Changes between Initial Version and Version 1 of SOPs/rna-seq-diff-expressions/DESeq

Timestamp:

01/23/13 16:49:43 (13 years ago)

Author:

trac

Comment:

--

SOPs/rna-seq-diff-expressions/DESeq

@@ +1 @@
+=== 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")''