[BioC] Questions about multi-factor contrast setting in DESeq2
Michael Love
michaelisaiahlove at gmail.com
Tue Mar 11 16:51:45 CET 2014
hi Ming,
Let's keep the discussion on list, as it might be relevant to others.
On Mon, Mar 10, 2014 at 11:55 PM, Ming Yi <yi02 at hotmail.com> wrote:
>
> I am not sure how tumor and RasPNot = 1,1,0,0,1,0,0,1, could you explain to
> me a bit?
>
Let's work with the GLM formula in the vignette (Section 4.1 GLM)
we have
E(K_ij) = mu_ij = s_j 2^( x_j. beta_i )
if we divide out size factors and take log2 we have:
log2( mu_ij / s_j ) = x_j. beta_i
x_j. is the j-th row of the model matrix X, and beta_i is the vector
of coefficients
(a log2-scale intercept and log2 fold changes) for gene i.
Your model has a beta vector for each gene with values:
1 "Intercept"
2 "Type_Tumor_vs_Normal"
3 "RasType_RasP_vs_RasOnly"
4 "RasType_RasP1Hit_vs_RasOnly"
5 "RasType_RasPNot_vs_RasOnly"
6 "TypeTumor.RasTypeRasP"
7 "TypeTumor.RasTypeRasP1Hit"
8 "TypeTumor.RasTypeRasPNot"
Where Normal is the base level of Type and RasOnly is the base level of RasType.
So a sample which is Normal & RasOnly is given by
log2( mu_ij / s_j) = beta_Intercept
And this sample will have
x_j. = [1 0 0 0 0 0 0 0]
A sample which is Tumor & RasPNot is given by:
log2( mu_ij / s_j) = beta_Intercept + beta_Type_Tumor_vs_Normal +
beta_RasType_RasPNot_vs_RasOnly + beta_TypeTumor.RasTypeRasPNot
This is because, all samples get an intercept, Tumor_vs_Normal is the
log2 fold change
of tumor samples over normal, RasPNot_vs_RasOnly is the log2 fold
change of RasPNot
samples over RasOnly samples, and TypeTumor.RasTypeRasPNot is the additional
interaction term for samples which are both Tumor and RasPNot.
And so this sample will have
x_j. = [1 1 0 0 1 0 0 1]
While the classic interaction linear model can be reparametrized
sometimes to have only
group means, we recommend users to use the following setup, because we
need the intercept
for the default setting of DESeq2 which includes moderation of log2
fold changes, but not the
intercept.
Note that in DESeq2 version >= 1.3, we implement expanded model
matrices, in which case
what were base levels in this analysis will also get a log2 fold
change (i.e. there are no longer
base levels). So then this will change the constrasts, but it can be
worked out using the above steps.
Mike
>
>
>
>
>
>
>
>> From: yi02 at hotmail.com
>> To: michaelisaiahlove at gmail.com
>> Date: Tue, 11 Mar 2014 01:27:29 +0000
>> CC: bioconductor at r-project.org
>> Subject: Re: [BioC] Questions about multi-factor contrast setting in
>> DESeq2
>
>>
>> Hi, Mike:
>>
>> Thx a lot for the input and advice, which seems a great idea. Appreciated
>> very much!
>>
>> I also come up a way using combined terms of Type and RasType, which seems
>> rather easy to derive the desired contrasts:
>>
>> > targets<-data.frame(targets,paste(targets$RasType,
>> > targets$Type,sep="."));
>> > colnames(targets)[ncol(targets)]<-"RasTum";
>> ...
