Unfortunately that doesn't work. Only one batch variable is allowed. Thanks for your suggestion though. On Wed, Mar 19, 2014 at 6:58 AM, James W. MacDonald <jmacdon@uw.edu> wrote: > Hi Magda, > > I'm not sure you need to do things sequentially like that. From what I can > tell, you should just be able to do > > mod <- model.matrix(~tissue, des) > bat <- ComBat(data, des[,c("plate","row","chip")], mod) > > And go from there. > > Best, > > Jim > > > > On 3/18/2014 6:04 PM, Magda Price wrote: > >> Hi Jim, >> >> Re numCovs - what you've stated was how I interpreted the use as well, >> which is why I didn't think it would helpful. >> >> As usual with these types of human disease datasets, the study design is >> not ideal, and more complicated than I initially let on! The 180 samples >> are a combination of 3 phenotype groups (1 control + 2 diseased) and 5 >> different tissues. Other samples, unrelated to this project were also run >> on these chips, which is why I'm working with less samples than the total >> that were run (which was 288). >> >> Here's a simplified version of what my ComBat code looks like: >> >> #1 - correct for plate effect >> mod.1<- model.matrix(~tissue+group+row+chip, data=des) >> bat.1<- ComBat(data, des$plate, mod.1) >> >> #2 - correct for row effect >> mod.2<-model.matrix(~tissue+group+chip, data=des) >> bat.2<-ComBat(data=bat.1, des$row, mod.2) >> >> #3 - correct for chip >> mod.3<-model.matrix(~tissue+group,data=des) >> bat.3<-ComBat(data=bat.2, des$chip,mod.3) >> We know from some pilot studies that the effect size (i.e. differential >> methylation between disease vs control samples in a give tissue) is small, >> so I am concerned about being thorough in the batch correction. I'm new to >> batch correction and you've correctly understood my concern about the row >> effect; so it sounds to me that how I have modeled the effect in the code >> above (i.e. each batch variable as a factor) was correct. Any >> corrections/suggestions for what I've done above? >> >> Thanks! >> >> >> On Tue, Mar 18, 2014 at 2:27 PM, James W. MacDonald <jmacdon@uw.edu<mailto:>> jmacdon@uw.edu>> wrote: >> >> Hi Magda, >> >> The numCovs argument won't work because that is simply used to >> specify columns in the model matrix (of non-batch things you want >> to fit in your linear model) that are continuous covariates rather >> than fixed effects. It has nothing to do with correcting for the >> batch effect. >> >> And I think you might be thinking about batch effects in the wrong >> way. If you fit a 'row' effect, then what you are saying is that >> on average, the measures you get from one row differ from the >> measures you get from another row. So as an example, row 1 might >> tend to have higher values because those arrays don't get washed >> as well, whereas rows 3 and 4 might be dimmer because they get >> washed more. You then want to estimate how much brighter on >> average, the row1 chips are (and how much dimmer the row 3 and 4 >> chips are), and adjust the observed data to account for this. >> >> But you do the estimation of these averages using factors, rather >> than continuous measures (because a chip either is or is not in >> row 1). >> >> You might just be over-thinking this. I don't see how 3 plates of >> 24 chips gets you to 180 samples, but regardless it seems like you >> have enough replication to estimate the batch effects, and still >> have enough degrees of freedom left over for your comparisons, >> unless you have some huge number of phenotypic combinations that >> you are trying to compare (do you?). >> >> Best, >> >> Jim >> >> >> >> >> On Tuesday, March 18, 2014 2:13:11 PM, Magda Price wrote: >> >> Hi Jim, >> >> I have several different "batch" variables - one for example >> is the >> chip that each sample was run on (there are 24 of these) and I >> think >> chip batch should definitely be treated as a factor. Another >> "batch" >> variable I would like to adjust for is the position the sample >> was run >> on the chip (there are 6 different rows). If I use row as a >> factor, >> then the effect of being in row 1 vs 2 is treated the same as the >> effect of 1 vs 6, but the bias I see changes step-wise from >> row 1, 2, >> 3, 4, 5, 6 thus I thought that treating row as a numeric or >> integer >> variable would better model the "batch" effect. In other words row >> batches have meaning relative to each other whereas chip >> batches do not. >> >> I guess this would be another reason why using the numCovs option >> (continuous not integer) might not work in my case?! >> >> Hope that explains things a bit better! Happy to provide any >> more info >> & I really appreciate the input. >> >> Magda >> >> >> On Tue, Mar 18, 2014 at 10:51 AM, James W. MacDonald >> <jmacdon@uw.edu <mailto:jmacdon@uw.edu=""> >> <mailto:jmacdon@uw.edu <mailto:jmacdon@uw.edu="">>> wrote: >> >> Hi Magda, >> >> I'm curious. How can one specify a batch using a continuous >> variable? In other words, isn't a particular sample in a >> batch or not? >> >> Best, >> >> Jim >> >> >> >> On 3/18/2014 1:44 PM, Magda Price wrote: >> >> Hi Steve, >> >> Thanks for your advice. I do know that I'm using an old >> version of R (one >> of the packages I'm using requires it) however, the >> options >> you mention >> from sva are in fact available in the older version as >> well, >> but it wasn't >> clear to me how to use them. >> >> I've copied the usage and argument information for the >> ComBat >> function >> below, maybe you can help clarify: >> >> *ComBat(dat, batch, mod, numCovs=NULL, >> par.prior=TRUE,prior.plots=__FALSE)* >> >> >> *dat Genomic measure matrix (dimensions probe x >> sample) - for >> example, >> expression matrix* >> >> *batch Batch covariate (multiple batches allowed)* >> >> *mod Model matrix for outcome of interest and other >> covariates >> besides >> batch* >> >> *numCovs (Optional) Vector containing the column >> numbers of >> the continuous >> >> covariates in the model matrix, or NULL if no continuous >> covariates are >> used* >> >> *par.prior (Optional) TRUE indicates parametric >> adjustments >> will be used, >> FALSE indicates non-parametric adjustments will be used* >> *prior.plots (Optional) TRUE give prior plots with >> black as a >> kernel >> >> estimate of the empirical batch effect density and red >> as the >> parametric >> estimate* >> >> >> The model matrix is supposed to contain the outcome of >> interest and other >> covariates *besides batch*, but batch is what I need >> to be a >> continuous >> variable. numCovs seems to allow me to specify >> *covariates* >> that should be >> continuous, but not *adjustment variables*. What am I >> missing? >> >> >> Thanks again! >> >> >> >> On Tue, Mar 18, 2014 at 9:48 AM, Steve Lianoglou >> <lianoglou.steve@gene.com>> <mailto:lianoglou.steve@gene.com> >> <mailto:lianoglou.steve@gene.com>> >> <mailto:lianoglou.steve@gene.com>>>__wrote: >> >> >> Hi Magda, >> >> You are using a version of R (2.14) that is >> horribly out >> of date, and >> as a result your bioconductor packages are frozen to >> versions that are >> quite old. >> >> Please update to the latest version of R (3.0.3) and >> reinstall your >> bioconductor packages using biocLite to ensure >> that you >> are running >> the the latest version of them. >> >> The package you are version (sva v3.0.2) is now at >> version >> 3.8.0. >> >> One question you asked: >> >> - Row would be better treated as a continuous >> adjustment variable than a >> >> factor. In the version of sva that I am using >> (3.0.2) I >> believe that only >> factor adjustment variables are supported. I have seen >> mention in a few >> forums that there might be an update to ComBat to >> adjust >> for a numeric >> batch variable, is one available? >> >> Is readily answered by reading through the >> vignette for >> the current >> version of the package: >> >> >> http://bioconductor.org/__packages/release/bioc/__ >> vignettes/sva/inst/doc/sva.pdf >> >> >> <http: bioconductor.org="">> packages/release/bioc/vignettes/sva/inst/doc/sva.pdf> >> >> Specifically in Section 7 (Applying the ComBat >> function to >> adjust for >> known batches), where it states: >> >> """ >> By default, all adjustment variables will be >> treated as factor >> variables by the ComBat function. If you would >> like to include >> continuous adjustment variables, also create a vector >> containing the >> column numbers of the continuous covariates in the >> model >> matrix. This >> vector must then be input into ComBat via the >> numCovs option. >> """ >> >> HTH, >> >> -steve >> >> -- >> Steve Lianoglou >> Computational Biologist >> Genentech >> >> >> >> >> -- >> James W. MacDonald, M.S. >> Biostatistician >> University of Washington >> Environmental and Occupational Health Sciences >> 4225 Roosevelt Way NE, # 100 >> Seattle WA 98105-6099 >> >> >> >> >> -- >> E. Magda Price >> PhD Candidate, Robinson Lab >> University of British Columbia >> >> CFRI Room 2071 >> 950 West 28th Ave. >> Vancouver BC., V5Z 4H4 >> (604)-875-3015 <tel:%28604%29-875-3015> >> >> >> >> -- >> James W. MacDonald, M.S. >> Biostatistician >> University of Washington >> Environmental and Occupational Health Sciences >> 4225 Roosevelt Way NE, # 100 >> Seattle WA 98105-6099 >> >> >> >> >> -- >> E. Magda Price >> PhD Candidate, Robinson Lab >> University of British Columbia >> >> CFRI Room 2071 >> 950 West 28th Ave. >> Vancouver BC., V5Z 4H4 >> (604)-875-3015 >> > > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > > -- E. Magda Price PhD Candidate, Robinson Lab University of British Columbia CFRI Room 2071 950 West 28th Ave. Vancouver BC., V5Z 4H4 (604)-875-3015 [[alternative HTML version deleted]]