'macaqueDevBrain-concat_files-sc_nmf.h5ad')):
adata = sc.read(
os.path.join(basepath, 'macaqueDevBrain-concat_files.h5ad'))
adata = adata[subset, :]
sc.pp.filter_genes(adata, min_cells=15)
adata = sc_utils.sc_nmf(adata, n_components=k, save=True)
else:
adata = sc.read(
os.path.join(directory,
'macaqueDevBrain-concat_files-sc_nmf.h5ad'))
print(adata)
adata.raw = adata
print('KNNing')
neighbors = sc.Neighbors(
anndata.AnnData(
np.array(adata.obs.loc[:,
[x for x in adata.obs.keys()
if "nmf" in x]])))
neighbors.compute_neighbors(n_neighbors=100, metric="correlation")
adata.uns['neighbors'] = {}
adata.uns['neighbors']['distances'] = neighbors.distances
adata.uns['neighbors']['connectivities'] = neighbors.connectivities
sc.tl.louvain(adata)
sc.tl.umap(adata)
sc.pl.umap(adata, color=["louvain"], save="_NMFKNN_louvain")
sc.pl.umap(adata,
color=["louvain", "percent_mito", 'percent_ribo', "n_counts"],
save="_NMFKNN_stats")
sc.pl.umap(adata,
color=[x for x in adata.obs.keys() if "nmf" in x],
save="_NMFKNN_nmf")
eta=eta,
gamma=gamma,
eps=1e-5,
save=True)
else:
adata = sc.read(
os.path.join(directory,
'macaqueDevBrain-concat_files-sc_hdp.h5ad'))
adata.raw = adata
sc_utils.dirichlet_marker_analysis(adata,
markerpath='~/markers/Markers.txt')
print(adata)
logg.info('KNNING!')
print(adata)
sys.stdout.flush()
neighbors = sc.Neighbors(anndata.AnnData(adata.obsm['cell_topic']))
neighbors.compute_neighbors(n_neighbors=100, use_rep='X')
print('KNNed')
sys.stdout.flush()
adata.uns['neighbors'] = {}
adata.uns['neighbors']['distances'] = neighbors.distances
adata.uns['neighbors']['connectivities'] = neighbors.connectivities
print('louvaining')
sys.stdout.flush()
sc.tl.louvain(adata)
pd.DataFrame(
adata.obs.groupby(['louvain',
'region']).size().unstack(fill_value=0)).to_csv(
os.path.join(sc.settings.figdir,
"RegionCluster.csv"))
print('umapping')