>> > dds <- DESeqDataSetFromMatrix(countData = countD,colData = colD,design =
>> > ~RasTum);
>> >colData(dds)$RasTum<-relevel(colData(dds)$RasTum,"RasP1Hit.Normal")
>> > dds<-DESeq(dds, betaPrior=FALSE);
>> estimating size factors
>> estimating dispersions
>> gene-wise dispersion estimates
>> mean-dispersion relationship
>> final dispersion estimates
>> fitting model and testing
>> > show(resultsNames(dds));
>> [1] "Intercept"
>> [2] "RasTum_RasOnly.Normal_vs_RasP1Hit.Normal"
>> [3] "RasTum_RasOnly.Tumor_vs_RasP1Hit.Normal"
>> [4] "RasTum_RasP.Normal_vs_RasP1Hit.Normal"
>> [5] "RasTum_RasP.Tumor_vs_RasP1Hit.Normal"
>> [6] "RasTum_RasP1Hit.Tumor_vs_RasP1Hit.Normal"
>> [7] "RasTum_RasPNot.Normal_vs_RasP1Hit.Normal"
>> [8] "RasTum_RasPNot.Tumor_vs_RasP1Hit.Normal"
>>
>> then for contrast:
>> RasOnly.Tumor_vs_RasPNot.Tumor,
>> res <- results(dds, contrast=c(0,0,1,0,0,0,0,-1));
>>
>> for contrast: RasOnly.Normal_vs_RasPNot.Normal
>> res <- results(dds, contrast=c(0,1,0,0,0,0,-1,0));
>>
>> I will check if these two ways would give the same results. Thanks again
>> and best
>>
>> Ming
>>
>>
>> > From: michaelisaiahlove at gmail.com
>> > Date: Mon, 10 Mar 2014 19:15:20 -0400
>> > Subject: Re: Questions about multi-factor contrast setting in DESeq2
>> > To: yi02 at hotmail.com
>> > CC: bioconductor at r-project.org
>> >
>> > hi Ming,
>> >
>> >
>> > On Mon, Mar 10, 2014 at 10:27 AM, Ming Yi <yi02 at hotmail.com> wrote:
>> > >
>> > > Hi, Mike:
>> > >
>> > > Thanks for the help. Now we have updated the R and bioconductor
>> > > version as well as the DESeq2. Here is sessionInfo()
>> > > R version 3.0.3 (2014-03-06)
>> > > Platform: x86_64-unknown-linux-gnu (64-bit)
>> > > [1] DESeq2_1.2.10
>> > >
>> > > All the function calls now seem working, But I still got some issues
>> > > for the contrasts I desired;
>> > >
>> > >
>> > > > dds <- DESeqDataSetFromMatrix(countData = countD,colData =
>> > > > colD,design = ~Type + RasType + Type:RasType);
>> > > Usage note: the following factors have 3 or more levels:
>> > > RasType
>> > > For DESeq2 versions < 1.3, if you plan on extracting results for
>> > > these factors, we recommend using betaPrior=FALSE as an argument
>> > > when calling DESeq().
>> > > ...
>> >
>> > just FYI, here are mailing list threads addressing this note in version
>> > 1.2:
>> >
>> > http://permalink.gmane.org/gmane.science.biology.informatics.conductor/51749
>> >
>> > http://permalink.gmane.org/gmane.science.biology.informatics.conductor/52331
>> >
>> > >
>> > > > colData(dds)$RasType <-
>> > > > factor(colData(dds)$RasType,levels=c("RasOnly","RasP","RasP1Hit","RasPNot"));
>> > > > colData(dds)$Type <-
>> > > > factor(colData(dds)$Type,levels=c("Normal","Tumor"))
>> > > > dds<-DESeq(dds, betaPrior=FALSE);
>> > >
>> > > estimating size factors
>> > > estimating dispersions
>> > > gene-wise dispersion estimates
>> > > mean-dispersion relationship
>> > > final dispersion estimates
>> > > fitting model and testing
>> > >
>> > > > show(resultsNames(dds));
>> > > [1] "Intercept" "Type_Tumor_vs_Normal"
>> > > [3] "RasType_RasP_vs_RasOnly" "RasType_RasP1Hit_vs_RasOnly"
>> > > [5] "RasType_RasPNot_vs_RasOnly" "TypeTumor.RasTypeRasP"
>> > > [7] "TypeTumor.RasTypeRasP1Hit" "TypeTumor.RasTypeRasPNot"
>> > >
>> > > > res_RasP_vs_RasOnly <- results(dds,"RasType_RasP_vs_RasOnly")
>> > > > res_RasP_vs_RasOnly <- results(dds,name="RasType_RasP_vs_RasOnly")
>> > > >
>> > > > res_Tumor_vs_Normal<-results(dds,contrast=c("Type","Tumor","Normal"))
>> > > > res_RasP_vs_RasOnly <-
>> > > > results(dds,contrast=c("RasType","RasP","RasOnly"))
>> > > >
>> > > > res_Tumor.RasP_vs_Tumor.RasPNot<-results(dds,contrast=c(0,0,0,0,0,1,0,-1))
>> > > I can get above contrast results working and got the DEGs lists.
