def fit(self, ds: loompy.LoomConnection, normalizer: cg.Normalizer, cells: np.ndarray = None) -> None: if cells is None: cells = np.fromiter(range(ds.shape[1]), dtype='int') n_cells = cells.shape[0] n_genes = self.genes.shape[0] # Support out-of-order datasets if "Accession" in ds.row_attrs: self.accessions = ds.row_attrs["Accession"] self.pca = IncrementalPCA(n_components=self.n_components) if self.layer is not None: # NOTE TO AVOID a BUG with layer of pickled objects try: for (ix, selection, view) in ds.scan(items=cells, axis=1, layers=[self.layer]): vals = normalizer.transform(view.layers[self.layer][:, :], selection) if self.nng is not None: vals[np.where(self.nng)[0][:, None], np.where(ds.TaxonomyRank1 == "Neurons")[0]] = 0 self.pca.partial_fit(vals[self.genes, :].transpose()) # PCA on the selected genes except AttributeError: self.layer = None if self.layer is None: for (ix, selection, view) in ds.scan(items=cells, axis=1): vals = normalizer.transform(view[:, :], selection) if self.nng is not None: vals[np.where(self.nng)[0][:, None], np.where(ds.TaxonomyRank1 == "Neurons")[0]] = 0 self.pca.partial_fit(vals[self.genes, :].transpose()) # PCA on the selected genes
def fit_transform(self, ds: loompy.LoomConnection) -> None:
# Poisson pooling
self.fit(ds)
knn = self.knn.astype("bool")
logging.debug(f"Poisson pooling")
ds["pooled"] = 'int32'
if self.compute_velocity and "spliced" in ds.layers:
ds["spliced_pooled"] = 'int32'
ds["unspliced_pooled"] = 'int32'
for (_, indexes, view) in ds.scan(axis=0,
layers=["spliced", "unspliced"],
what=["layers"]):
ds["spliced_pooled"][
indexes.min():indexes.max() +
1, :] = view.layers["spliced"][:, :] @ knn.T
ds["unspliced_pooled"][
indexes.min():indexes.max() +
1, :] = view.layers["unspliced"][:, :] @ knn.T
ds["pooled"][indexes.min():indexes.max() +
1, :] = ds["spliced_pooled"][indexes.min(
):indexes.max() + 1, :] + ds["unspliced_pooled"][
indexes.min():indexes.max() + 1, :]
else:
for (_, indexes, view) in ds.scan(axis=0,
layers=[""],
what=["layers"]):
ds["pooled"][indexes.min():indexes.max() +
1, :] = view[:, :] @ knn.T
def fit(self, ds: loompy.LoomConnection, mu: np.ndarray = None, sd: np.ndarray = None, totals: np.ndarray = None) -> None: self.sd = sd self.mu = mu self.totals = totals if mu is None or sd is None: (self.sd, self.mu) = ds.map([np.std, np.mean], axis=0) if totals is None: self.totals = ds.map([np.sum], chunksize=100, axis=1)[0]
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 mito_genes_ratio(ds: loompy.LoomConnection) -> None:
mito_genes = np.where(ds.ra.Chromosome == "MT")[0]
exp_mito = ds[mito_genes, :]
sum_mito = exp_mito.sum(axis=0)
sum_all = ds.ca.TotalUMI
mito_ratio = np.divide(sum_mito, sum_all)
ds.ca["MT_ratio"] = mito_ratio
def fit(self, ds: loompy.LoomConnection) -> None:
self.sd = np.zeros(ds.shape[0])
self.mu = np.zeros(ds.shape[0])
self.totals = np.zeros(ds.shape[1])
for _, selection, view in ds.scan(axis=0):
vals = view[self.layer][:, :].astype("float")
self.totals += np.sum(vals, axis=0)
self.level = np.median(self.totals)
for _, selection, view in ds.scan(axis=0):
vals = view[self.layer][:, :].astype("float")
