CalcFeatureSignificance

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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/score for that Feature.

Parameters

  • <expression_mode> : 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.

<zenbu_script>
   <parameters>
	<source_outmode>skip_metadata</source_outmode>
	<skip_default_expression_binning>true</skip_default_expression_binning>
   </parameters>
   <stream_stack>
	<spstream module="CalcFeatureSignificance">
		<expression_mode>sum</expression_mode>
	</spstream>
	<spstream module="TemplateCluster">
		<overlap_mode>height</overlap_mode>
		<expression_mode>sum</expression_mode>
		<ignore_strand>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>
			</spstream>
		</side_stream>
   </spstream>
</zenbu_script>