Enrichment Tool
Example usage of EnrichmentTool class
First select studies to use:
In [1]: from DAE import vDB
In [2]: studies = vDB.get_studies('ALL WHOLE EXOME')
In [3]: denovo_studies = [st for st in studies if 'WE' ==
st.get_attr('study.type')]
In [4]: autism_studies = [st for st in denovo_studies if 'autism' ==
st.get_attr('study.phenotype')]
Then create a background model object:
In [5]: from enrichment_tool.background import SamochaBackground
In [6]: background = SamochaBackground()
After that create a counter object:
In [7]: from enrichment_tool.event_counters import GeneEventsCounter
In [8]: counter = GeneEventsCounter()
Create an enrichment tool:
In [9]: from enrichment_tool.tool import EnrichmentTool
In [10]: tool = EnrichmentTool(background, counter)
Select a gene set to work with:
In [11]: from DAE import get_gene_sets_symNS
In [12]: gt = get_gene_sets_symNS('main')
In [13]: gene_set = gt.t2G['chromatin modifiers'].keys()
And then we are ready to perform the actual calculations:
In [14]: res = tool.calc(autism_studies, 'prb', 'LGDs', gene_set)
The result is a dictionary. The keys in the dictionary are:
In [16]: res.keys()
Out[16]: ['rec', 'all', 'male', 'female']
Each value in the dictionary is an instance of the class
EnrichmentResult:
In [19]: r = res['rec']
In [20]: len(r.events)
Out[20]: 39
In [21]: len(r.overlapped)
Out[21]: 9
In [22]: r.expected
Out[22]: 0.8992414922169882
In [23]: r.pvalue
Out[23]: 9.4660348870512223e-07