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mkatari-bioinformatics-august-2013-phytozome2gostats [2016/08/15 09:45] mkatarimkatari-bioinformatics-august-2013-phytozome2gostats [2016/08/15 09:47] (current) mkatari
Line 101: Line 101:
 gsc = phytozome2gostats("Macuminata_304_v1.annotation_info.txt", gsc = phytozome2gostats("Macuminata_304_v1.annotation_info.txt",
                         "Musa acuminata")                         "Musa acuminata")
-<code>+</code>
  
 Now we simply provide our genelist, which is normally obtained from differential expression analysis or clustering, and use all gene names from our gene count matrix as the background to identify GO-terms that are significant. The script will not perform an filtering, it is up to you to do that. Now we simply provide our genelist, which is normally obtained from differential expression analysis or clustering, and use all gene names from our gene count matrix as the background to identify GO-terms that are significant. The script will not perform an filtering, it is up to you to do that.
Line 109: Line 109:
 musa_genes = row.names(time12ddsMat_gentrt_res_sig) musa_genes = row.names(time12ddsMat_gentrt_res_sig)
  
-res_sig_goterms = runGostats(gsc, +res_sig_goterms = runGostats(gsc, musa_genes, musa_universe
-                            row.names(time12ddsMat_gentrt_res_sig), +
-                                                 row.names(allcounts))+
 </code> </code>
  
  
  
mkatari-bioinformatics-august-2013-phytozome2gostats.1471254353.txt.gz · Last modified: 2016/08/15 09:45 by mkatari