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pdf(file="scatterplot.pdf") csScatter(genes(cuff_data), 'C1', 'C2') dev.off() |
The resultant plot is here.
Exercise 2a: Pull out from your data, significantly differentially expressed genes and isoforms.
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Exercise 2b: Pull out from your data, genes that are significantly differentially expressed and above log2 fold change of 1
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#Below command is equivalent to looking at the gene_exp.diff file that we spent a lot of time parsing yesterday gene_diff_data <- diffData(genes(cuff_data)) #Do gene_diff_data followed by tab to see all the variables in this data object sig_gene_data <- subset(gene_diff_data, (significant == 'yes')) up_gene_data <- subset(sig_gene_data, (log2_fold_change > 1)) #How many nrow(up_gene_data) #Pause here to consider a mistake I made yesterday down_gene_data <- subset(up_gene_data, (log2_fold_change < -1)) nrow(up_gene_data) |
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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.
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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.
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The resultant graph is here.
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Use csBoxplot function on cuff_data object to generate a boxplot of gene or isoform level fpkms. |
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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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Use csHeatmap function on the up_gene_data data structure we created. But its a little tricky. |
The resultant plot is here.