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In this lab, we'll look at how to use cummeRbund to visualize our gene expression results from cuffdiff.  CummeRbund is part of the tuxedo pipeline and it is an R package that is capable of plotting the results generated by cuffdiff.

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Figure from: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks, Trapnell et al, Nature Protocols, 2013. 

Get your data

#IF YOU HAVE NOT COPIED THIS OVER ALREADY
cds
cd my_rnaseq_course
cp -r /corral-repl/utexas/BioITeam/rnaseq_course_2015/day_3_partB/cuffdiff_results .

IF YOU HAVE ALREADY COPIED IT OVER
cds
cd my_rnaseq_course
cd day_3_partB/cuffdiff_results
 

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

Introduction

CummeRbund is powerful package with many different functions. Cuffdiff produces a lot of output files and all the output files are related by sets by IDs.

#If you have a local copy:
ls
-l cuffdiff_results

-rwxr-x--- 1 daras G-801020  2691192 Aug 21 12:20 isoform_exp.diff  : Differential expression testing for transcripts
-rwxr-x--- 1 daras G-801020  1483520 Aug 21 12:20 gene_exp.diff     : Differential expression testing for genes
-rwxr-x--- 1 daras G-801020  1729831 Aug 21 12:20 tss_group_exp.diff: Differential expression testing for primary transcripts
-rwxr-x--- 1 daras G-801020  1369451 Aug 21 12:20 cds_exp.diff      : Differential expression testing for coding sequences
 
-rwxr-x--- 1 daras G-801020  3277177 Aug 21 12:20 isoforms.fpkm_tracking
-rwxr-x--- 1 daras G-801020  1628659 Aug 21 12:20 genes.fpkm_tracking
-rwxr-x--- 1 daras G-801020  1885773 Aug 21 12:20 tss_groups.fpkm_tracking
-rwxr-x--- 1 daras G-801020  1477492 Aug 21 12:20 cds.fpkm_tracking
 
-rwxr-x--- 1 daras G-801020  1349574 Aug 21 12:20 splicing.diff  : Differential splicing tests
-rwxr-x--- 1 daras G-801020  1158560 Aug 21 12:20 promoters.diff : Differential promoter usage
-rwxr-x--- 1 daras G-801020   919690 Aug 21 12:20 cds.diff       : Differential coding output

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Figures from http://compbio.mit.edu/cummeRbund/manual_2_0.html

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MAKE SURE YOU ARE IN THE IDEV SESSION FOR THIS!
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title
Get set up
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A) First load R and enter R environment
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#module load R will not work for this R package because its needs the latest version of R. So, we'll load a local version.

/work/01184/daras/bin/R
 
B) DON'T RUN THIS-IT HAS ALREADY BEEN INSTALLED! Within R environment, install package cummeRbundthis is how cummeRbund was installed. 
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#DON'T RUN THIS-IT HAS ALREADY BEEN INSTALLED!
source("http://bioconductor.org/biocLite.R")
biocLite("cummeRbund")
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library(cummeRbund)
cuff_data <- readCufflinks('cuffdiffdiff_resultsout')

 
 
 
D) Use cummeRbund to visualize the differential expression results.
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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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title
To pull out significantly differentially expressed genes and isoforms
#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'))

#Count how many we have
nrow(sig_gene_data)

#Below command is equivalent to looking at the isoform_exp.diff file 
 
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)

 
Why do we have so many? Yesterday we saw
a
lot
less.
 
Exercise 2b:    Pull out from your data, genes that are significantly differentially expressed and above log2 fold change of 1
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title
To pull out genes
#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(sig_gene_data, (log2_fold_change < -1))
nrow(updown_gene_data)

Exercise 3: For a gene, regucalcin, plot gene and isoform level expression.
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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.
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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.
Take cummeRbund for a spin...
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Solution
Solution
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title
R command to generate box plot of gene level fpkms
csBoxplot(genes(cuff_data))
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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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Hint
Hint
Use csHeatmap function on the up_gene_data data structure we created. But its a little tricky.
The resultant plot is here.