dynamo.ext.enrichr(genes, organism, background=None, gene_sets=['GO_Biological_Process_2018'], description=None, outdir='./enrichr', cutoff=0.05, no_plot=False, **kwargs)[source]

Perform gene list enrichment with gseapy.

  • genes (Union[list, str]) – Flat file with list of genes, one gene id per row, or a python list object.

  • organism (str) – Enrichr supported organism. Select from (human, mouse, yeast, fly, fish, worm). see here for details: https://amp.pharm.mssm.edu/modEnrichr

  • gene_sets (Union[str, list, tuple] :param gene_sets:) – str, list, tuple of Enrichr Library name(s).

  • description (Optional[str]) – name of analysis. optional.

  • outdir (str) – Output file directory

  • cutoff (float) – Show enriched terms which Adjusted P-value < cutoff. Only affects the output figure. Default: 0.05

  • kwargs – additional arguments passed to the gp.enrichr function.

Return type

An Enrichr object, which obj.res2d stores your last query, obj.results stores your all queries.

>>> import dynamo as dyn
>>> adata = dyn.sample_data.pancreatic_endocrinogenesis()
>>> dyn.pp.recipe_monocle(adata, n_top_genes=1000, fg_kwargs={'shared_count': 20})
>>> dyn.tl.dynamics(adata, model='stochastic')
>>> dyn.tl.reduceDimension(adata, n_pca_components=30)
>>> dyn.tl.cell_velocities(adata)
>>> dyn.pl.streamline_plot(adata, color=['clusters'], basis='umap', show_legend='on data', show_arrowed_spines=False)
>>> # perform gene enrichment analysis which will create the enrichr folder with saved figures and txt file of the
>>> # enrichment analysis results and return an Enrichr object
>>> enr = dyn.ext.enrichr(adata.var_names[adata.var.use_for_transition].to_list(), organism='mouse', outdir='./enrichr')
>>> enr.results.head(5)
>>> # simple plotting function
>>> from gseapy.plot import barplot, dotplot
>>> # to save your figure, make sure that ``ofname`` is not None
>>> barplot(enr.res2d, title='GO_Biological_Process_2018', cutoff=0.05)
>>> dotplot(enr.res2d, title='KEGG_2016',cmap='viridis_r', cutoff=0.05)