[BioC] Methods for time course gene expression analysis in an observational cohort

[Oliver Hofmann]

Rich,


thanks for this. Is there a way to adjust for confounding factors in EDGE that I missed? I.e., having the main exposure as covariate, but adjusting for other measured exposures (other allergens, asthma status, etc.)?

Best, Oliver


On 16 Jan 2013, at 18:14, Richard Friedman <friedman at cancercenter.columbia.edu> wrote:

> Dear Oliver,
> 
> 	In addition to Limma, which measures changes between conditions,
> the program EDGE is useful for time course analysis
> to measure statistical significance of changes over the entire time course. You
> can read about here
> 
> http://www.ncbi.nlm.nih.gov/pubmed/16357033
> http://www.ncbi.nlm.nih.gov/pubmed/16141318
> 
> With hopes that this helps,
> Rich
> 
> Richard A. Friedman, PhD
> Associate Research Scientist,
> Biomedical Informatics Shared Resource
> Herbert Irving Comprehensive Cancer Center (HICCC)
> Lecturer,
> Department of Biomedical Informatics (DBMI)
> Educational Coordinator,
> Center for Computational Biology and Bioinformatics (C2B2)/
> National Center for Multiscale Analysis of Genomic Networks (MAGNet)/
> Columbia Initiative in 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
> http://cancercenter.columbia.edu/~friedman/
> 
> "Complex numbers! Ha! Ha! There is nothing weirder
> than imaginary numbers. Architects don't need to know 
> complex numbers. Whenever I get a  negative root for
> an area, I throw it out. And don't talk to me about
> quaternions. I am not going into computer animation."
> -Rose Friedman, age 16
>  
> 
> On Jan 16, 2013, at 6:05 PM, Oliver Hofmann wrote:
> 
>> Dear all,
>> 
>> 
>> posting this on behalf of a colleague who is looking for help with a time series analysis:
>> 
>> "I've read a number of articles and searched the list's archive, but cannot figure out what is the best method to analyze time course data where I need to adjust for confounders.
>> 
>> First - my experiment: Longitudinal cohort study in 80 children (60 asthmatics and 20 non-asthmatics), three time points (one prior to exposure, two after exposure), one exposure (endotoxin) and several measured confounders (other allergens, asthma vs. no asthma, atopy vs. no atopy).
>> 
>> The research question is:
>> 
>> 1) Are there differentially expressed genes in response to endotoxin at the two time points after exposure (early and late) - note issue of confounding exposures
>> 
>> 2) Do differentially expressed genes differ between asthmatics and non-asthmatics at the two time points in response to endotoxin exposure
>> 
>> Most time course studies are laboratory studies where by experimental design there are no (known) confounders.  Limma or a linear mixed effect model seem to handle time course with covariates.
>> 
>> Can anybody give me advice on:
>> 
>> 1. What is the best method (limma vs. linear mixed effect model vs. other) for this research question and design, and _why?
>> 
>> 2. What is the best way to decide which covariates to include (prior biological knowledge vs. algorithm)
>> 
>> 3. How does one typically handle missing data in this kind of time course microarray studies?"
>> 
>> Any feedback would be appreciated!
>> 
>> Best, Oliver
>> 
>> 
>> -- 
>> Research Scientist    Harvard School of Public Health
>> Associate Director    Bioinformatics Core
>> Skype: ohofmann       Phone: +1 (617) 365 0984
>> http://compbio.sph.harvard.edu/chb/
>> 
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--
Research Scientist    Harvard School of Public Health
Associate Director    Bioinformatics Core
Skype: ohofmann       Phone: +1 (617) 365 0984
http://compbio.sph.harvard.edu/chb/