The CutoffFilter processing module is designed to operate on the significance of Features and perform high pass, low pass, or band pass filtering. It is often used after CalcFeatureSignificance which combines the multiple experiment/expression data of a Feature into a single significance for that Feature.
- <min_cutoff> : Features with significance less than min_cutoff are filtered out of the data stream.
- <max_cutoff> : Features with significance above max_cutoff are filtered out of the data stream.
- <filter_by_experiment> : set to true to perform filter testing at Experiment/Expression level (not feature significance) level. At least one Experiment in the collection of the Feature must pass the min/max filtering criteria. If no Experiment passes the criteria, the feature is removed from the data stream. The collection of Experiment/Expression is not altered.
This is a complex script which incorporates a FeatureEmitter / TemplateCluster expression histogram binning with de-novo clustering via Paraclu followed by CalcFeatureSignificance and then several filtering steps including NeighborCutoff, CutoffFilter, and FeatureLengthFilter
<zenbu_script> <stream_queue> <spstream module="TemplateCluster"> <overlap_mode>area</overlap_mode> <expression_mode>sum</expression_mode> <side_stream> <spstream module="FeatureEmitter"> <width>1</width> <fixed_grid>true</fixed_grid> <both_strands>true</both_strands> </spstream> </side_stream> </spstream> <spstream module="Paraclu"> <min_cutoff>10</min_cutoff> <stability>0</stability> <max_cluster_length>100</max_cluster_length> </spstream> <spstream module="CalcFeatureSignificance"> <expression_mode>sum</expression_mode> </spstream> <spstream module="NeighborCutoff"> <ratio>300</ratio> <distance>100</distance> </spstream> <spstream module="CutoffFilter"> <min_cutoff>100</min_cutoff> </spstream> <spstream module="FeatureLengthFilter"> <max_length>50</max_length> </spstream> </stream_queue> </zenbu_script>
Example ZENBU view showing this script in use