HI Jim, Here, strains are different clones and two technical replicates from each clone/strain. So it means Here DEG can be identify using two type of comparing? 1) Differentiall Expression between Control vs KO. 2) Differential Expression b/n Control vs KO after adjusting Strain differences Regards On Mon, Dec 30, 2013 at 11:59 PM, James W. MacDonald < jmacdon@u.washington.edu> wrote: > Hi Reema, > > I don't know what strain means in this context, but if the KO samples both > have knocked out genes and are different strains from the control samples, > then you have at least partially aliased strain and KO. In other words, if > you compare strain1_KO vs strain3_Control, you can't say if the differences > are due to the knocked out gene or are simply due to differences between > strain. > > If you fit the model below (with Design <- model.matrix(~Condition+Straing)), > then you can determine genes that are different between KO and Control > after adjusting for strain. But you cannot detect any strain specific > differences. > > Best, > > Jim > > > > On 12/30/2013 3:41 PM, Reema Singh wrote: > >> HI Jim, >> >> I am really sorry, I made a typing mistake in my table. I actually have >> four different strain. This is the actual table:- >> >> *1) Sample table for design matrix* >> >> Sample Strain Condition >> >> KO1-A 1 KO >> >> KO1-B 1 KO >> >> KO2-A 2 KO >> >> KO2-B 2 KO >> >> Cont1-A 3 Control >> >> >> Cont1-B 3 Control >> >> Cont2-A 4 Control >> >> Cont2-B 4 Control >> >> >> In this case is it fine to use factor of strain like this = ( Straing <- >> factor(target$Strain)? >> >> Regards >> >> >> On Mon, Dec 30, 2013 at 12:27 AM, James W. MacDonald <jmacdon@uw.edu<mailto:>> jmacdon@uw.edu>> wrote: >> >> Hi Reema, >> >> You only have two strains, right? In your original email it seemed >> like that was what you indicated. If so, then you want a factor >> that equals one if strain 1 and two if the other strain. Then you >> should have a single column of your design matrix that is a zero >> for one strain and a one if the other. >> >> So something like >> >> strain <- factor(rep(1:2, each=2, times=2)) >> >> Best, >> >> Jim >> >> On Dec 29, 2013 10:09 AM, "Reema Singh" <reema28sep@gmail.com>> <mailto:reema28sep@gmail.com>> wrote: >> >> Hi Jim, >> >> My apologies, I didn't mean to take this offline. But next >> time,I will keep this in mind. >> >> Thank you so much for your clarification on this design >> matrix. It really helps me a lot. So for finding DEG after >> taking strain variability into consideration, I should follow >> this: >> >> Straing <- factor(target$Strain) >> >> Condition <- target$Condition >> >> Design <- model.matrix(~Condition+Straing) >> >> Design >> >> (Intercept) ConditionKO Straing2 Straing3 Straing4 >> >> 111000 >> >> 211000 >> >> 311100 >> >> 411100 >> >> 510010 >> >> 61001 0 >> >> 710001 >> >> 810001 >> >> attr(,"assign") >> >> [1] 0 1 2 2 2 >> >> attr(,"contrasts") >> >> attr(,"contrasts")$Condition >> >> [1] "contr.treatment" >> >> attr(,"contrasts")$Straing >> >> [1] "contr.treatment" >> >> diff <- glmLRT(fit,coef=2) >> >> >> Regards >> >> >> >> On Sat, Dec 28, 2013 at 10:31 PM, James W. MacDonald >> <jmacdon@uw.edu <mailto:jmacdon@uw.edu="">> wrote: >> >> Hi Reema, >> >> Please don't take conversations off list. >> >> In your second parameterization you are pretty close, >> except the strain should be converted to a factor. In >> other words the design matrix should have just zeros and >> ones, instead of 1-4. >> >> Also note that you are not likely to get a lot of >> differential expression with just two samples per group. >> After you have reasonable depth per sample, sufficient >> replication is needed in order to detect anything but the >> largest differences. >> >> Best, >> >> Jim >> >> On Dec 27, 2013 1:51 PM, "Reema Singh" >> <reema28sep@gmail.com <mailto:reema28sep@gmail.com="">> wrote: >> >> Hello Jim, >> >> Thanks very much. I tried this design matrix and it >> works great. The contrast(I am using) first finds the >> DEG based on strain difference and then between >> control and KO. The resulting DEG number is very low. >> Here's my contrast >> >> my.ctst <- >> makeContrasts(deg=(trt_srtControl_3-trt_srtControl_4) >> - (trt_srtKO_1 - trt_srtKO_2),levels=design) >> >> >> diff <- glmLRT(fit,design,contrast = my.ctst) OR diff >> <- glmLRT(fit,design,contrast = c(1,-1,-1,1)) >> >> >> Is there any way to create contrast matrix to find out >> the DEG b/w Control and KO by adjusting the Starin >> difference instead of first