Thanks a lot. Chen 2012/4/5 James W. MacDonald <jmacdon@uw.edu> > Hi Chen, > > > On 4/5/2012 10:03 AM, yao chen wrote: > >> Thanks, James. >> >> You are right. I find expression of some genes are significant between >> treatment and control: I use lmFit(x,design), in which design only include >> treatment vs control >> >> and the case vs control contrast is no longer significant when I add >> cholesterol level to the model. In this case, cholesterol level is in the >> design matrix, as >> case control cholesterol >> 1 0 130 >> 0 1 120 >> 1 0 110 >> My question is, the cholesterol explain gene expression more may be the >> reflection of treatment . That is treatment change the expression of some >> genes and then cause the cholesterol difference. Therefore it's not the >> confounding factor. But I don't know how to distinguish them. >> > > That is the point I tried to make in my last response. All you know at > this point is that the difference in gene expression correlates with > cholesterol level. The model only tells you correlation, not causation. > > If you want to know if the gene causes differences in cholesterol levels > or if cholesterol levels cause differential gene expression, you will have > to design and perform an experiment to test for the causal factor. > > Best, > > Jim > > > >> Any suggestion? >> >> >> Chen >> >> >> >> 2012/4/5 James W. MacDonald <jmacdon@uw.edu <mailto:jmacdon@uw.edu="">> >> >> >> Hi Chen, >> >> >> On 4/5/2012 9:12 AM, yao chen wrote: >> >> Thanks, Moshe and James >> >> Any question about confounding factor if you or anybody can >> help me. >> >> Actually, I have many different phenotype of samples here, >> such as weight, age, cholesterol. I want to find the >> differential expressed genes between case and control >> (say:high blood pressure vs health ). I found some significant >> genes when I adjust the age, weight. However, when I adjust >> the cholesterol level, these genes became not significant (/P/ >> values larger). So either cholesterol is confounding factor >> which I should control , or these differential expressed genes >> control the cholesterol level which I should not adjust. I am >> not sure which assumption is right. I am thinking it should be >> the last one. >> >> >> I'm not sure I completely understand what you are saying here. It >> is conventional in this field to state that 'genes are >> significant', but that is an incomplete statement. The real >> statement is that the gene expression appears to be significantly >> differentially expressed between (e.g., treatment and control). So >> are you saying that the case vs control contrast is no longer >> significant if you add cholesterol level to the model? >> >> If you add cholesterol level to the model, you have moved from an >> ANOVA model to an ANCOVA model in which you are fitting a linear >> model between the gene expression and cholesterol level, and >> allowing different intercepts between the different factor levels, >> but assuming equal slopes. If the contrasts between factor levels >> are no longer significant, that implies that the intercepts aren't >> different. >> >> In this case, the cholesterol level 'explains' more about gene >> expression than the treatment. Whether or not the genes control >> the cholesterol level or the cholesterol level is affecting gene >> expression cannot be determined. But what can be said is that once >> you control for cholesterol level, the differences between the >> factor levels are no longer significant. >> >> Also note that fitting a linear model (as compared to ANOVA) is >> trickier. For ANOVA, you really just assume that the distribution >> of the data for the different factor levels is relatively close to >> normal (or at least 'hump shaped'). With linear regression you can >> run into problems if your residuals are not correctly distributed, >> if you have high leverage data points, etc, which are not things >> you can check for thousands of models. In addition, you are making >> the assumption that the slopes of the regression lines are >> parallel, which might not always be valid. You may need to >> introduce an interaction term to allow for diverging slopes. >> >> Best, >> >> Jim >> >> >> >> 2012/4/4 Moshe Olshansky <olshansky@wehi.edu.au>> <mailto:olshansky@wehi.edu.au> <mailto:olshansky@wehi.edu.au>> >> <mailto:olshansky@wehi.edu.au>**>> >> >> >> You are right. My mistake. Sorry... >> >> > Hi, Moshe, >> > >> > I still did not get it. How to use your method? >> > >> > lmFit<-(x,design) which x allow missing values, but design >> didn't. >> > >> > usually, I want to adjust batch effects, I will include it >> in design >> > matrix, i.e. >> > case control batch >> > 1 0 1 >> > 0 1 1 >> > 1 0 2 >> > which x is expression matrix, and then I will get the >> differential >> > expressed gene between case and control with adjusting batch. >> > >> > In this case, I just want to simply use "weight" instead of >> "batch" in >> > design matrix. But unfortunately, you can't have missing values >> in design >> > matrix which I think it's very often if we have many samples. >> > >> > 2012/4/4 Moshe Olshansky <olshansky@wehi.edu.au>> <mailto:olshansky@wehi.edu.au> >> <mailto:olshansky@wehi.edu.au <mailto:olshansky@wehi.edu.au="">**>> >> >> >> > >> >> Why do you want to include weight in the design matrix? >> >> It may be more reasonable to include weight in the linear >> model, i.e. >> >> expression ~ condition + weight and then your design matrix >> will have >> >> two >> >> columns (if there are two conditions): the first column will >> contain 0's >> >> and 1's depending on the condition (group) of the patient >> while the >> >> second >> >> one will be identically 1. And then the weights of the patients >> will be >> >> in >> >> the values (i.e. the second argument to lmFit) which I believe >> allows >> >> missing values. >> >> But this implicitly assumes that the weight (or some function >> of it - >> >> you >> >> can use any transformation you like) contributes additively >> (and >> >> linearly) >> >> to the expression (or it's logarithm). >> >> >> >> Moshe. >> >> >> >> > Hi, >> >> > >> >> > I am using Limma to get differential expressed genes between >> case and >> >> > control samples. In addition, I want to adjust the >> "weight" of >> >> patients >> >> by >> >> > including it in the "design" matrix. However, one weight >> value of a >> >> > patient >> >> > is missing, so I have a "NA" in design matrix. When I run >> lmFit, I got >> >> the >> >> > error message :Error in qr.default(x) : NA/NaN/Inf in foreign >> function >> >> > call >> >> > (arg 1). >> >> > >> >> > Can limma handle the missing value in design matrix or I >> have to >> >> remove >> >> > this sample ? >> >> > >> >> > Thanks, >> >> > >> >> > Chen >> >> > >> >> > [[alternative HTML version deleted]] >> >> > >> >> > ______________________________**_________________ >> >> > Bioconductor mailing list >> >> > Bioconductor@r-project.org >> <mailto:bioconductor@r-**project.org <bioconductor@r-project.org="">> >> <mailto:bioconductor@r-**project.org <bioconductor@r-project.org=""> >> >> <mailto:bioconductor@r-**project.org <bioconductor@r-project.org=""> >> >> >> >> >> > https://stat.ethz.ch/mailman/**listinfo/bioconductor<ht tps:="" stat.ethz.ch="" mailman="" listinfo="" bioconductor=""> >> >> > Search the archives: >> >> > >> http://news.gmane.org/gmane.**science.biology.informatics.** >> conductor<http: news.gmane.org="" gmane.science.biology.informatics.c="" onductor=""> >> >> > >> >> >> >> >> >> >> >> >> ______________________________** >> ______________________________**__________ >> >> The information in this email is confidential and intended >> solely for >> >> the >> >> addressee. >> >> You must not disclose, forward, print or use it without the >> permission >> >> of >> >> the sender. >> >> >> ______________________________** >> ______________________________**__________ >> >> >> > >> >> >> >> ______________________________** >> ______________________________**__________ >> The information in this email is confidential and intended >> solely >> for the addressee. >> You must not disclose, forward, print or use it without the >> permission of the sender. >> ______________________________** >> ______________________________**__________ >> >> >> >> -- James W. MacDonald, M.S. >> Biostatistician >> University of Washington >> Environmental and Occupational Health Sciences >> 4225 Roosevelt Way NE, # 100 >> Seattle WA 98105-6099 >> >> >> > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > > [[alternative HTML version deleted]]