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--- mkatari-bioinformatics-august-2013-deseq [2013/08/23 14:18] – created mkatari
+++ mkatari-bioinformatics-august-2013-deseq [2013/08/23 15:53] – mkatari
@@ Line 1: / Line 1: @@
-[[mkatari-bioinformatics-august-2013|Manny's Bioinformatics Workshop]]
+[[mkatari-bioinformatics-august-2013|Back to Manny's Bioinformatics Workshop HOME]]
-Here we will discuss how to create an R script that can be executed on HPC. Majority of the script is the same except for the first few commands that read the arguments from command line.
+Here we will discuss how to create an R script (DESeq.R) that can be executed on HPC. Majority of the script is the same as if you were running it interactively except paths to the files are replaced with variables.
 If you are going to run DESeq in R on your desktop you will have to make sure DESeq is already installed.
- In order to install DESeq type the following:
+In order to install DESeq type the following:
 <code>
@@ Line 11: / Line 11: @@
 </code>
-However for the script that is available on HPC the script will automatically find DESeq. R uses variables to store locations where it should look for packages. Here we can simply add a path to where a specific module is located. This will prevent the need for others to have to install the module themselves. This is done int he following lines of the code
+However to make the script easy to run for anyone on the server, we will tell the R script where exactly to look for DESeq. R uses a variable (.libPaths) to store locations where it should look for packages. We will simply add the path to this variable. This way the person running the script does not need to have DESeq installed in their local R libraries. The other option is to tell the system administrator to add the packages. This is done in the following lines of the code
 <code>
@@ Line 24: / Line 24: @@
 </code>
-Now for our script we will use a command that allows the R to read in arguments from command line automatically. This will be helpful when we are using an R script in an analysis pipeline. The code that reads arguments from the command line are:
+Now in our script we will use a function (commandArgs) that will allow us to read in arguments from command line automatically. We will use the command Rscript followed by our code (DESeq.R) and the arguments will follow. The code will read one word at a time and save it as a character vector:
 <code>
@@ Line 33: / Line 33: @@
 </code>
+This will save all the words as a character vector in userargs. The value TRUE in the commandArgs argument make sure only the trailing arguments are saved. If the value is FALSE you will see additional R arguments when the command Rscript is executed. Notice the order of arguments is important. First we will provide the path to the count data file, then the path to the file containing the experimental design and finally the path to the directory where to save the results.
-The input for DESeq is a matrix/data.frame containing read counts. An example is provided [[https://docs.google.com/file/d/0B172nc4dAaaOMG44Zk1BT2NFdkU/edit?usp=sharing|here]]
+An example of the count data file is provided [[https://docs.google.com/file/d/0B172nc4dAaaOMG44Zk1BT2NFdkU/edit?usp=sharing|here]]
-#counts = read.table("NextGenRaw.txt", header=T, row.names=1)
+First we will load the count data file.
+<code>
 counts = read.table(pathToCountsData, header=T, row.names=1)
+</code>
-#This is simply meta-data to store information about the samples.
+Then we will load the experimental design. An example is provided [[https://docs.google.com/file/d/0B172nc4dAaaOaE5fTVVhUHJKazg/edit?usp=sharing|here]]:
-#expdesign = data.frame(
+<code>
-#  row.names=colnames(counts),
-#  condition=c("untreated","untreated","treated","treated"),
-#  libType=c("single-end","single-end","single-end","single-end")
-#)
 expdesign = read.table(pathToExpDesign)
+</code>
-#The counts that were loaded as a data.frame are now used to create
+The counts that were loaded as a data.frame are now used to create a new type of object: count data set
-#a new type of object-> count data set
+<code>
 cds = newCountDataSet(counts, expdesign$condition)
+</code>
-#Now we can perform operations on the dataset and save the results in
+Now we can perform operations on the dataset and save the results in the same object.
-#the same object.
-#first lets estimate the size factor based on the number of aligned reads
+First lets estimate the size factor based on the number of aligned reads from each sample.
-#from each sample.
+<code>
 cds = estimateSizeFactors(cds)
+</code>
-#to see the size factors:
+To see the size factors:
+<code>
 sizeFactors(cds)
+</code>
-#To perform a normalization you can simply use this command.
+To perform a normalization you can simply use this command. Note that the normalized values will not be used for identifying differentially expressed genes but we can use for some downstream analysis.
-#Note that the normalized values will not be used for identifying
+<code>
-#differentially expressed genes
 normalized=counts( cds, normalized=TRUE )
+</code>
-#An important part of DESeq is to estimate dispersion. This is simply
+An important part of DESeq is to estimate dispersion. This is simply a form of variance for the genes.
-#a form of variance for the genes.
+<code>
 cds = estimateDispersions( cds )
+</code>
-#To visualize the disperson graph
+To visualize the disperson graph
-pdf("Dispersion.pdf")
+<code>
+dispersionFile = paste(pathToOutputDir, "Dispersion.pdf", sep="")
+pdf(dispersionFile)
 plotDispEsts( cds )
 dev.off()
+</code>
 #To see the dispersion values which will be used for the final test
+<code>
 head( fData(cds) )
+</code>
-#Finally to perform the negative binomial test on the dataset to identify
+Finally to perform the negative binomial test on the dataset to identify differentially expressed genes.
-#differentially expressed genes.
+<code>
 res = nbinomTest( cds, "untreated", "treated" )
+</code>
-#An MA plot allows us to see the fold change vs level of expression.
+An MA plot allows us to see the fold change vs level of expression. In the plot, the red points are for genes that have FDR of 10%.
-#In the plot, the red points are for genes that have FDR of 10%.
+<code>
 pdf("MAplot.pdf")
 plotMA(res)
 dev.off()
+</code>
 #To get the genes that have FDR of 10%