>> > > However, although I can get contrast result Tumor.RasP_vs_Tumor.RasPNot
>> > > using contrast=c(0,0,0,0,0,1,0,-1) as guide indicated, some of desired
>> > > contrasts I am not sure how to get:
>> > > I have 4 levels of RasType: "RasOnly","RasP","RasP1Hit","RasPNot", it
>> > > seems that RasOnly might be in intercept, since I did not see it in
>> > > show(resultsNames(dds)). And I am interested in contrast:
>> > > Tumor.RasOnly_Tumor.RasPNot, how do I get the results for this contrast?
>> >
>> > To generate the contrast in tumor group between RasOnly and RasPNot
>> > you can consider what the specifications for these groups would be in
>> > the model matrix, and then subtract them to obtain your contrast.
>> >
>> > The model matrix columns are given by resultsNames, so:
>> >
>> > 1 "Intercept"
>> > 2 "Type_Tumor_vs_Normal"
>> > 3 "RasType_RasP_vs_RasOnly"
>> > 4 "RasType_RasP1Hit_vs_RasOnly"
>> > 5 "RasType_RasPNot_vs_RasOnly"
>> > 6 "TypeTumor.RasTypeRasP"
>> > 7 "TypeTumor.RasTypeRasP1Hit"
>> > 8 "TypeTumor.RasTypeRasPNot"
>> >
>> > the groups you want to compare would be then specified by the
>> > following model matrix rows:
>> >
>> > tumor and RasOnly = 1,1,0,0,0,0,0,0
>> > tumor and RasPNot = 1,1,0,0,1,0,0,1
>> >
>> > subtracting the first line from the second line (because you asked for
>> > "Tumor-RasOnly vs Tumor-RasPNot") gives:
>> >
>> > contrast=c(0,0,0,0,-1,0,0,-1)
>> >
>> > which can be supplied as a contrast to results()
>> >
>> > You can follow such steps to produce your contrasts of interest.
>> >
>> > Mike
>> >
>> > > my best guess is if TypeTumor.RasTypeRasOnly is at intercept, I can
>> > > potentially get using below:
>> > > Res_Tumor.RasOnly_vs_Tumor.RasPNot <-results(dds,
>> > > contrast=c(1,0,0,0,0,0,0,-1).
>> > >
>> > > However, I like to get the results of contrast:
>> > > RasOnly.Normal_vs_RasPNot.Normal just to check normal contrast as
>> > > background, then I am stuck, since no TypeNormal.RasTypeRasP contrast terms
>> > > etc shown up in show(resultsNames(dds)).
>> > >
>> > > Any advice here?
>> > >
>> > > Thanks so much!
>> > > Best
>> > > Ming
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > > ________________________________
>> > > From: michaelisaiahlove at gmail.com
>> > >
>> > > Date: Sun, 9 Mar 2014 13:35:34 -0400
>> > > Subject: Re: Questions about multi-factor contrast setting in DESeq2
>> > > To: yi02 at hotmail.com
>> > > CC: bioconductor at r-project.org
>> > >
>> > > hi Ming,
>> > >
>> > > Are you using the latest release of DESeq2, version 1.2.x? The
>> > > contrast functionality was implemented in this release.
>> > >
>> > > You can check the help for ?results to debug, i.e. to see if there is
>> > > a 'contrast' argument in your installed version. You can check your versions
>> > > with sessionInfo(). You can install latest versions with biocLite("DESeq2"),
>> > > but you might need to upgrade to the latest release version of R, see the
>> > > installation help on the Bioc website.
