def run(self) -> None:
logging = cg.logging(self, True)
with self.output().temporary_path() as out_dir:
logging.info("Exporting cluster data")
if not os.path.exists(out_dir):
os.mkdir(out_dir)
with loompy.connect(self.input()[0].fn) as dsagg:
dsagg.export(
os.path.join(out_dir, f"L6_R{self.rank}_expression.tab"))
dsagg.export(os.path.join(out_dir,
f"L6_R{self.rank}_enrichment.tab"),
layer="enrichment")
dsagg.export(os.path.join(out_dir,
f"L6_R{self.rank}_enrichment_q.tab"),
layer="enrichment_q")
dsagg.export(os.path.join(out_dir,
f"L6_R{self.rank}_trinaries.tab"),
layer="trinaries")
logging.info("Plotting manifold graph with auto-annotation")
with loompy.connect(self.input()[1].fn) as ds:
cg.plot_graph(
ds,
os.path.join(out_dir,
f"L6_R{self.rank}_manifold.aa.png"),
list(dsagg.ca.AutoAnnotation))
logging.info(
"Plotting manifold graph with auto-auto-annotation")
cg.plot_graph(
ds,
os.path.join(out_dir,
f"L6_R{self.rank}_manifold.aaa.png"),
list(dsagg.ca.MarkerGenes))
logging.info("Plotting manifold graph with taxon names")
cg.plot_graph(
ds,
os.path.join(out_dir,
f"L6_R{self.rank}_manifold.names.png"),
list(dsagg.ca[f"TaxonomyRank{self.rank}"]))
logging.info("Plotting marker heatmap")
cg.plot_markerheatmap(ds,
dsagg,
n_markers_per_cluster=self.n_markers,
out_file=os.path.join(
out_dir,
f"L6_R{self.rank}_heatmap.pdf"))
def run(self) -> None:
logging = cg.logging(self, True)
logging.info("Exporting cluster data")
with self.output().temporary_path() as out_dir:
if not os.path.exists(out_dir):
os.mkdir(out_dir)
with loompy.connect(self.input()[0].fn) as dsagg:
logging.info("Computing auto-annotation")
aa = cg.AutoAnnotator(root=am.paths().autoannotation)
aa.annotate_loom(dsagg)
aa.save_in_loom(dsagg)
dsagg.export(os.path.join(out_dir, "L1_" + self.tissue + "_expression.tab"))
dsagg.export(os.path.join(out_dir, "L1_" + self.tissue + "_enrichment.tab"), layer="enrichment")
dsagg.export(os.path.join(out_dir, "L1_" + self.tissue + "_enrichment_q.tab"), layer="enrichment_q")
dsagg.export(os.path.join(out_dir, "L1_" + self.tissue + "_trinaries.tab"), layer="trinaries")
ds = loompy.connect(self.input()[1].fn)
logging.info("Plotting MKNN graph")
cg.plot_knn(ds, os.path.join(out_dir, "L1_" + self.tissue + "_manifold.mknn.png"))
# logging.info("Plotting Louvain resolution")
# cg.plot_louvain(ds, os.path.join(out_dir, "L1_" + self.tissue + "_manifold.louvain.png"))
try:
logging.info("Plotting manifold graph with classes")
cg.plot_classes(ds, os.path.join(out_dir, "L1_" + self.tissue + "_manifold.classes.png"))
except Exception:
pass
logging.info("Plotting manifold graph with auto-annotation")
tags = list(dsagg.col_attrs["AutoAnnotation"])
cg.plot_graph(ds, os.path.join(out_dir, "L1_" + self.tissue + "_manifold.aa.png"), tags)
logging.info("Plotting manifold graph with auto-auto-annotation")
tags = list(dsagg.col_attrs["MarkerGenes"])
cg.plot_graph(ds, os.path.join(out_dir, "L1_" + self.tissue + "_manifold.aaa.png"), tags)
logging.info("Plotting marker heatmap")
cg.plot_markerheatmap(ds, dsagg, n_markers_per_cluster=self.n_markers, out_file=os.path.join(out_dir, "L1_" + self.tissue + "_heatmap.pdf"))
