Visualization using CummeRbund 2014 Overview

Transcription

1 Visualization using CummeRbund 2014 Overview In this lab, we'll look at how to use cummerbund to visualize our gene expression results from cuffdiff. CummeRbund is part of the tuxedo pipeline and it is an R package that is capable of plotting the results generated by cuffdiff.

2 Figure from: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks, Trapnell et al, Nature Protocols, 2013.

3 Introduction CummeRbund is powerful package with many different functions. Cuffdiff produces a lot of output files and all the output files are related by sets by IDs. #If you have a local copy: ls -l cuffdiff_results cuffdiff output -rwxr-x--- 1 daras G Aug 21 12:20 isoform_exp.diff : Differential expression testing for transcripts -rwxr-x--- 1 daras G Aug 21 12:20 gene_exp.diff : Differential expression testing for genes -rwxr-x--- 1 daras G Aug 21 12:20 tss_group_exp.diff: Differential expression testing for primary transcripts -rwxr-x--- 1 daras G Aug 21 12:20 cds_exp.diff : Differential expression testing for coding sequences -rwxr-x--- 1 daras G Aug 21 12:20 isoforms.fpkm_tracking -rwxr-x--- 1 daras G Aug 21 12:20 genes.fpkm_tracking -rwxr-x--- 1 daras G Aug 21 12:20 tss_groups.fpkm_tracking -rwxr-x--- 1 daras G Aug 21 12:20 cds.fpkm_tracking -rwxr-x--- 1 daras G Aug 21 12:20 splicing.diff : Differential splicing tests -rwxr-x--- 1 daras G Aug 21 12:20 promoters.diff : Differential promoter usage -rwxr-x--- 1 daras G Aug 21 12:20 cds.diff : Differential coding output CummeRbund makes managing and querying this data easier by loading the data into multiple objects of several different classes and having functions to query them. Because all of this gets stored in an sql database, you can access it quickly without loading everything in to memory. readcufflinks- Most important function designed to read all the output files that cuffdiff generates into an R data object (of class CuffSet). cuff_data <- readcufflinks(diff_out) CummeRbund has at least 6 classes that it will place different parts of your data in. Now you can access information using different functions: gene information using genes(cuff_data), your isoform level output using isoforms(cuff_data), TSS related groups using tss(cuff_data) and so forth What can you do with cummerbund?

4 Global statistics on data for quality, dispersion, distribution of gene expression scores etc. Plot things for specific features or genes Data exploration- This are more advanced steps. You can look at your data in a genome browser like view, do PCA, do clustering etc. It claims to be able to even do GO analysis.

5 Figures from Get your data MAKE SURE YOU ARE IN THE IDEV SESSION FOR THIS! Get set up cds cd my_rnaseq_course cp -r /corral-repl/utexas/bioiteam/rnaseq_course/diff_results. cd diff_results #We need the cuffdiff results because that is the input to cummerbund. A) First load R and enter R environment

6 #module load R will not work for this R package because its needs the latest version of R. So, we'll load a local version. $BI/bin/R What is $BI here? B) Within R environment, install package cummerbund source(" bioclite("cummerbund") C) Load cummerbund library and read in the differential expression results. If you save and exit the R environment and return, these commands must be executed again. library(cummerbund) cuff_data <- readcufflinks('cuffdiff_results') D) Use cummerbund to visualize the differential expression results. NOTE: Any graphic outputs will be automatically saved as "Rplots.pdf" which can create problems when you want to create multiple plots with different names in the same process. To save different plots with different names, preface any of the commands below with the command: pdf(file="myplotname.pdf") And after executing the necessary commands, add the line: Exercise 1: Generate a scatterplot comparing genes across conditions C1 vs C2.

7 pdf(file="scatterplot.pdf") csscatter(genes(cuff_data), 'C1', 'C2') #How does this plot look? What is it telling us? Exercise 2a: Pull out from your data, significantly differentially expressed genes and isoforms. To pull out significantly differentially expressed genes and isoforms #Below command is equivalent to looking at the gene_exp.diff file that we spent a lot of time parsing yesterday gene_diff_data <- diffdata(genes(cuff_data)) #Do gene_diff_data followed by tab to see all the variables in this data object sig_gene_data <- subset(gene_diff_data, (significant == 'yes')) #Count how many we have nrow(sig_gene_data) #Below command is equivalent to looking at the isoform_exp.diff file isoform_diff_data <-diffdata(isoforms(cuff_data)) sig_isoform_data <- subset(isoform_diff_data, (significant == 'yes')) #Count how many we have nrow(sig_isoform_data) Why do we have so many? Yesterday we saw a lot less. Exercise 2b: Pull out from your data, genes that are significantly differentially expressed and above log2 fold change of 1

8 To pull out genes #Below command is equivalent to looking at the gene_exp.diff file that we spent a lot of time parsing yesterday gene_diff_data <- diffdata(genes(cuff_data)) #Do gene_diff_data followed by tab to see all the variables in this data object sig_gene_data <- subset(gene_diff_data, (significant == 'yes')) up_gene_data <- subset(sig_gene_data, (log2_fold_change > 1)) #How many nrow(up_gene_data) #Pause here to consider a mistake I made yesterday down_gene_data <- subset(sig_gene_data, (log2_fold_change < -1)) nrow(up_gene_data) Exercise 3: For a gene, regucalcin, plot gene and isoform level expression. To plot gene level and isoform level expression for gene regucalcin pdf(file="regucalcin.pdf") mygene1 <- getgene(cuff_data,'regucalcin') expressionbarplot(mygene1) expressionbarplot(isoforms(mygene1)) Exercise 4: For a gene, Rala, plot gene and isoform level expression. To plot gene level and isoform level expression for gene Rala pdf(file="rala.pdf") mygene2 <- getgene(cuff_data, 'Rala') expressionbarplot(mygene2) expressionbarplot(isoforms(mygene2)) Take cummerbund for a spin... CummeRbund is powerful package with many different functions. Above was an illustration of a few of them. Try any of the suggested exercises below to further explore the differential expression results with different cummerbund functions.

9 If you would rather just look at the resulting graphs, the links to each of the resultant graphs is given below the commands. You can refer to the cummerbund manual for more help and remember that?functionname will provide syntax information for different functions. Exercise 5: Visualize the distribution of fpkm values across the two different conditions using a boxplot. Solution csboxplot(genes(cuff_data)) R command to generate box plot of gene level fpkms R command to generate box plot of isoform level fpkms csboxplot(isoforms(cuff_data)) The resultant graph is here. Hint Use csboxplot function on cuff_data object to generate a boxplot of gene or isoform level fpkms. Exercise 6: Visualize the significant vs non-significant genes using a volcano plot. Solution csvolcano(genes(cuff_data), "C1", "C2") Hint Use csvolcano function on cuff_data object to generate a volcano plot. Exercise 7: MORE COMPLICATED! Generate a heatmap for genes that are upregulated and significant. Solution geneids <- c(up_gene_data$gene_id) mygenes <- getgenes(cuff_data, geneids) pdf("heatmap.pdf") csheatmap(mygenes, cluster="both") Hint Use csheatmap function on the up_gene_data data structure we created. But its a little tricky.