I think that you need to start by learning some elementary statistics. There are lots of good books out there. I like the ones by Ott (or Ott and Longnecker) and by Devore and Peck. Read a few pages a day, work through the examples in the text and you'll be much better equipped to handle your analyses in about 3 weeks. --Naomi At 08:25 AM 9/1/2010, Johnny H wrote: >Hi. >I have found some R/Bioconductor/Genominator code on the web (below) and it >measures differential expression of RNA-seq short read data using a general >linear model. > >Can someone explain some basic questions I have? > >1) What is the reason for using 2 glm's for measuring differential >expression? > >2) In the function(y) there are two linear models ran; one with argument y ~ >groups and the other with argument y ~ 1. Why do this? > >3) What does the offset do? > >4) Why use ANOVA; is to compare the linear models? > >5) What can we say about results, if adjusted for multiple testing; how >would you explain a significant result? > >6) Would an adjusted p-value of <= 0.05 be significant? > >Basically, I don't know much about the statistics done below and any advice >or pointers to good literature for this would be a great help. Thank you. > > > head(geneCountsUI) > mut_1_f mut_2_f wt_1_f wt_2_f >YAL069W 0 0 0 0 >YBL049W 19 18 10 4 > ># Normalisation of RNA-seq lanes >notZero <- which(rowSums(geneCountsUI) != 0) >upper.quartiles <- apply(geneCountsUI[notZero, ], 2, function(x) quantile(x, >0.75)) >uq.scaled <- upper.quartiles/sum(upper.quartiles) * sum(laneCounts) > ># Calculating differential expression >groups <- factor(rep(c("mut", "wt"), times = c(2, 2))) > >pvalues <- apply(geneCountsUI[notZero, ], 1, > function(y) { > fit <- glm(y ~ groups, family = poisson(), offset = log(uq.scaled)) > fit0 <- glm(y ~ 1, family = poisson(), offset = log(uq.scaled)) > anova(fit0, fit, test = "Chisq")[2, 5] >}) > > [[alternative HTML version deleted]] > >_______________________________________________ >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 Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111