[BioC] ANCOVA microarray time-course continuous & categorical variables / Limma extensions
rct at thompsonclan.org
Wed Oct 23 00:03:54 CEST 2013
Including continuous covariates in design matrices is R is just as easy
as including categorical ones. Instead of creating a column for each
degree of freedom in the categorical variable, you just end up with a
single column that simply contains the values of the continuous
variable. Try using the model.matrix function with a combination of
your categorical variables and continuous ones to see what it does.
On Tue Oct 22 14:36:38 2013, Richard Friedman wrote:
> On Oct 22, 2013, at 5:30 PM, Michael Breen wrote:
>> Hi all,
>> Our lab analyzes gene-expression from microarray and RNAseq platforms.
>> Currently, I am looking for a package to test differential expression (DE)
>> while considering continuous variables that may alter gene-expression
>> profiles. In other words, an ANCOVA type tool. I am quite familiar with
>> Limma (ANOVA) but including continuous variables is not very well described.
>> Specifically, we have a project were two groups can be modeled over the
>> same 2 time points. One group starts healthy and ends in a disease state.
>> The other group starts healthy and remains healthy.
>> We are interested in identifying genes uniquely responding within one group
>> and not in the other. Thus, we have implemented a longitudinal contrast
>> with linear modeling through Limma. However, we are also interested in
>> adding one or two continuous variables (tumor size, time spent meditating,
>> the amount of drinks one consumes etc..) to check if gene expression
>> differences or similarities may be due to these factors instead of due to
>> belonging to a certain class. Limma seems to test categorical variables,
>> but I don't think it is capable of either correlating gene-expression to
>> continuous variables.
>> If not, can someone recommend a tool which may be appropriate for such a
>> [[alternative HTML version deleted]]
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> Dear Michael and list,
> I think that you write the design and contrast matrices
> exactly as you would for an ANCOVA in R only you do the
> fit and Bayesian correction in Limma.
> Perhaps someone who has had experience doing this
> kind of analysis can comment.
> Best wishes,
> Richard A. Friedman, PhD
> Associate Research Scientist,
> Biomedical Informatics Shared Resource
> Herbert Irving Comprehensive Cancer Center (HICCC)
> Department of Biomedical Informatics (DBMI)
> Educational Coordinator,
> Center for Computational Biology and Bioinformatics (C2B2)/
> National Center for Multiscale Analysis of Genomic Networks (MAGNet)/
> Columbia Department of Systems Biology
> Room 824
> Irving Cancer Research Center
> Columbia University
> 1130 St. Nicholas Ave
> New York, NY 10032
> (212)851-4765 (voice)
> friedman at cancercenter.columbia.edu
> In memoriam, Frederik Pohl
> Bioconductor mailing list
> Bioconductor at r-project.org
> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
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