# Rescale to the median total UMI count, plus 1 (to avoid log of zero), then log transform
vals = np.log2(div0(vals, self.totals) * self.level + 1)
self.mu[selection] = np.mean(vals, axis=1)
self.sd[selection] = np.std(vals, axis=1)
def expression_patterns(ds: loompy.LoomConnection, labels: np.ndarray, pep: float, f: float, cells: np.ndarray = None) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """ Derive enrichment and trinary scores for all genes Args: ds (LoomConnection): Dataset labels (numpy array): Cluster labels (one per cell) pep (float): Desired posterior error probability f (float): Fraction required for a gene to be considered 'expressed' cells (nump array): Indices of cells to include Returns: score1 (numpy 2d array): Array of (n_genes, n_labels) score2 (numpy 2d array): Array of (n_genes, n_labels) trinary (numpy 2d array): Array of (n_genes, n_labels) Remarks: If the cells argument is provided, the labels should include only those cells. That is, labels.shape[0] == cells.shape[0]. Amit says, regarding marker genes. i usually rank the genes by some kind of enrichment score. score1 = mean of gene within the cluster / mean of gene in all cells score2 = fraction of positive cells within cluster enrichment score = score1 * score2^power (where power == 0.5 or 1) i usually use 1 for 10x data """ n_labels = np.max(labels) + 1 scores1 = np.empty((ds.shape[0], n_labels)) scores2 = np.empty((ds.shape[0], n_labels)) trinary_pat = np.empty((ds.shape[0], n_labels)) trinary_prob = np.empty((ds.shape[0], n_labels)) j = 0 for (ix, selection, vals) in ds.batch_scan(cells=cells, genes=None, axis=0): # vals = normalizer.normalize(vals, selection) for j, row in enumerate(selection): data = vals[j, :] mu0 = np.mean(data) f0 = np.count_nonzero(data) score1 = np.zeros(n_labels) score2 = np.zeros(n_labels) for lbl in range(n_labels): if np.sum(labels == lbl) == 0: continue sel = data[np.where(labels == lbl)[0]] if mu0 == 0 or f0 == 0: score1[lbl] = 0 score2[lbl] = 0 else: score1[lbl] = np.mean(sel) / mu0 score2[lbl] = np.count_nonzero(sel) # f0 scores1[row, :] = score1 scores2[row, :] = score2 trinary_prob[row, :], trinary_pat[row, :] = betabinomial_trinarize_array(data, labels, pep, f) return (scores1, scores2, trinary_prob, trinary_pat)
def transform(self, ds: loompy.LoomConnection, normalizer: cg.Normalizer, cells: np.ndarray = None) -> np.ndarray: if cells is None: cells = np.fromiter(range(ds.shape[1]), dtype='int') n_cells = cells.shape[0] # Support out--of-order datasets if self.accessions is not None: # This is magic sauce for making the order of one list be like another ordering = np.where(ds.row_attrs["Accession"][None, :] == self.accessions[:, None])[1] transformed = np.zeros((cells.shape[0], self.pca.n_components_)) j = 0 if self.layer is not None: # NOTE TO AVOID a BUG with layer of pickled objects try: for (ix, selection, view) in ds.scan(items=cells, axis=1, layers=[self.layer]): vals = normalizer.transform(view.layers[self.layer][:, :], selection) if self.nng is not None: vals[np.where(self.nng)[0][:, None], np.where(ds.TaxonomyRank1 == "Neurons")[0]] = 0 n_cells_in_batch = selection.shape[0] transformed[j:j + n_cells_in_batch, :] = self.pca.transform(vals[self.genes, :].transpose()) j += n_cells_in_batch except AttributeError: self.layer = None if self.layer is None: for (ix, selection, view) in ds.scan(items=cells, axis=1): vals = normalizer.transform(view[:, :], selection) if self.nng is not None: vals[np.where(self.nng)[0][:, None], np.where(ds.TaxonomyRank1 == "Neurons")[0]] = 0 n_cells_in_batch = selection.shape[0] transformed[j:j + n_cells_in_batch, :] = self.pca.transform(vals[self.genes, :].transpose()) j += n_cells_in_batch # Must select significant components only once, and reuse for future transformations if self.sigs is None: pvalue_KS = np.zeros(transformed.shape[1]) # pvalue of each component for i in range(1, transformed.shape[1]): (_, pvalue_KS[i]) = ks_2samp(transformed[:, i - 1], transformed[:, i]) self.sigs = np.where(pvalue_KS < 0.1)[0] if len(self.sigs) == 0: self.sigs = (0, 1) transformed = transformed[:, self.sigs] return transformed
def unspliced_ratio(ds: loompy.LoomConnection,
graphs: bool = True,
sample_name: object = "tmp") -> None:
u = ds.layers["unspliced"][:]
sum_all = ds.ca.TotalUMI
sum_u = u.sum(axis=0)
unspliced_ratio = np.divide(sum_u, sum_all)
ds.ca["unspliced_ratio"] = unspliced_ratio
def fit(self, ds: loompy.LoomConnection, plot: str = None) -> np.ndarray:
"""
Fit a classifier and use it to determine cluster predictive power
Args:
ds Dataset
plot Filename for optional plot
Returns:
Matrix of classification probabilities, shape (n_cells, n_labels)
"""
logging.info("Feature selection")
nnz = ds.map([np.count_nonzero], axis=0)[0]
valid_genes = np.logical_and(nnz > 5, nnz < ds.shape[1] * 0.5).astype("int")
ds.ra._Valid = valid_genes
logging.info("Normalization")
normalizer = cg.Normalizer(False)
normalizer.fit(ds)
logging.info("Feature selection")
(_, enrichment, _) = cg.MarkerSelection(findq=False, labels_attr="Clusters").fit(ds)
genes = np.zeros_like(ds.ra.Gene, dtype=bool)
for ix in range(enrichment.shape[1]):
genes[np.argsort(-enrichment[:, ix])[:25]] = True
logging.info("PCA projection")
pca = cg.PCAProjection(genes, max_n_components=50)
transformed = pca.fit_transform(ds, normalizer)
le = LabelEncoder().fit(ds.ca.ClusterName)
self.le = le
labels = le.transform(ds.ca.ClusterName)
train_X, test_X, train_Y, test_Y = train_test_split(transformed, labels, test_size=0.2)
classifier = RandomForestClassifier(max_depth=30)
classifier.fit(train_X, train_Y)
self.report = classification_report(test_Y, classifier.predict(test_X), target_names=le.classes_)
self.proba = classifier.predict_proba(transformed)
if plot:
agg = npg.aggregate(labels, self.proba, axis=0, func="mean")
plt.imshow(agg, cmap="viridis")
plt.xticks(np.arange(le.classes_.shape[0]), le.classes_, rotation="vertical", fontsize=7)
plt.yticks(np.arange(le.classes_.shape[0]), le.classes_, rotation="horizontal", fontsize=7)
plt.xlabel("Predicted cell type")
plt.ylabel("Observed cell type")
plt.title("Predictive power of cluster identities")
cbar = plt.colorbar()
cbar.set_label('Average classification probability', rotation=90)
plt.savefig(plot, bbox_inches="tight")
return self.proba
