="" packages="" 2.12="" bioc="" vignettes="" limma="" inst="" doc="" users="" guide.pdf=""> > > This example is perfect for my experiment, but "Tissue = A or B" should be > "Timepoint = 1 or 2". > > So timepoint = 1 or 2 = Paired data, and Disease vs Normal = unpaired data. > > I want to compare both within and between samples, so how can I do this in > DESeq2? > > sampleTable >> > sampleName fileName status timepoints > 1 D104.txt D104.txt Healthy 1 > 2 D121.txt D121.txt Healthy 1 > 3 D153.txt D153.txt Healthy 1 > 4 D155.txt D155.txt Healthy 1 > 5 D161.txt D161.txt Healthy 1 > 6 D162.txt D162.txt Healthy 1 > 7 D167.txt D167.txt Healthy 1 > 8 D173.txt D173.txt Healthy 1 > 9 D176.txt D176.txt Healthy 1 > 10 D177.txt D177.txt Healthy 1 > 11 D179.txt D179.txt Healthy 1 > 12 D204.txt D204.txt Healthy 1 > 13 D221.txt D221.txt Healthy 1 > 14 D253.txt D253.txt Healthy 2 > 15 D255.txt D255.txt Healthy 2 > 16 D261.txt D261.txt Healthy 2 > 17 D262.txt D262.txt Healthy 2 > 18 D267.txt D267.txt Healthy 2 > 19 D273.txt D273.txt Healthy 2 > 20 D276.txt D276.txt Healthy 2 > 21 D277.txt D277.txt Healthy 2 > 22 D279.txt D279.txt Healthy 2 > 23 N101.txt N101.txt Healthy 2 > 24 N108.txt N108.txt Healthy 2 > 25 N113.txt N113.txt Healthy 2 > 26 N170.txt N170.txt Healthy 2 > 27 N171.txt N171.txt Diabetic 1 > 28 N172.txt N172.txt Diabetic 1 > 29 N175.txt N175.txt Diabetic 1 > 30 N181.txt N181.txt Diabetic 1 > 31 N182.txt N182.txt Diabetic 1 > 32 N183.txt N183.txt Diabetic 1 > 33 N186.txt N186.txt Diabetic 1 > 34 N187.txt N187.txt Diabetic 1 > 35 N188.txt N188.txt Diabetic 1 > 36 N201.txt N201.txt Diabetic 1 > 37 N208.txt N208.txt Diabetic 1 > 38 N213.txt N213.txt Diabetic 2 > 39 N270.txt N270.txt Diabetic 2 > 40 N271.txt N271.txt Diabetic 2 > 41 N272.txt N272.txt Diabetic 2 > 42 N275.txt N275.txt Diabetic 2 > 43 N281.txt N281.txt Diabetic 2 > 44 N282.txt N282.txt Diabetic 2 > 45 N283.txt N283.txt Diabetic 2 > 46 N286.txt N286.txt Diabetic 2 > 47 N287.txt N287.txt Diabetic 2 > 48 N288.txt N288.txt Diabetic 2 > > The commands used in Limma (still at page 49): > > targets <- readTargets("/Volumes/**timemachine/HTseq_DEseq2/** > Targets.rtf"); > Treat <- factor(paste(targets$status,**targets$timepoints,sep=".")); > design <- model.matrix(~0+Treat); > colnames(design) <- levels(Treat) > > So how can I create the "Targets.rtf" file? And is these commands the same > when using DESeq2? > > Thank you so much! > > ______________________________**_________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/**listinfo/bioconductor<https: stat.et="" hz.ch="" mailman="" listinfo="" bioconductor=""> > Search the archives: http://news.gmane.org/gmane.** > science.biology.informatics.**conductor<http: news.gmane.org="" gmane.="" science.biology.informatics.conductor=""> > [[alternative HTML version deleted]]
ADD COMMENTlink written 5.7 years ago by Michael Love24k
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 245 users visited in the last hour