Visualization using CummeRbund

A) First load R and enter R environment

module load R
R

B) Within R environment, set up cummeRbund

source("http://bioconductor.org/biocLite.R")
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('diff_out')

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:

dev.off()

Thus, to use the csScatter command and save the results in a file called scatterplot.pdf, one would execute the following commands:

pdf(file="scatterplot.pdf")

csScatter(genes(cuff_data), 'C1', 'C2')

dev.off()

To pull out significantly differentially expressed genes and isoforms

gene_diff_data  <- diffData(genes(cuff_data))
sig_gene_data  <- subset(gene_diff_data, (significant ==  'yes'))
nrow(sig_gene_data)
isoform_diff_data <-diffData(isoforms(cuff_data))
sig_isoform_data <- subset(isoform_diff_data, (significant == 'yes'))
nrow(sig_isoform_data)

To draw a scatterplot

csScatter(genes(cuff_data), 'C1', 'C2')

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))

dev.off()

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))

dev.off()

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.

If you would rather just look at the resulting graphs, they are at the URL: http://loving.corral.tacc.utexas.edu/bioiteam/tophat_cufflinks/ as exercise5_Rplots.pdf, exercise6_Rplots.pdf, and exercise7_Rplots.pdf.

You can refer to the cummeRbund manual for more help and remember that ?functionName will provide syntax information for different functions.
http://compbio.mit.edu/cummeRbund/manual.html

You may need to redo Step C) when you reopen an R session.

Exercise 5: Visualize the distribution of fpkm values across the two different conditions using a boxplot.

Solution

R command to generate box plot of gene level fpkms

csBoxplot(genes(cuff_data))

R command to generate box plot of isoform level fpkms

csBoxplot(isoforms(cuff_data))

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: Pull out a subset of the genes using a ln_fold_change greater than 1.5. Generate expression bar plots for just those genes.

Solution

One possible solution

gene_diff_data  <- diffData(genes(cuff_data))
sig_gene_data  <- subset(gene_diff_data, (ln_fold_change > 2))
head(sig_gene_data)
sig_geneids <- c(sig_gene_data$gene_id)

myGenes <- getGenes(cuff_data, sig_geneids)
expressionBarplot(myGenes)

If you have trouble sourcing cummeRbund, try this:
module swap gcc intel
Reenter R and redo above steps.