RNA-Seq VisualizationcummrRbund in Atmosphere

Jason Williams iPlant / Cold Spring Harbor Laboratory

*Graphics taken from these publications

The Tuxedo Protocol

*TopHat and Cufflinks require a sequenced genome

Tophat

TopHat outputs in IGV

Using CummeRbund in Atmosphere

Using CummeRbund in Atmosphere

• Visualize and mine Cuffdiff results• Output files from Cuffdiff are reorganized into a local database

Choose the right image

We will be using “RNA-Seq Visualization”Rmi-BE9C2D12

Any image w/R can work, and you could also searchFor an image with cummeRbund installed

Installing cummeRbund in R

Installing cummeRbund in R

Reading the data in

> cuff <- readCufflinks()

> cuff

CuffSet instance with: 2 samples 33714 genes 43481 isoforms 35113 TSS 32924 CDS 33621 promoters 35113 splicing 27350 relCDS

Visualizing 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

Visualizing dispersion

http://www.fgcz.ch/education/StatMethodsExpression/03_Count_data_analysis.pdf

Poisson adequately describes technical variation

Visualizing dispersion

Squared-coefficient of Variation (SCV)

>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

Distributions of FPKM scores across samples

>dens<-csDensity(genes(cuff))>dens

>densRep<-csDensity(genes(cuff),replicates=T)>densRep

Non-parametric estimate of pdf

FPKMPairwise Scatter Plots

> csScatter(genes(cuff),‘WT’,‘hy5’,smooth=T)

Saving your Plots

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

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

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’)

Heat mapping Similar Expression Values

>sigGenes <-getGenes(cuff,tail(sig,50))#last 50 genes in the list we created of genes

>csHeatmap(sigGenes,cluster=‘both’)

Heat mapping Similar Expression Values

>csHeatmap(sigGenes,cluster=‘both’,replicates=‘T’)

Expression Plots by Genes

> myGeneId<-”AT5G41471"

> myGene<-getGene(cuff,myGeneId)

> myGene

Expression Plots by Genes

> expressionPlot(myGene,replicates=‘T’)