def fit_loom(self, ds: loompy.LoomConnection, *, tolayer: str = "enrichment", knn: Union[str, sparse.csr_matrix] = "KNN") -> None: if tolayer not in ds.layers: ds[tolayer] = "float32" if type(knn) is str: knn_matrix = ds.col_graphs[knn].tocsr() else: knn_matrix = knn k = knn_matrix.count_nonzero() / knn_matrix.shape[0] with tqdm(total=ds.shape[0], desc="Neighborhood enrichment") as pbar: for ix, selection, view in ds.scan(axis=0, what=["layers"]): for j in range(view.shape[0]): ds[tolayer][j + ix, :] = self.fit(view[j, :], knn_matrix, k) pbar.update(view.shape[0])
def transform(self,
ds: loompy.LoomConnection,
normalizer: Normalizer,
cells: np.ndarray = None) -> np.ndarray:
if cells is None:
cells = np.arange(ds.shape[1])
transformed = np.zeros((cells.shape[0], self.pca.n_components_))
j = 0
# Support out-of-order datasets
key = None
if "Accession" in ds.row_attrs:
key = "Accession"
layer = self.layer if self.layer is not None else ""
for (_, selection, view) in ds.scan(items=cells,
axis=1,
layers=[layer],
key=key):
vals = normalizer.transform(view.layers[layer][:, :], selection)
n_cells_in_batch = selection.shape[0]
transformed[j:j + n_cells_in_batch, :] = self.pca.transform(
vals[self.genes, :].transpose())
j += n_cells_in_batch
if self.test_significance:
# Must select significant components only once, and reuse for future transformations
if self.sigs is None:
pvalue_KS = np.zeros(
transformed.shape[1]) # pvalue of each component
for i in range(1, transformed.shape[1]):
(_, pvalue_KS[i]) = ks_2samp(transformed[:, i - 1],
transformed[:, i])
self.sigs = np.where(pvalue_KS < 0.1)[0]
if len(self.sigs) == 0:
self.sigs = (0, 1)
transformed = transformed[:, self.sigs]
if self.batch_keys is not None and len(self.batch_keys) > 0:
keys_df = pd.DataFrame.from_dict(
{k: ds.ca[k]
for k in self.batch_keys})
transformed = harmonize(transformed,
keys_df,
batch_key=self.batch_keys)
return transformed
def fit(self, ds: loompy.LoomConnection) -> np.ndarray:
cells = np.where(ds.col_attrs["Clusters"] >= 0)[0]
labels = ds.col_attrs["Clusters"][cells]
n_labels = np.max(labels) + 1
logging.info("n_labels %d", n_labels)
self.trinary_prob = np.empty((ds.shape[0], n_labels))
self.genes = ds.ra.Gene
for (ix, selection, view) in ds.scan(axis=0, what=["layers"]):
vals = view[:, cells]
for j, row in enumerate(selection):
data = np.round(vals[j, :])
self.trinary_prob[row, :] = self._betabinomial_trinarize_array(
data, labels, self.f, n_labels)
return self.trinary_prob
def fit(self,
ds: loompy.LoomConnection,
cells: np.ndarray = None,
mu: np.ndarray = None,
sd: np.ndarray = None,
mask: np.ndarray = None) -> np.ndarray:
"""
Fits a noise model (CV vs mean)
Args:
ds (LoomConnection): Dataset
n_genes (int): number of genes to include
cells (ndarray): cells to include when computing mean and CV (or None)
mu, std: Precomputed mean and standard deviations (optional)
Returns:
ndarray of selected genes (list of ints)
"""
if mu is None or sd is None:
(mu, sd) = ds.map((np.mean, np.std), axis=0, selection=cells)
if "_Valid" in ds.ra:
valid = ds.ra._Valid == 1
else:
valid = np.ones(ds.shape[0], dtype='bool')
if mask is not None:
valid = np.logical_and(valid, np.logical_not(mask))
valid = np.logical_and(valid, ds.row_attrs["Gene"] != "Xist")