finding DEG from different >> Strains? >> >> >> I have also tried different ways of creating design >> matrix and I am ended up totally confused. Here the >> another design matrix, I tried:- >> >> >> Straing <- target$Strain >> >> Condition <- targets$Condition >> >> Design <- model.matriix(~Condition+Straing) >> >> >> design1 >> >> (Intercept) ConditionKO Straing >> >> KO1-A111 >> >> KO1-B111 >> >> KO2-A112 >> >> KO2-B112 >> >> Cont1-A103 >> >> Cont1-B103 >> >> Cont2-A104 >> >> Cont2-B104 >> >> attr(,"assign") >> >> [1] 0 1 2 >> >> attr(,"contrasts") >> >> attr(,"contrasts")$Condition >> >> [1] "contr.treatment" >> >> >> diff <- glmLRT(fit,coef=2) >> >> >> Now this gives me more DEG. As I am using coef2, so is >> it finding the DEG b/w control vs ko after adjusting >> strain effect? Or is it simply finding the DEG the >> Control vs KO? >> >> >> Thank you >> >> Regards >> >> >> >> On Wed, Dec 25, 2013 at 5:59 PM, James W. MacDonald >> <jmacdon@uw.edu <mailto:jmacdon@uw.edu="">> wrote: >> >> Hi Reema, >> >> Instead of using that parameterization, you might >> consider creating a treatment-strain coefficient, >> which will tend to be more easily interpreted. So >> if you read in the sample table and then do: >> >> trt_srt <- paste(targets$Condition, >> targets$strain, sep = "_") >> design <- model.matrix(~ 0 + trt_srt) >> >> Then you can do any contrast between any >> treatment/strain combination you like, including >> the interaction, which tests for any differences >> between treatment that depend on the strain. >> >> Best, >> >> Jim >> >> On Dec 23, 2013 5:56 PM, "Reema Singh" >> <reema28sep@gmail.com>> <mailto:reema28sep@gmail.com>> wrote: >> >> Dear All, >> >> I have some queries regarding design matrix >> for two group(Control vs. KO) >> Differential expression.I am using edgeR for >> this. Here is the ording of my >> question:- 1) Sample table for design matrix, >> 2) Design matrix, 3) >> Questions. >> >> *1) Sample table for design matrix* >> >> >> Sample Strain Condition >> >> KO1-A 1 KO >> >> KO1-B 1 KO >> >> KO2-A 2 KO >> >> KO2-B 2 KO >> >> Cont1-A 1 Control >> >> Cont1-B 1 Control >> >> Cont2-A 2 Control >> >> Cont2-B 2 Control >> >> >> *2) Design Matrix* >> >> >> >> targets <- read.table(file= >> "Samples",sep="\t",header=TRUE) >> >> design <- model.matrix(~Strain+Condition,targets) >> >> > design >> >> (Intercept) Strain ConditionKO >> >> KO1-A 1 1 1 >> >> KO1-B 1 1 1 >> >> KO2-A 1 2 1 >> >> KO2-B 1 2 1 >> >> Cont1-A 1 3 0 >> >> Cont1-B 1 3 0 >> >> Cont2-A 1 4 0 >> >> Cont2-B 1 4 0 >> >> attr(,"assign") >> >> [1] 0 1 2 >> >> attr(,"contrasts") >> >> attr(,"contrasts")$Condition >> >> [1] "contr.treatment" >> >> >> *3) Questions* >> >> 1) *A)* During differential expression I >> also want to consider >> >> different strain variation along with the >> control vs KO variation. As far >> as I understand this design matrix only >> consider one Control vs KO for DE. >> I would like to known How I can make it >> consider strain variation as well? >> >> 2) *B)* After DE using the same design >> matrix with glmFit, I inspect >> >> the read count for the top down regulated gene >> and find out that the read >> count for this gene is very low in knockout as >> compare to Control . So is >> it means the comparison is Control vs KO and >> topTags gives the DEG list in >> KO as compare to Control? >> I would appreciate your suggestion. >> >> Kind Regards >> >> >> -- >> Reema Singh >> Postdoctoral Research Assistant >> College of Life Sciences >> University of Dundee, >> Dundee DD1 4HN, Scotland >> United Kingdom >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> <mailto:bioconductor@r-project.org> >> >> https://stat.ethz.ch/mailman/ >> listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane. >> science.biology.informatics.conductor >> >> >> >> >> -- Reema Singh >> Postdoctoral Research Assistant >> College of Life Sciences >> University of Dundee, >> Dundee DD1 4HN, Scotland >> United Kingdom >> >> >> >> >> -- Reema Singh >> Postdoctoral Research Assistant >> College of Life Sciences >> University of Dundee, >> Dundee DD1 4HN, Scotland >> United Kingdom >> >> >> >> >> -- >> Reema Singh >> Postdoctoral Research Assistant >> College of Life Sciences >> University of Dundee, >> Dundee DD1 4HN, Scotland >> United Kingdom >> > > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > > -- Reema Singh Postdoctoral Research Assistant College of Life Sciences University of Dundee, Dundee DD1 4HN, Scotland United Kingdom [[alternative HTML version deleted]]