=== Using DESeq ===
  * For experiments with or without replication
  * See [[http://www.bioconductor.org/packages/devel/bioc/html/DESeq.html|the DESeq home page]] for official documentation
  * Also see our [[http://jura.wi.mit.edu/bio/education/R2011/|Hot Topics RNA-Seq vignette]] (session 3, third part) for another discussion of the use of DESeq.
  * Package summary: "Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution"
  * From the DESeq publication: "DESeq owes its basic idea to edgeR, yet differs in several aspects."
  * Input is also a matrix of counts, with gene identifiers used as row names.
  * Sample code to use __with__ replication:
 
{{{
 # Use package
 library(DESeq)
 # Read counts
 countsTable = read.delim("Gene_counts.txt")
 # Describe groups
 groups = c(rep("C",3), rep("T", 3))
 # Make a CountDataSet
 cds = newCountDataSet(countsTable, groups)
 # Estimate effective library size
 cds = estimateSizeFactors(cds)
 # Core assumption of the method: variance is a function of expression level
 # Estimate variance for each gene
 cds = estimateDispersions(cds)
 # Do stats based on a negative binomial distribution
 results = nbinomTest(cds, "T", "C")
 write.table(results, file="DESeq_output.txt", sep="\t", quote=F)
}}}

 *  Sample code to use __without__ replication:

{{{ 
# Show that all samples are different
 conds = c("a", "b", "c", "d", "e", "f")
}}}


    * Then apply the same code as above except for ''estimateVarianceFunctions()'':
         * ''cds = estimateVarianceFunctions(cds, method='blind')''

    * Make desired comparison
         * ''results = nbinomTest(cds, "a", "b")''