Changes between Version 7 and Version 8 of SOPs/rna-seq-diff-expressions_TE

Timestamp:

02/25/21 10:10:30 (4 years ago)

Author:

twhitfie

Comment:

--

SOPs/rna-seq-diff-expressions_TE

@@ -32 +32 @@
 * **Assessing differential expression for genes and TEs**
     * After running TEcount on each sample in your experiment, the reported counts (i.e. a list of raw counts per gene/TE for each sample) can be combined into a counts matrix and analyzed following the steps outlined in the '''Statistics for differential expression''', '''Identifying differentially expressed genes''' and '''Accounting for a batch effect in a differential expression model''' sections of our [http://barcwiki.wi.mit.edu/wiki/SOPs/rna-seq-diff-expressions best practices] page.
-    * If the number of samples is not too large, the counting and analysis of differential expression can be carried out using a single execution of TEtranscripts (reverse stranded reads are shown in the example below, for forward stranded reads use --stranded forward and for unstranded reads use --stranded no (the default)):
+    * If the number of samples is not too large, the counting and analysis of differential expression can be carried out using a single execution of TEtranscripts ''instead'' of using TEcount (reverse stranded reads are shown in the example below, for forward stranded reads use --stranded forward and for unstranded reads use --stranded no (the default)):
 
 {{{
@@ -38 +38 @@
 bsub TEtranscripts --format BAM --stranded reverse -t /path/to/treat1.bam /path/to/treat2.bam -c /path/to/control1.bam /path/to/control2.bam --GTF /path/to/gene.gtf --TE /path/to/TE.gtf --mode multi --project treat_vs_control --minread 1 -i 100 --padj 0.05 --norm DESeq_default --sortByPos
 }}}
+    * The design for tests of differential expression above is a comparison between to biological contexts (e.g. treatment versus control, samples listed after the -t flag versus samples listed after the -c flag).  If your experimental design is more complex, you should use TEcounts with a subsequent custom analysis of differential expression.
+    * The output from TEtranscripts includes tests of differential expression carried out using [http://www.bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html DESeq2], in addition to a (sample x transcript) matrix of counts.
  * **Alternative software**
    * [https://github.com/nerettilab/RepEnrich2 RepEnrich2]