valid = np.logical_and(valid, ds.row_attrs["Gene"] != "Tsix")
valid = valid.astype('int')
ok = np.logical_and(mu > 0, sd > 0)
cv = sd[ok] / mu[ok]
log2_m = np.log2(mu[ok])
log2_cv = np.log2(cv)
svr_gamma = 1000. / len(mu[ok])
clf = SVR(gamma=svr_gamma)
clf.fit(log2_m[:, np.newaxis], log2_cv)
fitted_fun = clf.predict
# Score is the relative position with respect of the fitted curve
score = log2_cv - fitted_fun(log2_m[:, np.newaxis])
score = score * valid[ok]
self.genes = np.where(ok)[0][np.argsort(score)][-self.n_genes:]
return self.genes
def plot_knn(ds: loompy.LoomConnection, out_file: str) -> None:
n_cells = ds.shape[1]
valid = ds.col_attrs["_Valid"].astype('bool')
(a, b, w) = ds.get_edges("MKNN", axis=1)
mknn = sparse.coo_matrix(
(w, (a, b)), shape=(n_cells, n_cells)).tocsr()[valid, :][:, valid]
xy = np.vstack(
(ds.col_attrs["_X"], ds.col_attrs["_Y"])).transpose()[valid, :]
fig = plt.figure(figsize=(10, 10))
g = nx.from_scipy_sparse_matrix(mknn)
ax = fig.add_subplot(111)
nx.draw_networkx_edges(g, pos=xy, alpha=0.25, width=0.2, edge_color='gray')
ax.axis('off')
plt.tight_layout()
fig.savefig(out_file, format="png", dpi=300)
plt.close()
def fit(self, ds: loompy.LoomConnection) -> None:
# Validating genes
logging.info("Marking invalid genes")
nnz = ds.map([np.count_nonzero], axis=0)[0]
valid_genes = np.logical_and(nnz > 5,
nnz < ds.shape[1] * 0.5).astype("int")
ds.ra._Valid = valid_genes
with open(os.path.join(self.classified_dir, "genes.txt"), "w") as f:
for ix in range(valid_genes.shape[0]):
f.write(ds.Accession[ix])
f.write("\t")
f.write(str(valid_genes[ix]))
f.write("\n")
logging.info("Normalization")
normalizer = cg.Normalizer(True)
normalizer.fit(ds)
self.mu = normalizer.mu
self.sd = normalizer.sd
logging.info("Feature selection")
genes = cg.FeatureSelection(2000).fit(ds)
logging.info("PCA projection")
self.pca = cg.PCAProjection(genes, max_n_components=50)
transformed = self.pca.fit_transform(ds, normalizer)
self.classes = ds.col_attrs["SubclassAssigned"]
self.le = LabelEncoder().fit(self.classes)
self.labels = self.le.transform(self.classes)
train_X, test_X, train_Y, test_Y = train_test_split(transformed,
self.labels,
test_size=0.2,
random_state=0)
self.classifier = SVC(probability=True)
self.classifier.fit(train_X, train_Y)
with open(os.path.join(self.classified_dir, "performance.txt"),
"w") as f:
f.write(
classification_report(test_Y,
self.classifier.predict(test_X),
target_names=self.le.classes_))
def connect(self, project: str, filename: str, mode: str="r+", timeout: float=10) -> LoomConnection: """ Try to connect to a local loom file. If returns None already connected. Uses a semaphore to ensure there is never more than one connection open to a loom file (as long as this is the only object that connects to the loom file) Remember to call `release(project, filename)` after closing the connection! Args: - project (string): Name of the project (e.g. "Midbrain") - filename (string): Filename of the loom file (e.g. "Midbrain_20160701.loom") Returns: A loom file connection, or None if file does not exist or was already connected. """ if self.acquire(project, filename, timeout): absolute_path = self.list.absolute_file_path(project, filename) return LoomConnection(absolute_path, mode) return None
