Dear Jim, Good day and thanks for your prompt replied. This brings us to a pedantic discussion of what a p-value means, what a > statistical test is actually testing, etc. This is in general boring for > non statisticians, but is a fairly critical point that people should > understand. > > Without getting into the nuances (if interested, there was a recent > discussion on Simply Statistics; see point 4 here; > http://simplystatistics.org/2013/03/17/sunday-datastatistics-link- roundup-31713/), > when you are doing a statistical test you are only trying to reject the > null hypothesis, which in this case is that there is no interaction between > area and treatment. Failure to reject the null does not imply the converse > of proving the null true. > > There are any number of reasons why you couldn't reject the null here. You > might not have enough data, there might be an uncontrolled variable that is > messing things up, etc. At this point all you can say is that you have no > evidence to support an interaction between treatment and area. > You are right about that. I should rephrase my sentence in my previous email. My apologies, I'm not a statistician. Thanks for pointing out statistic fundamental concern. Have a nice day. Best regards, KJ Lim On 26 March 2013 15:24, James W. MacDonald <jmacdon@uw.edu> wrote: > Hi KJ, > > > On 3/26/2013 6:31 AM, KJ Lim wrote: > >> Dear Jim and community, >> >> I read the Limma User's Guide and read again the edgeR User's Guide. >> >> I believe the section 3.5 "Comparison both Between and Within Subjects" >> in the edgeR User's Guide is suitable to answer my question: Do phenotypes >> HS & LS have effect on the area (SW is control)? >> >> I constructed the design matrix as: >> >> > Phenotype <- factor(targets$phenotype, levels=c("HS", "LS")) >> > Area <- factor(targets$area, levels=c("SW","TZ")) >> > Tree <- gl(2,2, length=8) >> >> > design <- model.matrix(~Phenotype+**Phenotype:Tree+Phenotype:Area) >> >> > colnames(design) >> [1] "(Intercept)" "PhenotypeLS" "PhenotypeHS:Tree2" >> [4] "PhenotypeLS:Tree2" "PhenotypeHS:AreaTZ" "PhenotypeLS:AreaTZ" >> >> Then carried out the Likelihood Ratio Test (LRT): >> >> >lrtHvsL <- glmLRT(fit, contrast=c(0,0,0,0,1,-1)) >> >> > summary(decideTestsDGE(**lrtHvsL)) >> [,1] >> -1 1 >> 0 27849 >> 1 0 >> >> Based on the LRT result, can I concluded that phenotypes HS & LS have NO >> effect on the area? If this is not the way, kindly please advice. >> > > This brings us to a pedantic discussion of what a p-value means, what a > statistical test is actually testing, etc. This is in general boring for > non statisticians, but is a fairly critical point that people should > understand. > > Without getting into the nuances (if interested, there was a recent > discussion on Simply Statistics; see point 4 here; > http://simplystatistics.org/**2013/03/17/sunday-** > datastatistics-link- roundup-**31713/<http: simplystatistics.org="" 2013="" 03="" 17="" sunday-="" datastatistics-link-roundup-31713=""/>), > when you are doing a statistical test you are only trying to reject the > null hypothesis, which in this case is that there is no interaction between > area and treatment. Failure to reject the null does not imply the converse > of proving the null true. > > There are any number of reasons why you couldn't reject the null here. You > might not have enough data, there might be an uncontrolled variable that is > messing things up, etc. At this point all you can say is that you have no > evidence to support an interaction between treatment and area. > > Best, > > Jim > > > >> Thank you very much for your time and help. >> >> Have a nice day. >> >> Best regards, >> KJ Lim >> >> >> >> >> On 25 March 2013 09:20, KJ Lim <jinkeanlim@gmail.com <mailto:="">> jinkeanlim@gmail.com>> wrote: >> >> Dear Jim, >> >> Thanks for your prompt