Methods for time course gene expression analysis in an observational cohort
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@oliver-hofmann-3005
Last seen 9.6 years ago
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_ one would be preferable over the other? 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/
Microarray limma Microarray limma • 1.2k views
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@oliver-hofmann-3005
Last seen 9.6 years ago
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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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@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/ > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]]
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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/ >> >> _______________________________________________ >> 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 > -- 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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On Jan 17, 2013, at 1:55 PM, Oliver Hofmann wrote: > 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 > > Oliver, Not of which I am aware. Perhaps you can treat Main exposure+ asthma status etc as a level. Best wishes, Rich
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