def fit(self,
ds: loompy.LoomConnection,
normalizer: Normalizer,
cells: np.ndarray = None) -> None:
if cells is None:
cells = np.fromiter(range(ds.shape[1]), dtype='int')
# Support out-of-order datasets
key = None
if "Accession" in ds.row_attrs:
key = "Accession"
self.pca = IncrementalPCA(n_components=self.n_components)
layer = self.layer if self.layer is not None else ""
for (_, selection, view) in ds.scan(items=cells,
axis=1,
layers=[layer],
key=key):
if len(selection) < self.n_components:
continue
vals = normalizer.transform(view.layers[layer][:, :], selection)
self.pca.partial_fit(
vals[self.genes, :].transpose()) # PCA on the selected genes
def tile(self, project: str, filename: str, truncate: bool = False) -> None: absolute_path = self.list.absolute_file_path(project, filename) ds = None if absolute_path is not "": try: lock = self.connections.dataset_locks.get(absolute_path) if lock is not None and lock.acquire(blocking=True, timeout=10): ds = LoomConnection(absolute_path, 'r') tiles = LoomTiles(ds) tiles.prepare_heatmap(truncate) ds.close() lock.release() except TimeoutError: # May happen when cancelled by the environment (for example, on a server). # If so, and the lock was acquired, release the dataset and lock if ds is not None: ds.close() lock.release() pass
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 plot_graph(ds: loompy.LoomConnection,
out_file: str,
tags: List[str] = None) -> None:
logging.info("Loading graph")
n_cells = ds.shape[1]
cells = np.where(ds.col_attrs["_Valid"] == 1)[0]
has_edges = False
if "MKNN" in ds.list_edges(axis=1):
(a, b, w) = ds.get_edges("MKNN", axis=1)
has_edges = True
pos = np.vstack((ds.col_attrs["_X"], ds.col_attrs["_Y"])).transpose()
labels = ds.col_attrs["Clusters"]
if "Outliers" in ds.col_attrs:
outliers = ds.col_attrs["Outliers"]
else:
outliers = np.zeros(ds.shape[1])
# Compute a good size for the markers, based on local density
logging.info("Computing node size")
min_pts = 50
eps_pct = 60
nn = NearestNeighbors(n_neighbors=min_pts, algorithm="ball_tree", n_jobs=4)
nn.fit(pos)
knn = nn.kneighbors_graph(mode='distance')
k_radius = knn.max(axis=1).toarray()
epsilon = 24 * np.percentile(k_radius, eps_pct)
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
# Draw edges
if has_edges:
logging.info("Drawing edges")
lc = LineCollection(zip(pos[a], pos[b]),
linewidths=0.25,
zorder=0,
color='grey',
alpha=0.1)
ax.add_collection(lc)
# Draw nodes
logging.info("Drawing nodes")
colors20 = np.vstack((plt.cm.Vega20b(np.linspace(0., 1, 20))[::2],
plt.cm.Vega20c(np.linspace(0, 1, 20))[1::2]))
plots = []
names = []
for i in range(max(labels) + 1):
cluster = labels == i
n_cells = cluster.sum()
if np.all(outliers[labels == i] == 1):
edgecolor = colorConverter.to_rgba('red', alpha=.1)
plots.append(
plt.scatter(x=pos[outliers == 1, 0],
y=pos[outliers == 1, 1],
c='grey',
marker='.',
edgecolors=edgecolor,
alpha=0.1,
s=epsilon))
names.append(f"{i}/n={n_cells} (outliers)")
else:
plots.append(
plt.scatter(x=pos[cluster, 0],
y=pos[cluster, 1],
c=cg.colors75[np.mod(i, 75)],
