CalcFeatureSignificance

Data Stream Processing > Processing Modules > General manipulation Modules

Description
The CalcFeatureSignificance processing module is designed to sit in the middle of a processing stream and transform the multiple Experiment / Expression data of a Feature into the single significance for that Feature.

Parameters

 *  : defines how expression between different Experiments are combined together when calculating the Feature significance. Possible values are:
 * sum : sum the expression between different Experiments into the significance.
 * min : calculate the minimum expression value among different Experiments
 * max : calculate the maximum expression value among different Experiments
 * count : count the number of different Experiments of the Feature.
 * mean : calculate the average expression value among the different Experiments of the feature

Example
This script combines FeatureEmitter / TemplateCluster strandless, expression histogram binning with a CalcFeatureSignificance. This can then be visualized in a hybrid track using a color spectrum.

 skip_metadata true   sum  height sum true</ignore_strand> <overlap_subfeatures>true</overlap_subfeatures> <side_stream> <spstream module="FeatureEmitter"> <num_per_region>970</num_per_region> <fixed_grid>true</fixed_grid> <both_strands>false</both_strands> </side_stream> </zenbu_script>