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Code Block
pdf(file="scatterplot.pdf") csScatter(genes(cuff_data), 'C1', 'C2') dev.off() #How does this plot look? What is it telling us?

The resultant plot is here.

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

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Code Block

title	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()

The resultant plot is here.

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

Code Block

title	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()

The figure is here.

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	Solution
	Solution

Code Block

title	R command to generate box plot of gene level fpkms

csBoxplot(genes(cuff_data))

Code Block

title	R command to generate box plot of isoform level fpkms

csBoxplot(isoforms(cuff_data))

The resultant graph is here.

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	Hint
	Hint

Use csBoxplot function on cuff_data object to generate a boxplot of gene or isoform level fpkms.

The resultant plot is here.

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

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	Hint
	Hint

Use csVolcano function on cuff_data object to generate a volcano plot.

The resultant plot is here.

Exercise 7: MORE COMPLICATED! Generate a heatmap for genes that are upregulated and significant.

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Expand

	Hint
	Hint

Use csHeatmap function on the up_gene_data data structure we created. But its a little tricky.

The resultant plot is here.

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