marker='.',
lw=0,
s=epsilon,
alpha=0.75))
txt = str(i)
if "ClusterName" in ds.ca.keys():
txt = ds.ca.ClusterName[ds.ca.Clusters == i][0]
if tags is not None:
names.append(f"{txt}/n={n_cells} " +
tags[i].replace("\n", " "))
else:
names.append(f"{txt}/n={n_cells}")
logging.info("Drawing legend")
plt.legend(plots,
names,
scatterpoints=1,
markerscale=2,
loc='upper left',
bbox_to_anchor=(1, 1),
fancybox=True,
framealpha=0.5,
fontsize=10)
logging.info("Drawing cluster IDs")
for lbl in range(0, max(labels) + 1):
txt = str(lbl)
if "ClusterName" in ds.ca.keys():
txt = ds.ca.ClusterName[ds.ca.Clusters == lbl][0]
if np.all(outliers[labels == lbl] == 1):
continue
if np.sum(labels == lbl) == 0:
continue
(x, y) = np.median(pos[np.where(labels == lbl)[0]], axis=0)
ax.text(x,
y,
txt,
fontsize=12,
bbox=dict(facecolor='white', alpha=0.5, ec='none'))
logging.info("Saving to file")
fig.savefig(out_file, format="png", dpi=144, bbox_inches='tight')
plt.close()
def plot_classification(ds: loompy.LoomConnection, out_file: str) -> None:
n_cells = ds.shape[1]
valid = ds.col_attrs["_Valid"].astype('bool')
(a, b, w) = ds.get_edges("MKNN", axis=1)
mknn = sparse.coo_matrix(
(w, (a, b)), shape=(n_cells, n_cells)).tocsr()[valid, :][:, valid]
pos = np.vstack(
(ds.col_attrs["_X"], ds.col_attrs["_Y"])).transpose()[valid, :]
labels = ds.col_attrs["Clusters"][valid]
fig = plt.figure(figsize=(10, 10))
g = nx.from_scipy_sparse_matrix(mknn)
classes = [
"Neurons", "Astrocyte", "Ependymal", "OEC", "Oligos", "Schwann",
"Cycling", "Vascular", "Immune"
]
colors = [plt.cm.get_cmap('Vega20')((ix + 0.5) / 20) for ix in range(20)]
combined_colors = np.zeros((ds.shape[1], 4)) + np.array((0.5, 0.5, 0.5, 0))
for ix, cls in enumerate(classes):
cmap = LinearSegmentedColormap.from_list('custom cmap',
[(1, 1, 1, 0), colors[ix]])
cells = ds.col_attrs["Class0"] == classes[ix]
if np.sum(cells) > 0:
combined_colors[cells] = [
cmap(x) for x in ds.col_attrs["Class_" + classes[ix]][cells]
]
cmap = LinearSegmentedColormap.from_list('custom cmap',
[(1, 1, 1, 0), colors[ix + 1]])
ery_color = np.array(
[[1, 1, 1, 0],
[0.9, 0.71, 0.76,
0]])[(ds.col_attrs["Class"][valid] == "Erythrocyte").astype('int')]
cells = ds.col_attrs["Class0"] == "Erythrocyte"
if np.sum(cells) > 0:
combined_colors[cells] = np.array([1, 0.71, 0.76, 0])
cmap = LinearSegmentedColormap.from_list('custom cmap',
[(1, 1, 1, 0), colors[ix + 2]])
exc_color = np.array(
[[1, 1, 1, 0],
[0.5, 0.5, 0.5,
0]])[(ds.col_attrs["Class0"][valid] == "Excluded").astype('int')]
cells = ds.col_attrs["Class0"] == "Excluded"
if np.sum(cells) > 0:
combined_colors[cells] = np.array([0.5, 0.5, 0.5, 0])
ax = fig.add_subplot(1, 1, 1)
ax.set_title("Class")
nx.draw_networkx_edges(g, pos=pos, alpha=0.2, width=0.1, edge_color='gray')
nx.draw_networkx_nodes(g,
pos=pos,
node_color=combined_colors[valid],
node_size=10,
alpha=0.6,
linewidths=0)
ax.axis('off')
plt.tight_layout()
fig.savefig(out_file, format="png", dpi=300)
plt.close()
def plot_graph_age(ds: loompy.LoomConnection, out_file: str,
tags: List[str]) -> None:
def parse_age(age: str) -> float:
if age == "":
return 0
unit, amount = age[0], float(age[1:])
if unit == "P":
amount += 19.
