def main():
gn = Granatum()
n_neighbors = gn.get_arg('nNeighbors', 15)
neighbor_method = gn.get_arg('neighborMethod', 'gauss')
assay = gn.get_import('assay')
adata = sc.AnnData(np.array(assay.get('matrix')).transpose())
adata.var_names = assay.get('geneIds')
adata.obs_names = assay.get('sampleIds')
sc.pp.neighbors(adata,
n_neighbors=n_neighbors,
use_rep='X',
method=neighbor_method)
sc.tl.dpt(adata, n_branchings=1)
gn._pickle(adata, 'adata')
# dpt_groups
for spec in [{
'col': 'dpt_order',
'caption': 'Cell order'
}, {
'col': 'dpt_groups',
'caption': 'Cell groups'
}]:
fig = plt.figure()
sc.pl.diffmap(adata, color=spec['col'])
gn.add_current_figure_to_results(spec['caption'])
gn.export_statically(
dict(
zip(adata.obs_names.tolist(),
adata.obs[spec['col']].values.tolist())), spec['col'])
gn.commit()
def main():
tic = time.perf_counter()
gn = Granatum()
assay = gn.get_import('assay')
sample_ids = assay.get('sampleIds')
group_dict = gn.get_import('groupVec')
group_vec = pd.Categorical([group_dict.get(x) for x in sample_ids])
num_groups = len(group_vec.categories)
figheight = 400 * (math.floor((num_groups - 1) / 7) + 1)
adata = sc.AnnData(np.array(assay.get('matrix')).transpose())
adata.var_names = assay.get('geneIds')
adata.obs_names = assay.get('sampleIds')
adata.obs['groupVec'] = group_vec
sc.pp.neighbors(adata, n_neighbors=20, use_rep='X', method='gauss')
try:
sc.tl.rank_genes_groups(adata, 'groupVec', n_genes=100000)
sc.pl.rank_genes_groups(adata, n_genes=20)
gn.add_current_figure_to_results('One-vs-rest marker genes',
dpi=75,
height=figheight)
gn._pickle(adata, 'adata')
rg_res = adata.uns['rank_genes_groups']
for group in rg_res['names'].dtype.names:
genes_names = [str(x[group]) for x in rg_res['names']]
scores = [float(x[group]) for x in rg_res['scores']]
newdict = dict(zip(genes_names, scores))
gn.export(newdict,
'Marker score ({} vs. rest)'.format(group),
kind='geneMeta')
newdictstr = [
'"' + str(k) + '"' + ", " + str(v) for k, v in newdict.items()
]
gn.export("\n".join(newdictstr),
'Marker score {} vs rest.csv'.format(group),
kind='raw',
meta=None,
raw=True)
# cluster_assignment = dict(zip(adata.obs_names, adata.obs['louvain'].values.tolist()))
# gn.export_statically(cluster_assignment, 'cluster_assignment')
toc = time.perf_counter()
time_passed = round(toc - tic, 2)
timing = "* Finished marker gene identification step in {} seconds*".format(
time_passed)
gn.add_result(timing, "markdown")
gn.commit()
except Exception as e:
plt.figure()
plt.text(0.01, 0.5,
'Incompatible group vector due to insufficent cells')
plt.text(0.01, 0.3,
'Please retry the step with a different group vector')
plt.axis('off')
gn.add_current_figure_to_results('One-vs-rest marker genes')
gn.add_result('Error = {}'.format(e), "markdown")
gn.commit()
