Gene Expression Analysis of Time Course Experiment - Lecture Slides | STAT 5570, Study notes of Statistics

Download Gene Expression Analysis of Time Course Experiment - Lecture Slides | STAT 5570 and more Study notes Statistics in PDF only on Docsity! Gene expression analysis of time course experiments Reed Gann Statistical bioinformatics 6570 Spring ‘09 References •  Conesa, M. Nueda, A. Ferrer, and M. Tal´on. masigpro: a method to identify significant differential expression profiles in time course microarray experiments., 2005. •  Tusher, V., Tibshirani, R. and Chu, G. (2001): Significance analysis of microarrays applied to the ionizing radiation response" PNAS 2001 98: 5116-5121 Replicates •  Not discussed previously •  Allow us to get a better estimate of variance •  Define Biological vs technical replicates •  Rep plot; additional quality check required Rep Plot library (affy) data <- ReadAffy(celfile.path="~/Documents/idisk local/Spring '09/Statistical Bioinformatics/TIMECOURSE" Data <- rma(data) exprs.Data <- exprs(Data) dim(exprs.Data) plot(exprs.Data[,1],exprs.Data[,2], pch=16, cex=.8, xlab='Trt0tp30Rep1', ylab='Trt0tp30Rep2' ) abline(0,1) Rep Plot RG13-TRT4-TP120-REP2.CEL 2 4 6 8 10 2 14 RG34-4-120-1a.CEL maSigPro library (affy) data <- ReadAffy(celfile.path="~/Documents/idisk local/Spring '09/Statistical Bioinformatics/TIMECOURSE") #prepare data# Data <- rma(data) exprs.Data <- exprs(Data) T.cell <- c(rep(0,6),rep(1,6)) # O=control, 1=treatment gn <- rownames(exprs.Data) maSigPro.data <- list(x=exprs.Data, y=(T.cell), geneid=gn, genenames=gn, logged2=TRUE) colnames(exprs.Data) <- paste("Array", c(1:12), sep = "") #create experimental design# Time <- rep(c(rep(c(1:3), each = 2)), Replicates <- c(rep(c(1:6), each = 2)) Control <- c(rep(1, 6), rep(0, 6)) Treatl <- c(rep(0, 6), rep(1, 6)) edesign <- cbind(Time, Replicates, Control, Treatl) 2) #### RUN maSigPro library(maSigPro) tc.test <- maSigPro (exprs.Data, edesign, degree = 1, vars = “groups", main = "Test") tc.test$summary # shows significant genes by experimental groups maSigPro •  NO significant genes?!? •  This is a result of the model maSigPro model •  “maSigPro follows a two step regression strategy to find genes with significant temporal expression changes and significant differences between experimental groups”. •  Does not make any mention of repeated measures or accounting for time-treatment interactions •  Major issue-must characterize highest order interactions if significance is found doing a post hoc analysis SAM •  We get significant genes at an FDR of 5% •  At delta 1.68 get about 30 significant genes •  Model is more appropriate SAM •  Recall from our previous visit to SAM •  “relative difference” function •  s(i) is “gene-specific scatter” and is defined as the standard deviation of repeated expression measurements SAM vs. maSigPro •  SAM model accounts for repeated measurements that are a result of a time course experimental design-maSigPro is simply a two step regression method applied to each of the experimental factors •  Costly error-Significant genes vs. no significant genes •  Post-hoc analysis proves that the time/treatment interaction is significant (not shown)