>> > >
>> > > best
>> > >
>> > > Mike
>> > >
>> > >
>> > > On Sun, Mar 9, 2014 at 1:11 PM, Ming Yi <yi02 at hotmail.com> wrote:
>> > >
>> > > Hi, Mike:
>> > >
>> > > Thx for your advice again and I did try what you suggested as below:
>> > >
>> > > > dds <- DESeqDataSetFromMatrix(countData = countD,colData =
>> > > > colD,design = ~Type + RasType + Type:RasType);
>> > >
>> > > > dds <- DESeq(dds);
>> > > estimating size factors
>> > > estimating dispersions
>> > > gene-wise dispersion estimates
>> > > mean-dispersion relationship
>> > > final dispersion estimates
>> > > fitting generalized linear model
>> > > 105 rows did not converge in beta, labelled in mcols(object)$betaConv.
>> > > Use larger maxit argument with nbinomWaldTest
>> > > > show(resultsNames(dds));
>> > >
>> > > [1] "Intercept" "Type_Tumor_vs_Normal"
>> > > [3] "RasType_RasP_vs_RasOnly" "RasType_RasP1Hit_vs_RasOnly"
>> > > [5] "RasType_RasPNot_vs_RasOnly" "TypeTumor.RasTypeRasP"
>> > > [7] "TypeTumor.RasTypeRasP1Hit" "TypeTumor.RasTypeRasPNot"
>> > >
>> > > certainly we can use
>> > > > res_RasP_vs_RasOnly <- results(dds,"RasType_RasP_vs_RasOnly")
>> > > > res_RasP_vs_RasOnly <- results(dds,name="RasType_RasP_vs_RasOnly")
>> > > > res_Tumor_vs_Normal<-results(dds,"Type_Tumor_vs_Normal")
>> > > > res_Tumor_vs_Normal<-results(dds,name="Type_Tumor_vs_Normal")
>> > >
>> > > but I can not do the following with contrast as suggested in section
>> > > 3.2:
>> > > >
>> > > > res_Tumor_vs_Normal<-results(dds,contrast=c("Type","Tumor","Normal"))
>> > > Error in results(dds, contrast = c("Type", "Tumor", "Normal")) :
>> > > unused argument (contrast = c("Type", "Tumor", "Normal"))
>> > > > res_RasP_vs_RasOnly <-
>> > > > results(dds,contrast=c("RasType","RasP","RasOnly"))
>> > > Error in results(dds, contrast = c("RasType", "RasP", "RasOnly")) :
>> > > unused argument (contrast = c("RasType", "RasP", "RasOnly"))
>> > >
>> > > also I can not get contrast like Tumor.RasP_Tumor.RasPNot:
>> > > >
>> > > > res_Tumor.RasP_Tumor.RasPNot<-results(dds,contrast=c(0,0,0,0,0,1,0,-1))
>> > > Error in results(dds, contrast = c(0, 0, 0, 0, 0, 1, 0, -1)) :
>> > > unused argument (contrast = c(0, 0, 0, 0, 0, 1, 0, -1))
>> > >
>> > > it seems the interaction terms in the design (design = ~Type + RasType
>> > > + Type:RasType) changed the behavior of results()?
>> > >
>> > > Any idea or advice?
>> > > Thanks again for your time and help!
>> > >
>> > > Ming
>> > >
>> > > ________________________________
>> > > From: michaelisaiahlove at gmail.com
>> > > Date: Fri, 7 Mar 2014 19:12:23 -0500
>> > >
>> > > Subject: Re: Questions about multi-factor contrast setting in DESeq2
>> > > To: yi02 at hotmail.com
>> > > CC: bioconductor at r-project.org
>> > >
>> > > hi Ming,
>> > >
>> > > To follow up on the question about contrasts, the way to perform
>> > > comparisons like "RasOnly.Tumor vs RasOnly.Normal", would be a design:
>> > >
>> > > Type + RasType + Type:RasType
>> > >
>> > > where:
>> > >
>> > > results(dds, contrast=c("Type","Tumor","Normal"))
>> > >
>> > > tests for the general effect,
>> > >
>> > > and then the results for the interactions -- which are present in
>> > > resultsNames(dds) and can be extracted using the 'name' argument to
>> > > results() -- tests for an effect in a specific RasType which is different
>> > > than the general effect.
