def get_neuronal_enrichment(df):
clusters = df['kmeans_cluster_name'].unique()
dfl = []
for cl in clusters:
dfc = df[df['kmeans_cluster_name'] == cl].copy()
dfe = ai.get_enrichment(dfc.index.tolist(),
'neuronal_gene_set_library')
dfe2 = dfe[dfe['Adjusted P-value'] < 0.2].copy()
dfe2['cluster_name'] = [cl] * len(dfe2)
dfl.append(dfe2)
dfn = pd.concat(dfl)
dfp = dfn.pivot(index='Term',
columns='cluster_name',
values='Adjusted P-value')
return dfp
def get_cmap_sig(gene_list):
genes = [x for x in gene_list if x is not None]
library = 'LINCS_L1000_Chem_Pert_down'
dfe = ai.get_enrichment(genes, library)
dfe = dfe[dfe['Adjusted P-value'] < 0.2]
return dfe
def get_delta_enrichment(df, cond='mev_delta', delta_thresh=2):
df2 = df[df[cond] > delta_thresh].copy()
genes = df2.index.tolist()
dfe = ai.get_enrichment(genes, 'neuronal_gene_set_library')
return dfe
def get_cmap_sig(gene_list):
genes = [x for x in gene_list if x is not None]
library = 'LINCS_L1000_Chem_Pert_down'
dfe = ai.get_enrichment(genes, library)
dfe = dfe[dfe['Adjusted P-value'] < 0.2]
return dfe