relied and suggestion. I will have a look >> on the Limma User's Guide. >> Thank you. >> >> Best regards, >> KJ Lim >> >> >> On 22 March 2013 17:28, James W. MacDonald <jmacdon@uw.edu>> <mailto:jmacdon@uw.edu>> wrote: >> >> Hi KJ, >> >> >> On 3/22/2013 11:17 AM, KJ Lim wrote: >> >> Dear Jim, >> >> Thanks for your replied. >> >> I used read.delim to read my data into R session. The >> target object is: >> >> files phenotype area >> 1 H3TZ.txt HS TZ >> 2 H3SW.txt HS SW >> 3 H4TZ.txt HS TZ >> 4 H4SW.txt HS SW >> 5 L2TZ.txt LS TZ >> 6 L2SW.txt LS SW >> 7 L3TZ.txt LS TZ >> 8 L3SW.txt LS SW >> >> I built the factors as: >> >> > phenotype <- factor(targets$phenotype) >> > area <- factor(ts.targets$area, levels=c("TZ","SW")) >> >> I want to know do the phenotypes (HS & LS) have effect on >> the area. I built the design matrix in each way and I >> found the colnames is not as you have suggested. >> >> >> Right. But that isn't the point I was trying to make (that the >> colnames would be something in particular). Instead, I was >> pointing out that the coefficients being estimated will be the >> same regardless, and the only difference is the order in the >> design matrix. >> >> >> >> > colnames(model.matrix(~**phenotype+area)) >> [1] "(Intercept)" "phenotypeL" "areaTZ" >> > colnames(model.matrix(~area+**phenotype)) >> [1] "(Intercept)" "areaTZ" "phenotypeL" >> >> Perhaps, I made mistakes in between? >> >> >> No, you illustrated my point as well. The two coefficients of >> note here are areaTZ and phenotypeL, and the only difference >> between the design matrices is the order that they appear. >> >> But you make a further statement above 'I want to know do the >> phenotypes (HS & LS) have effect on the area.'. This looks to >> me like you want the interaction term, which captures the >> difference in phenotype, given the area (or conversely, the >> differences in area, given phenotype). >> >> I believe there is at least one example of an interaction term >> in the limma User's Guide that you can look at to gain >> understanding. >> >> Best, >> >> Jim >> >> >> >> Thanks for your time. Have a nice weekend. >> >> Best regards, >> KJ Lim >> >> On 22 March 2013 16:17, James W. MacDonald <jmacdon@uw.edu>> <mailto:jmacdon@uw.edu> <mailto:jmacdon@uw.edu>> >> <mailto:jmacdon@uw.edu>>> wrote: >> >> Hi KJ, >> >> The short answer is that it doesn't matter, contingent >> upon you >> having set up the experiment correctly. Have you tried >> constructing the design matrix each way? >> >> > phenotype <- factor(rep(1:2, each=8)) >> > area <- factor(rep(1:2, times = 8)) >> > colnames(model.matrix(~area+**phenotype)) >> [1] "(Intercept)" "area2" "phenotype2" >> > colnames(model.matrix(~**phenotype+area)) >> [1] "(Intercept)" "phenotype2" "area2" >> >> The interpretation of each coefficient will be the >> same in both >> instances. >> >> Best, >> >> Jim >> >> >> >> >> On 3/22/2013 9:57 AM, KJ Lim wrote: >> >> Dear edgeR community, >> >> Good day. >> >> I have sets of RNA-Seq data of 2 phenotype (HighS, >> LowS) >> plants with area >> TZ& SW (control). I used exactTest to study >> which genes that >> are >> >> differential expressed in area TZ compare to SW of >> each phenotype. >> >> I would like to know do the phenotypes have effect >> on the >> area, in this >> case I should use glmLRT test. Before fit into the >> GLM, I >> defined my design >> matrix as: >> >> ~phenotype+area >> >> Could someone please advice me regarding the >> design matrix? >> Should I define >> my design matrix as ~area+phenotype ? >> >> Thank you very much for your time and help. >> >> Have a nice weekend. >> >> Best regards, >> KJ Lim >> >> [[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=""> >> >> >> -- 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 >> >> >> >> > -- > 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]]