>> > >
>> > > Mike
>> > >
>> > >
>> > > On Fri, Mar 7, 2014 at 5:31 PM, Michael Love
>> > > <michaelisaiahlove at gmail.com> wrote:
>> > >
>> > > Hi Ming,
>> > >
>> > > Exploratory data analysis might be a more fruitful approach here
>> > > rather than brute force combinatorics and testing.
>> > >
>> > > Copying from Wolfgang's recommendation in a similar situation:
>> > >
>> > > "my advice here would be to put less emphasis on the testing and move
>> > > straight to clustering, using one of the transformations described in the
>> > > DESeq2 vignette to bring the data to a 'well-behaved' (log-like) scale."
>> > >
>> > > "To filter out the genes that vary not much, use the range (max-min)
>> > > or IQR and a subjective cutoff (e.g. retain the top 20% of genes), then use
>> > > standard clustering functions (e.g. pam from the cluster package), and other
>> > > exploratory data analyses (e.g. PCA) to see the types of behaviours."
>> > >
>> > > You might also try constructing a heatmap, as shown in the vignette,
>> > > using a subset of genes which vary the most, and then explore the grouping
>> > > of samples in the hierarchical clustering on the columns. For ease of
>> > > visualization, this subset should probably be in the 100s.
>> > >
>> > > Mike
>> > >
>> > > On Mar 7, 2014 2:59 PM, "Ming Yi" <yi02 at hotmail.com> wrote:
>> > >
>> > > Hi, Mike:
>> > >
>> > > Thx for the info, indeed I did try the following before with the
>> > > following for the data in user guide:
>> > > > dds <- DESeqDataSetFromMatrix(countData = countData,colData =
>> > > > colData, design = ~ condition+type)
>> > > > dds <- DESeq(dds)
>> > > > resultsNames(dds)
>> > > [1] "Intercept" "condition_untreated_vs_treated"
>> > > [3] "type_single.read_vs_paired.end"
>> > >
>> > > As you can see, the contrast I can get here is overall
>> > > untreated_vs_treated and overall single.read_vs_paired.end, there is no
>> > > subtype contrast such as treated.single.read vs treated.paired-end etc.
>> > >
>> > > I would love to discuss briefly what I need here. I have a dataset
>> > > which has tumors and matched normal samples from many patients, and there
>> > > are subtypes of the tumors, say RasOnly, RasP, RasPNot types of tumors, of
>> > > course, corresponding matched would be also with subtypes of RasOnly,
>> > > RasP,RasP1Hit, RasPNot, and the metadata like this:
>> > >
>> > > Subject SampleName Type RasType RasTum
>> > > T6745_01A 49_6745 T6745_01A Tumor RasP RasP.Tumor
>> > > N6745_11A 49_6745 N6745_11A Normal RasP RasP.Normal
>> > > T6761_01A 49_6761 T6761_01A Tumor RasPNot RasPNot.Tumor
>> > > N6761_11A 49_6761 N6761_11A Normal RasPNot RasPNot.Normal
>> > > T5930_01A 50_5930 T5930_01A Tumor RasP1Hit RasP1Hit.Tumor
>> > > N5930_11A 50_5930 N5930_11A Normal RasP1Hit RasP1Hit.Normal
>> > > T5932_01A 50_5932 T5932_01A Tumor RasOnly RasOnly.Tumor
>> > > N5932_11A 50_5932 N5932_11A Normal RasOnly RasOnly.Normal
>> > > ........