def run(self) -> None:
logging = cg.logging(self)
with self.output().temporary_path() as out_dir:
logging.info("Exporting cluster data")
if not os.path.exists(out_dir):
os.mkdir(out_dir)
dsagg = loompy.connect(self.input()[0].fn)
logging.info("Computing auto-annotation")
aa = cg.AutoAnnotator(root=am.paths().autoannotation)
aa.annotate_loom(dsagg)
aa.save_in_loom(dsagg)
dsagg.export(
os.path.join(out_dir, "L3_" + self.target + "_expression.tab"))
dsagg.export(os.path.join(out_dir,
"L3_" + self.target + "_enrichment.tab"),
layer="enrichment")
dsagg.export(os.path.join(
out_dir, "L3_" + self.target + "_enrichment_q.tab"),
layer="enrichment_q")
dsagg.export(os.path.join(out_dir,
"L3_" + self.target + "_trinaries.tab"),
layer="trinaries")
logging.info("Plotting manifold graph with auto-annotation")
tags = list(dsagg.col_attrs["AutoAnnotation"][np.argsort(
dsagg.col_attrs["Clusters"])])
ds = loompy.connect(self.input()[1].fn)
cg.plot_graph(
ds,
os.path.join(out_dir,
"L3_" + self.target + "_manifold.aa.png"), tags)
logging.info("Plotting manifold graph with auto-auto-annotation")
tags = list(dsagg.col_attrs["MarkerGenes"][np.argsort(
dsagg.col_attrs["Clusters"])])
cg.plot_graph(
ds,
os.path.join(out_dir,
"L3_" + self.target + "_manifold.aaa.png"), tags)
logging.info("Plotting marker heatmap")
cg.plot_markerheatmap(ds,
dsagg,
n_markers_per_cluster=self.n_markers,
out_file=os.path.join(
out_dir,
"L3_" + self.target + "_heatmap.pdf"))
logging.info("Computing discordance distances")
pep = 0.05
n_labels = dsagg.shape[1]
def discordance_distance(a: np.ndarray, b: np.ndarray) -> float:
"""
Number of genes that are discordant with given PEP, divided by number of clusters
"""
return np.sum((1 - a) * b + a * (1 - b) > 1 - pep) / n_labels
data = dsagg.layer["trinaries"][:n_labels * 10, :].T
D = squareform(pdist(data, discordance_distance))
with open(
os.path.join(out_dir,
"L3_" + self.target + "_distances.txt"),
"w") as f:
f.write(str(np.diag(D, k=1)))
def run(self) -> None:
logging = cg.logging(self, True)
with self.output().temporary_path() as out_dir:
logging.info("Exporting cluster data")
if not os.path.exists(out_dir):
os.mkdir(out_dir)
with loompy.connect(self.input()[0].fn) as dsagg:
logging.info("Exporting tab files")
dsagg.export(
os.path.join(
out_dir, "L2_" + self.major_class + "_" + self.tissue +
"_expression.tab"))
dsagg.export(os.path.join(
out_dir, "L2_" + self.major_class + "_" + self.tissue +
"_enrichment.tab"),
layer="enrichment")
dsagg.export(os.path.join(
out_dir, "L2_" + self.major_class + "_" + self.tissue +
"_enrichment_q.tab"),
layer="enrichment_q")
dsagg.export(os.path.join(
out_dir, "L2_" + self.major_class + "_" + self.tissue +
"_trinaries.tab"),
layer="trinaries")
logging.info("Plotting manifold graph with auto-annotation")
tags = list(dsagg.col_attrs["AutoAnnotation"])
with loompy.connect(self.input()[1].fn) as ds:
cg.plot_graph(
ds,
os.path.join(
out_dir, "L2_" + self.major_class + "_" +
self.tissue + "_manifold.aa.png"), tags)
logging.info(
"Plotting manifold graph with auto-auto-annotation")
tags = list(dsagg.col_attrs["MarkerGenes"][np.argsort(
