= Updating to Reggie 4.27 =

**Note that BASE 3.16 is required for running Reggie 4.26.** 

== A. Before updating Reggie ==

=== 1. Install new/updated pipeline scripts ===

There are new pipeline scripts: http://baseplugins.thep.lu.se/browser/other/pipeline/trunk
Download and install at a suitable location on the prime cluster:

 * `novaseq_status.sh`: Used by auto-confirmation to check status of !NovaSeq sequencing.

=== 2. Install R-script for the Single Sample Predictor ===

TODO

=== 3. Import annotation types that are required for storing SSP results ===

The [attachment:ssp_annotationtypes.xlsx] file contains definitions for annotation types
required for the SSP models. Upload this file to BASE and use the batch annotation type 
importer to create the annotation types. It should create 16 new `SSP_*` annotation types. 


== B. Update Reggie ==

Update Reggie to version 4.27. Add the following models to the `<rscript>/<ssp>/<models>` section 
in `reggie-config.xml` (remove the `<model>` entry that is already there). Do not forget to run the Installation wizard.

{{{
<model name="Subtype" annotation-type="SSP_Subtype" annotation-type-scores="SSP_Subtype_Scores">
	Training_Run19081Genes_noNorm_SSP.PAM50subtype4Most.Fcc15_5x5foldCV.num.rules.50_21.selRules.AIMS.GS.RData
</model>
<model name="PAM50 subtype" annotation-type="SSP_PAM50subtype" annotation-type-scores="SSP_PAM50subtype_Scores">
	Training_Run19081Genes_noNorm_SSP.subtypeMost.Fcc15_5x5foldCV.num.rules.50_24.selRules.AIMS.GS.Rdata
</model>
<model name="ROR asT0" annotation-type="SSP_RORasT0" annotation-type-scores="SSP_RORasT0_Scores">
	Training_Run19081Genes_noNorm_SSP.scaled.ROR.tot.asT0.c005.Fcc15_5x5foldCV.num.rules.50_21.selRules.AIMS.GS.RData
</model>
<model name="ER" annotation-type="SSP_ER" annotation-type-scores="SSP_ER_Scores">
	Training_Run19081Genes_noNorm_SSP.ER_v2.Fcc15_5x5foldCV.num.rules.50_19.selRules.AIMS.GS.RData
</model>
<model name="PR" annotation-type="SSP_PR" annotation-type-scores="SSP_PR_Scores">
	Training_Run19081Genes_noNorm_SSP.PR_v2.Fcc15_5x5foldCV.num.rules.50_3.selRules.AIMS.GS.Rdata
</model>
<model name="ERBB2" annotation-type="SSP_ERBB2" annotation-type-scores="SSP_ERBB2_Scores">
	Training_Run19081Genes_noNorm_SSP.HER2.Fcc15_5x5foldCV.num.rules.50_8.selRules.AIMS.GS.RData
</model>
<model name="CC15" annotation-type="SSP_CC15" annotation-type-scores="SSP_CC15_Scores">
	Training_Run19081Genes_noNorm_SSP.CClusterK20_15_5x5foldCV.num.rules.50_27.selRules.AIMS.GS.Rdata
</model>
<model name="IntClust10" annotation-type="SSP_IntClust10" annotation-type-scores="SSP_IntClust10_Scores">
	Training_Run19081Genes_noNorm_SSP.iC10.mimic_5x5foldCV.num.rules.50_41.selRules.AIMS.GS.RData
</model>
}}}
 
== C. Run SSP for existing !StringTie raw bioassays ==

The configured SSP models are running automatically for new sequencing runs if auto-confirmation 
is selected. 

To run SSP for existing data, add !StringTie raw bioassays to the `Single Sample Predictor analysis`
item list. 

Then go to the **Start Single Sample Predictor analysis** wizard in the 
**Secondary analysis/Hisat and !StringTie pipeline** section and start jobs. 

It is possible to
run 500 raw bioassays at the same time.