Dear group, I have two types of samples (cancer and normal) with covariate data including survival times. I applied limma (and filtered genes that are significantly differentially expressed between cancer and normal. Say I have 500 genes after (adj.P.Value using BH) filtering. Is it meaningful to apply coxfilter on those 500 genes (by supplying expression values for those 500 genes and survival times for all samples) instead of using kOverA flter. Thanks Ad.

Hi Adrian, Adrian Johnson wrote: > Dear group, > > I have two types of samples (cancer and normal) with covariate data > including survival times. > > I applied limma (and filtered genes that are significantly > differentially expressed between cancer and normal. Say I have 500 > genes after (adj.P.Value using BH) filtering. > > Is it meaningful to apply coxfilter on those 500 genes (by supplying > expression values for those 500 genes and survival times for all > samples) instead of using kOverA flter. What is the hypothesis being tested here? A t-test and a Cox model test for completely different things, so I don't see why you would follow one with the other. Best, Jim > > Thanks > Ad. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician Hildebrandt Lab 8220D MSRB III 1150 W. Medical Center Drive Ann Arbor MI 48109-0646 734-936-8662

Hi Adrian, First, please don't take things off the BioC list. Adrian Johnson wrote: > Hi Jim, > I am not sure if Ive said it correctly that is correct for > statisticians (i am a biology student). Say I want to find prognostic > potential of the top 100 genes differentially expressed between normal > and cancer tissues. I have the survival data, remission status, sex > and other covariate information. > > In a tutorial by Drs. Gentleman and Dudoit and others, they used > bootstrap based MTP and cox-t statistics from multtest package to > associate gene expression measure and survival data. (Website : > www.stat.berkeley.edu/~sandrine/Docs/Talks/MBI04/mbi.html) > > If I am not mistaken, the aim there was to identify differentially > expressed genes (using either "f" or "t" stististics) on a filtered > expression matrix derived from RMA on affy study. The filter is a. > the coefficient of variation is between 0.7 and 10 b. at least 20% of > the samples have a measured intensity of at least 100 (100 on linear > scale). > ( http://www.bioconductor.org/workshops/2006/BioC2006/labs/kdhansen/ multtest.html > at section 1: getting started) That was the goal of that particular workshop, but they didn't mix the two (t-tests and survival analysis). > > This above step seems to be old, instead I wanted to test the > prognostic potential of top 100 genes filtered using adj.P.value (be > it BH method) from limma topTable function on eBayes fit object, by > applying cox-proportional hazard method on 100 genes using ER or > mutation status and survival data. > > The hypothesis is that the differentially expressed genes between > cancer and normal samples are prognostic genes. Instead of applying > cox model on every row of the gene expression matrix, I want to apply > on the genes that I know are differentially expressed. > I have no idea how this can be done. The problem with your methodology IMO, is that a gene may be differentially expressed between cancer and normal yet have no prognostic ability vis a vis survival. Two examples: Normal - c(4.5, 4.1, 4.7,4.5) Cancer - c(6.8, 7.2, 7.3, 6.6) Surv.time - c(3, 4.5, 15, 20) ## months These are likely significantly different, but I doubt there would be any significance for the cancer samples in a Cox model. Normal - c(4.5, 4.1, 4.7,4.5) Cancer - c(8.3, 6.4, 5.1, 3.4) Surv.time - c(3, 4.5, 15, 20) ## months These might be significantly different between cancer and normal (probably not), but the Cox model would likely have a very small p-value. Granted these are probably extreme examples, but the point here is that the t-test is probably not the best way to filter samples for a survival analysis. Best, Jim > > Is my question still valid or is it still naive way of connecting two > totally different things. I appreciate your suggestion and help. > > thank you. > > Adrian > > > > > > On Thu, Nov 13, 2008 at 9:01 AM, James W. MacDonald > <jmacdon at="" med.umich.edu=""> wrote: >> Hi Adrian, >> >> Adrian Johnson wrote: >>> Dear group, >>> >>> I have two types of samples (cancer and normal) with covariate data >>> including survival times. >>> >>> I applied limma (and filtered genes that are significantly >>> differentially expressed between cancer and normal. Say I have 500 >>> genes after (adj.P.Value using BH) filtering. >>> >>> Is it meaningful to apply coxfilter on those 500 genes (by supplying >>> expression values for those 500 genes and survival times for all >>> samples) instead of using kOverA flter. >> What is the hypothesis being tested here? A t-test and a Cox model test for >> completely different things, so I don't see why you would follow one with >> the other. >> >> Best, >> >> Jim >>> Thanks >>> Ad. >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at stat.math.ethz.ch >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >> -- >> James W. MacDonald, M.S. >> Biostatistician >> Hildebrandt Lab >> 8220D MSRB III >> 1150 W. Medical Center Drive >> Ann Arbor MI 48109-0646 >> 734-936-8662 >> -- James W. MacDonald, M.S. Biostatistician Hildebrandt Lab 8220D MSRB III 1150 W. Medical Center Drive Ann Arbor MI 48109-5646 734-936-8662