dsagg.col_attrs["Clusters"])])
cg.plot_graph(
ds,
os.path.join(
out_dir, "L2_" + self.major_class + "_" +
self.tissue + "_manifold.aaa.png"), tags)
logging.info("Plotting manifold graph with classes")
cg.plot_classes(
ds,
os.path.join(
out_dir, "L2_" + self.major_class + "_" +
self.tissue + "_manifold.classes.png"))
logging.info("Plotting marker heatmap")
cg.plot_markerheatmap(ds,
dsagg,
n_markers_per_cluster=10,
out_file=os.path.join(
out_dir,
"L2_" + self.major_class + "_" +
self.tissue + "_heatmap.pdf"))
logging.info("Plotting latent factors")
cg.plot_factors(ds,
base_name=os.path.join(
out_dir, "L2_" + self.major_class +
"_" + self.tissue + "_factors"))
def run(self) -> None:
logging = cg.logging(self, True)
with self.output().temporary_path() as out_dir:
logging.info("Exporting cluster data")
if not os.path.exists(out_dir):
os.mkdir(out_dir)
with loompy.connect(self.input()[0].fn) as dsagg:
dsagg.export(
os.path.join(
out_dir,
f"L6_R{self.rank}_({self.taxon})_expression.tab"))
dsagg.export(os.path.join(
out_dir, f"L6_R{self.rank}_({self.taxon})_enrichment.tab"),
layer="enrichment")
dsagg.export(os.path.join(
out_dir,
f"L6_R{self.rank}_({self.taxon})_enrichment_q.tab"),
layer="enrichment_q")
dsagg.export(os.path.join(
out_dir, f"L6_R{self.rank}_({self.taxon})_trinaries.tab"),
layer="trinaries")
logging.info("Plotting manifold graph with auto-annotation")
with loompy.connect(self.input()[1].fn) as ds:
cg.plot_graph(
ds,
os.path.join(
out_dir,
f"L6_R{self.rank}_({self.taxon})_manifold.aa.png"),
list(dsagg.ca.AutoAnnotation))
logging.info(
"Plotting manifold graph with auto-auto-annotation")
cg.plot_graph(
ds,
os.path.join(
out_dir,
f"L6_R{self.rank}_({self.taxon})_manifold.aaa.png"
), list(dsagg.ca.MarkerGenes))
logging.info("Plotting manifold graph with cluster names")
cg.plot_graph(
ds,
os.path.join(
out_dir,
f"L6_R{self.rank}_({self.taxon})_manifold.names.png"
), list(dsagg.ca.ClusterName))
logging.info("Plotting marker heatmap")
cg.plot_markerheatmap(
ds,
dsagg,
n_markers_per_cluster=self.n_markers,
out_file=os.path.join(
out_dir,
f"L6_R{self.rank}_({self.taxon})_heatmap.pdf"))
size = 200000 / ds.shape[1]
fig = plt.figure(figsize=(3, 3))
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
ix = 0
if self.rank == 3:
colors = cg.colorize(np.unique(ds.ca.ClusterName))
for cluster in np.unique(ds.ca.ClusterName):
cells = ds.ca.ClusterName == cluster
plt.scatter(x=ds.ca._X[cells],
y=ds.ca._Y[cells],
s=size,
c=colors[ix, :],
marker='.',
label=cluster,
alpha=0.5,
lw=0)
ix += 1
else:
colors = cg.colorize(np.unique(ds.ca.TaxonomyRank4))
for taxon4 in np.unique(ds.ca.TaxonomyRank4):
cells = ds.ca.TaxonomyRank4 == taxon4
plt.scatter(x=ds.ca._X[cells],
y=ds.ca._Y[cells],
s=size,
c=colors[ix, :],
marker='.',
label=taxon4,
alpha=0.5,
lw=0)
ix += 1
lgnd = ax.legend(fontsize=10,
labelspacing=0.2,
loc="upper left",
bbox_to_anchor=(1, 1),
frameon=False)
for handle in lgnd.legendHandles:
handle.set_sizes([250])
handle.set_alpha(1)
plt.savefig(os.path.join(
out_dir,
f"L6_R{self.rank}_({self.taxon})_manifold.pretty.png"),
dpi=600,
transparent=True,
bbox_extra_artists=(lgnd, ),
bbox_inches='tight')
plt.close()