def _fit(self, ds: loompy.LoomConnection,
labels: np.ndarray) -> np.ndarray:
logging.info("Computing enrichment statistic")
n_labels = len(np.unique(labels))
n_genes, n_cells = ds.shape
# Number of cells per cluster
sizes = np.bincount(labels, minlength=n_labels)
# Number of nonzero values per cluster
nnz = ds.aggregate(None, None, labels, np.count_nonzero, None)
# Mean value per cluster
means = ds.aggregate(None, None, labels, "mean", None)
# Non-zeros and means over all cells
(nnz_overall, means_overall) = ds.map([np.count_nonzero, np.mean],
axis=0)
# Scale by number of cells
f_nnz = nnz / sizes
f_nnz_overall = nnz_overall / n_cells
# Means and fraction non-zero values in other clusters (per cluster)
means_other = ((means_overall * n_cells)[None].T -
(means * sizes)) / (n_cells - sizes)
f_nnz_other = ((f_nnz_overall * n_cells)[None].T -
(f_nnz * sizes)) / (n_cells - sizes)
# enrichment = (f_nnz + 0.1) / (f_nnz_overall[None].T + 0.1) * (means + 0.01) / (means_overall[None].T + 0.01)
enrichment = (f_nnz + 0.1) / (f_nnz_other + 0.1) * (means + 0.01) / (
means_other + 0.01)
# Select best markers
if self.valid_genes is None:
logging.info("Identifying valid genes")
nnz = ds.map([np.count_nonzero], axis=0)[0]
self.valid_genes = np.logical_and(nnz > 10,
nnz < ds.shape[1] * 0.6)
if self.mask is None:
excluded = set(np.where(~self.valid_genes)[0])
else:
excluded = set(np.where(((~self.valid_genes) | self.mask))[0])
included = np.zeros(n_genes, dtype=bool)
for ix in range(n_labels):
enriched = np.argsort(enrichment[:, ix])[::-1]
n = 0
count = 0
while count < self.n_markers_per_cluster:
if enriched[n] in excluded:
n += 1
continue
included[enriched[n]] = True
excluded.add(enriched[n])
n += 1
count += 1
return (included, enrichment, means)
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)
