1 / 4

Investigating Top 20 Genes in Rice Array Experiment Using R Annotation Tools

This assignment involves utilizing R annotation tools to analyze the top 20 genes from the Rice array experiment. You will submit a gene list to David and explore the findings. Investigate gene-specific p-values (p < 0.05), posterior p-values (p < 0.05), and posterior FDR p-values (FDR < 0.20). Determine any qualitative differences and quantify the number of genes in each category. Furthermore, learn to compute the overlap among these gene categories for a comprehensive understanding of the results.

heinz
Télécharger la présentation

Investigating Top 20 Genes in Rice Array Experiment Using R Annotation Tools

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Assignment 5 • Use the R annotation tools to investigate the top 20 genes in the Rice array experiment. • Submit a gene list to David and see what you find. • Try this with • Gene Specific p-values < 0.05 • Posterior p-values < 0.05 • Posterior FDR p-values < 0.20 • Are there any qualitative differences? How many genes are in each of the three categories? • Can you figure out how to compute the overlap?

  2. > library(affy) > rrdata<- ReadAffy() > eset <- rma(rrdata) > group <- factor(c(0,0,1,1,2,2,3,3,4,4,5,5)) > vlist <- list(group=group) > eset.lmg <- neweS(exprs(eset),vlist) > lmg.results <- LMGene(eset.lmg) > pv1 <- genediff(eset.lmg) > names(pv1) [1] "Gene.Specific" "Posterior" > pv2 <- pvadjust(genediff.results) > names(pv2) [1] "Gene.Specific" "Posterior" "Gene.Specific.FDR" [4] "Posterior.FDR" > sum(pv2$Gene.Specific < .05) [1] 2615 > sum(pv2$Posterior < .05) [1] 3082 > sum(pv2$Posterior.FDR < .20) [1] 3360 SPH 247 Statistical Analysis of Laboratory Data

  3. > list1 <- pv1$Gene.Specific < .05 > list2 <- pv1$Posterior < .05 > list3 <- pv2$Posterior.FDR < .20 > gene.background<- featureNames(eset) > sig.genes1 <- gene.background[list1] > sig.genes2 <- gene.background[list2] > sig.genes3 <- gene.background[list3] > write(sig.genes1,"sig.genes1.txt") > write(sig.genes2,"sig.genes2.txt") > write(sig.genes3,"sig.genes3.txt") > write(gene.background,"gene.background.txt") > table(list1,list2,list3) , , list3 = FALSE list2 list1 FALSE TRUE FALSE 8960 0 TRUE 305 0 , , list3 = TRUE list2 list1 FALSE TRUE FALSE 87 963 TRUE 16 2294[1] 1173

  4. GS < .05 2615 Posterior < .05 3082 305 0 0 2294 16 963 87 Posterior FDR < .20 3360 SPH 247 Statistical Analysis of Laboratory Data

More Related