dynamo.ext.enrichr

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.

Parameters:

  • 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.

Returns:

  • 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)