>> > >
>> > > Here are the contrasts I am interested to get DEGs:
>> > > RasOnly.Tumor vs RasOnly.Normal
>> > > RasP.Tumor vs RasP.Normal
>> > > RasP.Tumor + RasP1Hit.Tumor vs RasP.Normal+RasP1Hit.Normal
>> > > RasPNot.Tumor vs RasPNot.Normal
>> > > RasP1Hit.Tumor vs RasP1Hit.Normal
>> > > RasP.Tumor vs RasPNot.Tumor
>> > > RasP.Tumor+RasP1Hit.Tumor vs RasPNot.Tumor
>> > > RasOnly.Tumor vs RasPNot.Tumor
>> > > RasOnly.Normal vs RasPNot.Normal
>> > > RasP.Normal vs RasPNot.Normal
>> > > RasPRasP1Hit.Normal vs RasPNot.Normal,
>> > > Tumor vs Normal
>> > >
>> > > The last item Tumor vs Normal certainly can easily use design = ~ type
>> > > to deal with. But many of the contrasts listed above not easy unless use the
>> > > RasTum of the metadata shown above. I did try to use design=~Type+RasType
>> > > here are the commands:
>> > > > dds <- DESeqDataSetFromMatrix(countData = countD,colData =
>> > > > colD,design = ~Type+RasType);
>> > > > show(resultsNames(dds))
>> > > character(0)
>> > > > dds <- DESeq(dds);
>> > > > resultsNames(dds)
>> > > [1] "Intercept" "Type_Tumor_vs_Normal"
>> > > [3] "RasType_RasP_vs_RasOnly" "RasType_RasP1Hit_vs_RasOnly"
>> > > [5] "RasType_RasPNot_vs_RasOnly"
>> > >
>> > > as you can see, I can only derive overall subtype contrasts from this
>> > > way but not something like RasP.Tumor vs RasOnly.Tumor, the overall subtype
>> > > contrasts for example, RasType_RasP_vs_RasOnly, consider both tumor and
>> > > normal of RasP compared with those of RasOnly, which is certainly not what
>> > > we want here.
>> > > user guide section 3.2 did show
>> > > resCtrst <- results(ddsCtrst, contrast=c("treatment","OHT","DPN"))
>> > > resCtrst <- results(ddsCtrst, contrast=c(0,0,0,0,-1,1))
>> > >
>> > > So besides RasTum, if you have a better way using just design =
>> > > ~Type+RasType, that would be great.
>> > > I did try the following:
>> > >
>> > > > dds <- DESeqDataSetFromMatrix(countData = countD,colData =
>> > > > colD,design = ~RasTum);
>> > > > dds <- DESeq(dds);
>> > > estimating size factors
>> > > estimating dispersions
>> > > gene-wise dispersion estimates
>> > > mean-dispersion relationship
>> > > final dispersion estimates
>> > > fitting generalized linear model
>> > > 172 rows did not converge in beta, labelled in mcols(object)$betaConv.
>> > > Use larger maxit argument with nbinomWaldTest
>> > > > resultsNames(dds)
>> > > [1] "Intercept"
>> > > [2] "RasTum_RasOnly.Tumor_vs_RasOnly.Normal"
>> > > [3] "RasTum_RasP.Normal_vs_RasOnly.Normal"
>> > > [4] "RasTum_RasP.Tumor_vs_RasOnly.Normal"
>> > > [5] "RasTum_RasP1Hit.Normal_vs_RasOnly.Normal"
>> > > [6] "RasTum_RasP1Hit.Tumor_vs_RasOnly.Normal"
>> > > [7] "RasTum_RasPNot.Normal_vs_RasOnly.Normal"
>> > > [8] "RasTum_RasPNot.Tumor_vs_RasOnly.Normal"
>> > >
>> > > I did get many contrasts as I desired, but the contrasts
>> > > RasTum_RasP.Tumor_vs_RasOnly.Normal does not make sense here to me, but it
>> > > shown up there in the resultsNames(dds) .
>> > >
>> > > based on section 3.2, I seem be able to derived more from the above
>> > > contrasts:
>> > > say: for contrast RasP.Tumor vs RasP.Normal, I can do:
>> > > resCtrst<-result(dds, contrast=c(0,0,-1,1,0,0,0,0);
>> > >
>> > > But is there any better way to do the above contrasts listed above
>> > > that I desire?
>> > >
>> > > Thx again for your advice!
