Enrichr is a web tool for gene set enrichment analysis.
- Compatible with both python2 and python3
- Able to export part of the enrichment results
- Able to plot the enrichment results
> pip install git+https://github.com/pwwang/enrichr.git
> # will install requests, matplotlib
> # to run tests, you have to install testly
> # pip install git+https://github.com/pwwang/testly.git
> python test.py>>> from enrichr import Enrichr
>>> en = Enrichr(library = 'KEGG_2016')
>>> # use a different library:
>>> # en = Enrichr('ChEA_2016')
>>> # Add different cutoff
>>> en = Enrichr(cutoff = 0.01, top = 10)
>>> # Use adjusted p-value 0.01
>>> # whichever comes first, records with adjusted p-value or top>>> en.addList(['PHF14', 'RBM3', 'MSL1', ...])
>>> # set a description and show the userListId:
>>> # r = en.addList([...], description = 'Some description')
>>> # print r.userListId
>>> # 7260214> head genes.txt
#ID,Gene
1,PHF14
2,RBM3
3,MSL1>>> en.addListFromFile('genes.txt', col = 1, delimit = ',', skip = 1)>>> v = en.view()
>>> # if you have a different listid:
>>> # v = en.view(listid = 1234567)
>>> print v # namedtuple
Enrichr_Genelist(genes=[u'PHF14', u'RBM3', u'MSL1', ...], description=u'Enrichr gene list.')
>>> print v.genes, v.description
[u'PHF14', u'RBM3', u'MSL1', ...], 'Enrichr gene list.'>>> results = en.enrich(cutoff = .25)
>>> # perform enrichment on a different library and a different gene list:
>>> # results = en.enrich(library = 'ChEA_2016', listid = 123456)
>>> print results # a list of namedtuple
[Enrichr_Term(
Term = 'Longevity regulating pathway - multiple species_hsa04213',
Overlap = '2/64',
Pval = 0.00560341272708137,
AdjPval = 0.2241365090832548,
OldPval = 0.0023516996733944644,
OldAdjPval = 0.09406798693577857,
Z = -2.017962732978835,
CombinedScore = 10.461884526362898,
Genes = 'HSPA1L;INSR'), Enrichr_Term(...), ...]>>> en.enrich()
>>> en.export('results.txt')
>>> # only export top 3 terms:
>>> # en.export('results.txt', top = 3)> cat results.txt
Term Overlap Pval AdjPval OldPval OldAdjPval Z CombinedScore Genes
Longevity regulating pathway - multiple species_hsa04213 2/64 5.60E-03 2.24E-01 2.35E-03 9.41E-02 -2.018 10.462 HSPA1L;INSR
HIF-1 signaling pathway_hsa04066 2/103 1.40E-02 2.80E-01 5.85E-03 1.17E-01 -1.816 7.755 INSR;CUL2
Phospholipase D signaling pathway_hsa04072 2/144 2.62E-02 3.39E-01 1.11E-02 1.48E-01 -1.846 6.720 CYTH2;INSR
MAPK signaling pathway_hsa04010 2/255 7.32E-02 3.39E-01 3.23E-02 2.34E-01 -1.896 4.957 PPM1B;HSPA1L
...>>> en.enrich()
>>> en.plot('results.png')
>>> # set a different title and different number of terms to plot:
>>> # en.plot('results.png', title = 'Gene enrichment: {library}', top = 20)
>>> libs = en.genemap('AKT1') # it's a generator
>>> print libs
<generator object genemap at 0x2b127b472730>
>>> lib = next(libs)
>>> print lib # namedtuple
Enrichr_Library(name='ChEA_2016', category='Transcription', hasGrid=True, isFuzzy=True, format='{1} binds to the promoter region of {0}.', description='', terms=['EGR1_19374776_ChIP-ChIP_THP-1_Human', 'CLOCK_20551151_ChIP-Seq_293T_Human', ...])