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What can you do with cummeRbund?

CummeRbund is powerful package with many different functions.  

Get your data

cds
cd my_rnaseq_course
cp -r /corral-repl/utexas/BioITeam/rnaseq_course/cuffdiff_results .
 
 

#We need the cuffdiff results because that is the input to cummeRbund.

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pdf(file="scatterplot.pdf")

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

dev.off()

Let's look at our scatterplot The resultant plot is here.

Exercise 2:  Pull out from your data, significantly differentially expressed genes and isoforms.

gene_diff_data  <- diffData(genes(cuff_data))
sig_gene_data  <- subset(gene_diff_data, (significant ==  'yes'))

#Count how many we have
nrow(sig_gene_data)

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)

The resultant plot is here.

Exercise 3: For a gene, regucalcin, plot gene and isoform level expression.

pdf(file="regucalcin.pdf")
mygene1 <- getGene(cuff_data,'regucalcin')
expressionBarplot(mygene1)
dev.off()expressionBarplot(isoforms(mygene1))
dev.off()

The resultant plot is here.

Exercise 4: For a gene, Rala, plot gene and isoform level expression.

pdf(file="rala.pdf")
mygene2 <- getGene(cuff_data, 'Rala')
expressionBarplot(mygene2)
expressionBarplot(isoforms(mygene2))

dev.off()

Take cummeRbund for a spin...

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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.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.
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.

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The resultant plot is here.

Exercise 6: Visualize the significant vs non-significant genes using a volcano plot.

The resultant plot is here.

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.

gene_diff_data  <- diffData(genes(cuff_data))
sig_gene_data  <- subset(gene_diff_data, (ln_fold_change > 21.5))
head(sig_gene_data)
sig_geneids <- c(sig_gene_data$gene_id)

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

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