>> > > best
>> > >
>> > > Ming
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > > ________________________________
>> > > From: michaelisaiahlove at gmail.com
>> > > Date: Fri, 7 Mar 2014 13:23:56 -0500
>> > > Subject: Re: Questions about multi-factor contrast setting in DESeq2
>> > > To: yi02 at hotmail.com
>> > > CC: bioconductor at r-project.org
>> > >
>> > > hi Ming,
>> > >
>> > > I'm confused why you are not following the instructions in the
>> > > vignette section 1.5 Multifactor designs? You should not and we do not
>> > > recommend pasting together columns like this, nor inserting + 0 into the
>> > > design. Please have a look at what we do recommend in this section.
>> > >
>> > > Extracting contrasts is covered in vignette section 3.2 Contrasts.
>> > > First take a look at the entire vignette, as we've spent a lot of time
>> > > writing the documentation to try to answer user questions.
>> > >
>> > > we are happy to discuss the best approach for your experiment, but
>> > > first we need to hear more about your aims and experiment e.g. what
>> > > hypotheses you wish to test, what kind of genes you are looking to find.
>> > > It's hard for us to reverse engineer a recommendation rather than to go at
>> > > it from basic aims.
>> > >
>> > > Mike
>> > >
>> > >
>> > > On Fri, Mar 7, 2014 at 12:52 PM, Ming Yi <yi02 at hotmail.com> wrote:
>> > >
>> > >
>> > > Hi, Mike and All:
>> > >
>> > > I am testing DESeq2 for multi-factor contrast setting for my own data
>> > > with more complex meta data but currently use the simpler dataset from the
>> > > user guide for testing purpose, and run into some issues that need your
>> > > input and advice. Here are the commands (only show some more relevant
>> > > outputs):
>> > >
>> > > library("DESeq2")
>> > > library("Biobase")
>> > > library("pasilla")
>> > > data("pasillaGenes")
>> > > countData <- counts(pasillaGenes)
>> > > colData <- pData(pasillaGenes)[,c("condition","type")]
>> > >
>> > > colData<-data.frame(colData,paste(colData$condition,colData$type,sep="."))
>> > > colnames(colData)[3]<-"condition_type";
>> > > > dds <- DESeqDataSetFromMatrix(countData = countData,colData =
>> > > > colData, design = ~ condition_type)
>> > > > colData(dds)
>> > > DataFrame with 7 rows and 4 columns
>> > > condition type condition_type sizeFactor
>> > > <factor> <factor> <factor> <numeric>
>> > > treated1fb treated single-read treated.single-read 1.5116926
>> > > treated2fb treated paired-end treated.paired-end 0.7843521
>> > > treated3fb treated paired-end treated.paired-end 0.8958321
>> > > untreated1fb untreated single-read untreated.single-read 1.0499961
>> > > untreated2fb untreated single-read untreated.single-read 1.6585559
>> > > untreated3fb untreated paired-end untreated.paired-end 0.7117763
>> > > untreated4fb untreated paired-end untreated.paired-end 0.783745
>> > > >dds <- DESeq(dds)
>> > > estimating size factors
>> > > estimating dispersions
>> > > gene-wise dispersion estimates
>> > > mean-dispersion relationship
>> > > final dispersion estimates
>> > > fitting generalized linear model
>> > > > resultsNames(dds)
>> > > [1] "Intercept"
>> > > [2] "condition_type_treated.single.read_vs_treated.paired.end"
>> > > [3] "condition_type_untreated.paired.end_vs_treated.paired.end"
>> > > [4] "condition_type_untreated.single.read_vs_treated.paired.end"
>> > > > colData(dds)
>> > > DataFrame with 7 rows and 4 columns
>> > > condition type condition_type sizeFactor
>> > > <factor> <factor> <factor> <numeric>
>> > > treated1fb treated single-read treated.single-read 1.5116926
>> > > treated2fb treated paired-end treated.paired-end 0.7843521
>> > > treated3fb treated paired-end treated.paired-end 0.8958321
>> > > untreated1fb untreated single-read untreated.single-read 1.0499961
>> > > untreated2fb untreated single-read untreated.single-read 1.6585559
>> > > untreated3fb untreated paired-end untreated.paired-end 0.7117763
>> > > untreated4fb untreated paired-end untreated.paired-end 0.7837458
>> > >
>> > > Then I tried a slight diiffermry setting of the design:
>> > > > dds <- DESeqDataSetFromMatrix(countData = countData,colData =
>> > > > colData, design = ~0+ condition_type)
>> > > > dds <- DESeq(dds)
>> > > estimating size factors
>> > > estimating dispersions
>> > > gene-wise dispersion estimates
>> > > mean-dispersion relationship
>> > > final dispersion estimates
>> > > fitting generalized linear model
>> > > 580 rows did not converge in beta, labelled in mcols(object)$betaConv.
