1. The iPlant Collaborative Community Cyberinfrastructure for Life Science Tools and Services Workshop RNA-Seq visualization with cummeRbund

2. Papers and source materials Useful References *Graphics taken from these publications

3. Tuxedo Workflow Differential expression *TopHat and Cufflinks require a sequenced genome

4. Discovery Environment Using a GUI Tophat (bowtie) Cufflinks Cuffmerge Cuffdiff CummeRbund Your Data iPlant Data Store FASTQ Discovery Environment Atmosphere

5. CummeRbund Bioconductor R library; Getting started in Atmosphre “Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations.” Any image w/R can work, and you could also search for an image with cummeRbund installed

6. Bring your Data into Atmosphere Using iCommands or iDrop

7. Connect with VNC Visualization use case for Atmosphere VNC Viewer

8. Installing CummeRbund Available via Bioconductor

9. Examine the cufflinks data > cuff <- readCufflinks() > cuff CuffSet instance with: 2 samples 33714 genes 43481 isoforms 35113 TSS 32924 CDS 33621 promoters 35113 splicing 27350 relCDS

10. Visualize sample dispersion >disp<-dispersionPlot(genes(cuff)) >disp Counts vs. dispersion Overdispersion greater variability in a data set than would be expected based on a given model ( in our case extra-Poisson variation) If you use Poisson model, you will overestimate differential expression

11. Variation matters http://www.fgcz.ch/education/StatMethodsExpression/03_Count_data_analysis.pdf Poisson adequately describes technical variation

12. Overdispersed Data

13. Squared Coefficient of Variation >genes.scv<-fpkmSCVPlot(genes(cuff)) >genes.scv Normalized measure of cross-replicate variability Represents the relationship of the standard deviation to the mean Differences in SCV can result in lower numbers of differentially expressed genes due to a higher degree of variability between replicate fpkm estimates

14. Distributions of FPKM scores across samples >dens<-csDensity(genes(cuff)) >dens >densRep<-csDensity(genes(cuff),replicates=T) >densRep Non-parametric estimate of pdf

15. FPKM Pairwise Scatter Plots > csScatter(genes(cuff),‘WT’,‘hy5’,smooth=T)

16. Saving your Plots Just in case you are not working in R studio 1. Plot type: >(e.g. jpeg, png, pdf) (file_path_and_file_name) 2. Plot function 3. dev.off() > png (‘csScatter.png’) #Will save in working directory > csScatter(genes(cuff),‘WT’,‘hy5’,smooth=T) >dev.off

17. Selecting and Filtering Gene Sets Using the ‘getSig’ function # Enables you to get genes at significance n >sig <-getSig(cuff, alpha=0.05, level =‘genes’) # genes of significance 0.05 >length(sig) #returns the number of genes in the sig object >sig <-getSig(cuff, alpha=0, level=‘genes’) >tail(sig,100) #displays the last 100 genes in the sig object you just made

18. Selecting and Filtering Gene Sets Using the ‘getGenes’ function # Get the gene information >sigGenes <- getGenes(cuff,sig) Plot this in another scatter plot >csScatter(sigGenes, ‘WT’, ‘hy5’)

19. Heat mapping Similar Expression Values >sigGenes <-getGenes(cuff,tail(sig,50)) #last 50 genes in the list we created >csHeatmap(sigGenes,cluster=‘both’)

20. Heat mapping Similar Expression Values >csHeatmap(sigGenes,cluster=‘both’,replicates=‘T’)

21. Expression Plots by Genes > myGeneId<-”AT5G41471" > myGene<-getGene(cuff,myGeneId) > myGene

22. Expression Plots by Genes > expressionPlot(myGene,replicates=‘T’)

23. Keep asking: ask.iplantcollabortive.org

24. The iPlant Collaborative is funded by a grant from the National Science Foundation Plant Cyberinfrastructure Program (#DBI-0735191).