def aggregate(self,
ds: loompy.LoomConnection,
out_file: str,
agg_spec: Dict[str, str] = None) -> None:
if agg_spec is None:
agg_spec = {
"Age": "tally",
"Clusters": "first",
"Class": "mode",
"_Total": "mean",
"Sex": "tally",
"Tissue": "tally",
"SampleID": "tally",
"TissuePool": "first",
"Outliers": "mean"
}
cells = ds.col_attrs["Clusters"] >= 0
labels = ds.col_attrs["Clusters"][cells]
n_labels = len(set(labels))
logging.info("Aggregating clusters by mean")
cg.aggregate_loom(ds, out_file, None, "Clusters", "mean", agg_spec)
with loompy.connect(out_file) as dsout:
logging.info("Trinarizing")
if type(self.f) is list or type(self.f) is tuple:
for ix, f in enumerate(self.f):
trinaries = cg.Trinarizer(f=f).fit(ds)
if ix == 0:
dsout.layers["trinaries"] = trinaries
else:
dsout.layers[f"trinaries_{f}"] = trinaries
else:
trinaries = cg.Trinarizer(f=self.f).fit(ds)
dsout.layers["trinaries"] = trinaries
logging.info("Computing cluster gene enrichment scores")
(markers, enrichment,
qvals) = cg.MarkerSelection(self.n_markers).fit(ds)
dsout.layers["enrichment"] = enrichment
dsout.layers["enrichment_q"] = qvals
dsout.ca.NCells = np.bincount(labels, minlength=n_labels)
# Renumber the clusters
logging.info(
"Renumbering clusters by similarity, and permuting columns")
if "_Selected" in ds.ra:
genes = (ds.ra._Selected == 1)
else:
logging.info("Normalization")
normalizer = cg.Normalizer(False)
normalizer.fit(ds)
logging.info("Selecting up to 1000 genes")
genes = cg.FeatureSelection(1000).fit(ds,
mu=normalizer.mu,
sd=normalizer.sd)
data = np.log(dsout[:, :] + 1)[genes, :].T
D = pdist(data, 'euclidean')
Z = hc.linkage(D, 'ward')
optimal_Z = optimal_leaf_ordering(Z, D)
ordering = hc.leaves_list(optimal_Z)
# Permute the aggregated file, and renumber
dsout.permute(ordering, axis=1)
dsout.ca.Clusters = np.arange(n_labels)
# Renumber the original file, and permute
d = dict(zip(ordering, np.arange(n_labels)))
new_clusters = np.array(
[d[x] if x in d else -1 for x in ds.ca.Clusters])
ds.ca.Clusters = new_clusters
ds.permute(np.argsort(ds.col_attrs["Clusters"]), axis=1)
# Reorder the genes, markers first, ordered by enrichment in clusters
logging.info("Permuting rows")
mask = np.zeros(ds.shape[0], dtype=bool)
mask[markers] = True
# fetch enrichment from the aggregated file, so we get it already permuted on the column axis
gene_order = np.zeros(ds.shape[0], dtype='int')
gene_order[mask] = np.argmax(dsout.layer["enrichment"][mask, :],
axis=1)
gene_order[~mask] = np.argmax(dsout.layer["enrichment"][~mask, :],
axis=1) + dsout.shape[1]
gene_order = np.argsort(gene_order)
ds.permute(gene_order, axis=0)
dsout.permute(gene_order, axis=0)
data = trinaries[:, ordering][gene_order, :][:self.n_markers *
n_labels, :].T
cluster_scores = []
for ix in range(n_labels):
cluster_scores.append(data[ix, ix * 10:(ix + 1) * 10].sum())
dsout.ca.ClusterScore = np.array(cluster_scores)
def aggregate(self,
ds: loompy.LoomConnection,
*,
out_file: str,
agg_spec: Dict[str, str] = None) -> None:
config = load_config() # Generic config, just to get the paths
if agg_spec is None:
agg_spec = {
"Age": "tally",
"Clusters": "first",
"Class": "mode",
"Total": "mean",
"Sex": "tally",
"Tissue": "tally",
"SampleID": "tally",
"TissuePool": "first",
"Outliers": "mean",
"PCW": "mean"
}
cells = ds.col_attrs["Clusters"] >= 0
labels = ds.col_attrs["Clusters"][cells]
n_labels = len(set(labels))
logging.info("Aggregating clusters")
ds.aggregate(out_file, None, "Clusters", "mean", agg_spec)
with loompy.connect(out_file) as dsout:
logging.info("Trinarizing")
if type(self.f) is list or type(self.f) is tuple:
for ix, f in enumerate(self.f): # type: ignore
trinaries = Trinarizer(f=f).fit(ds)
if ix == 0:
dsout.layers["trinaries"] = trinaries
else:
dsout.layers[f"trinaries_{f}"] = trinaries
else:
trinaries = Trinarizer(f=self.f).fit(ds) # type:ignore
dsout.layers["trinaries"] = trinaries
logging.info("Computing cluster gene enrichment scores")
fe = FeatureSelectionByMultilevelEnrichment(mask=self.mask)
markers = fe.fit(ds)
dsout.layers["enrichment"] = fe.enrichment
dsout.ca.NCells = np.bincount(labels, minlength=n_labels)
# Renumber the clusters
logging.info(
"Renumbering clusters by similarity, and permuting columns")
data = np.log(dsout[:, :] + 1)[markers, :].T
D = pdist(data, 'euclidean')
Z = hc.linkage(D, 'ward', optimal_ordering=True)
ordering = hc.leaves_list(Z)
# Permute the aggregated file, and renumber
dsout.permute(ordering, axis=1)
dsout.ca.Clusters = np.arange(n_labels)
# Redo the Ward's linkage just to get a tree that corresponds with the new ordering
data = np.log(dsout[:, :] + 1)[markers, :].T
D = pdist(data, 'euclidean')
dsout.attrs.linkage = hc.linkage(D, 'ward', optimal_ordering=True)
# Renumber the original file, and permute
d = dict(zip(ordering, np.arange(n_labels)))
new_clusters = np.array(
[d[x] if x in d else -1 for x in ds.ca.Clusters])
ds.ca.Clusters = new_clusters
ds.permute(np.argsort(ds.col_attrs["Clusters"]), axis=1)
# Reorder the genes, markers first, ordered by enrichment in clusters
logging.info("Permuting rows")
mask = np.zeros(ds.shape[0], dtype=bool)
mask[markers] = True
# fetch enrichment from the aggregated file, so we get it already permuted on the column axis
gene_order = np.zeros(ds.shape[0], dtype='int')
gene_order[mask] = np.argmax(dsout.layer["enrichment"][mask, :],
axis=1)
gene_order[~mask] = np.argmax(dsout.layer["enrichment"][~mask, :],
axis=1) + dsout.shape[1]
gene_order = np.argsort(gene_order)
ds.permute(gene_order, axis=0)
dsout.permute(gene_order, axis=0)
logging.info("Computing auto-annotation")
AutoAnnotator(root=config.paths.autoannotation,
ds=dsout).annotate(dsout)
logging.info("Computing auto-auto-annotation")
AutoAutoAnnotator(n_genes=6).annotate(dsout)
if "skeletonize" in config.steps:
logging.info("Graph skeletonization")
GraphSkeletonizer(min_pct=1).abstract(ds, dsout)