>> > > Use larger maxit argument with nbinomWaldTest
>> > > > resultsNames(dds)
>> > > [1] "condition_typetreated.paired.end"
>> > > [2] "condition_type_treated.single.read_vs_treated.paired.end"
>> > > [3] "condition_type_untreated.paired.end_vs_treated.paired.end"
>> > > [4] "condition_type_untreated.single.read_vs_treated.paired.end"
>> > > > colData(dds)
>> > > DataFrame with 7 rows and 4 columns
>> > > condition type condition_type sizeFactor
>> > > <factor> <factor> <factor> <numeric>
>> > > treated1fb treated single-read treated.single-read 1.5116926
>> > > treated2fb treated paired-end treated.paired-end 0.7843521
>> > > treated3fb treated paired-end treated.paired-end 0.8958321
>> > > untreated1fb untreated single-read untreated.single-read 1.0499961
>> > > untreated2fb untreated single-read untreated.single-read 1.6585559
>> > > untreated3fb untreated paired-end untreated.paired-end 0.7117763
>> > > untreated4fb untreated paired-end untreated.paired-end 0.7837458
>> > >
>> > > Then supposedly, I can use results(dds,
>> > > "condition_type_treated.single.read_vs_treated.paired.end") to get DEGs for
>> > > each contrast shown in resultsNames(dds).
>> > >
>> > > here are my questions:
>> > > 1. I used design = ~0+ condition_type instead of design = ~
>> > > condition_type in 2nd case, try to skip the intercept so that I can easily
>> > > get all possible contrasts, but seem not working the way I want.
>> > > 2. I tried to get all possible contrasts: but besides the contrasts
>> > > shown in resultsNames(dds) in both cases, the contrasts like
>> > > untreated.single.read vs treated.single.read, untreated.paired.end vs
>> > > untreated.single.read not even exists in the resultsNames(dds). also I like
>> > > the contrast generally like: treated (including both treated.single.read and
>> > > treated.paired-end) vs untreated (including both untreated.single-read and
>> > > untreated paired-end). I know for this case, we can just to design =
>> > > ~condition, but I wish to do this in the same roof of one single design
>> > > model although I can do a separate design. In limma and edgeR, there is a
>> > > function like: con.matrix<-makeContrasts() where one can set up any
>> > > contrasts under the design at will. Is there anythign like that in DESeq2? I
>> > > understand we can do design(dds) <- formula(~ condition_type), but no
>> > > contrast setting can be made at will. Anything in DESeq2 can get around
>> > > that?
>> > > 3. Also for simple contrast, I understand one can use
>> > > relevel(colData(dds)$condition,"control") kind of command to set base level,
>> > > but for multiple-factors contrasts as I am after, I almost need some kind of
>> > > makeContrasts() mechanism to set up contrasts at will or have to do that
>> > > individually one by one, which obvioulsy would be tedious and also these
>> > > contrasts won't be in a single model roof. Anything can get around like that
>> > > as well? if question 2 is addressed, this one shall be no problem.
>> > >
>> > > Thanks in advance for your help! Appreciated very much!
>> > >
>> > > Best
>> > >
>> > > Ming
>> > > ATRF/NCI-Frederick,
>> > > Maryland, USA.
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>>
>> [[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
More information about the Bioconductor
mailing list