return amount
n_cells = ds.shape[1]
valid = ds.col_attrs["_Valid"].astype('bool')
(a, b, w) = ds.get_edges("MKNN", axis=1)
mknn = sparse.coo_matrix(
(w, (a, b)), shape=(n_cells, n_cells)).tocsr()[valid, :][:, valid]
sfdp = np.vstack(
(ds.col_attrs["_X"], ds.col_attrs["_Y"])).transpose()[valid, :]
# The sorting below is to make every circle visible and avoid overlappings in crowded situations
orderx = np.argsort(sfdp[:, 0], kind="mergesort")
ordery = np.argsort(sfdp[:, 1], kind="mergesort")
orderfin = orderx[ordery]
sfdp_original = sfdp.copy(
) # still the draw_networkx_edges wants the sfd with respect of the graph `g`
# \it is shortcut to avoid resorting the graph
sfdp = sfdp[orderfin, :]
labels = ds.col_attrs["Clusters"][valid][orderfin]
age = np.fromiter(map(parse_age, ds.col_attrs["Age"]),
dtype=float)[valid][orderfin]
fig = plt.figure(figsize=(10, 10))
g = nx.from_scipy_sparse_matrix(mknn)
ax = fig.add_subplot(111)
# Draw the KNN graph first, with gray transparent edges
nx.draw_networkx_edges(g,
pos=sfdp_original,
alpha=0.1,
width=0.1,
edge_color='gray')
# Then draw the nodes, colored by label
block_colors = plt.cm.nipy_spectral_r((age - 6) / 14.)
nx.draw_networkx_nodes(g,
pos=sfdp,
node_color=block_colors,
node_size=10,
alpha=0.4,
linewidths=0)
for lbl in range(0, max(labels) + 1):
if np.sum(labels == lbl) == 0:
continue
(x, y) = np.median(sfdp[np.where(labels == lbl)[0]], axis=0)
text = "#" + str(lbl)
if len(tags[lbl]) > 0:
text += "\n" + tags[lbl]
ax.text(x,
y,
text,
fontsize=8,
bbox=dict(facecolor='gray', alpha=0.3, ec='none'))
ax.axis('off')
levels = np.unique(age)
for il, lev in enumerate(levels):
ax.add_patch(
plt.Rectangle((0.90, 0.7 + il * 0.016),
0.014,
0.014,
color=plt.cm.nipy_spectral_r((lev - 6) / 14.),
clip_on=0,
transform=ax.transAxes))
ax.text(0.93,
0.703 + il * 0.016,
("E%.1f" % lev if lev < 18.5 else "P%.1f" % (lev - 19)),
transform=ax.transAxes)
plt.tight_layout()
fig.savefig(out_file, format="png", dpi=300)
plt.close()
def fit(self, ds: loompy.LoomConnection) -> None:
logging.info("Computing pseudoage")
ages = np.array([age_to_num(x) for x in ds.ca.Age])
knn = ds.col_graphs.KNN
k = knn.nnz / knn.shape[0]
ds.ca.PseudoAge = (knn.astype("bool") @ ages) / k
logging.info("Slicing pseudoage")
slice_names: List[str] = []
with TemporaryDirectory() as tempfolder:
slices = np.percentile(ds.ca.PseudoAge, np.arange(0, 101, 5))
logging.info("Collecting cells")
for (ix, _, view) in ds.scan(axis=1):
for i in range(len(slices) - 2):
s1 = slices[i]
s2 = slices[i + 2]
slice_name = f"Age{s1:05.2f}to{s2:05.2f}".replace(
".", "") + ".loom"
if slice_name not in slice_names:
slice_names.append(slice_name)
cells = ((view.ca.PseudoAge >= s1) &
(view.ca.PseudoAge < s2))
if cells.sum() == 0:
continue
fname = os.path.join(tempfolder, slice_name)
if not os.path.exists(fname):
with loompy.new(fname) as dsout:
dsout.add_columns(view.layers[:, cells],
col_attrs=view.ca[cells],
row_attrs=view.ra)
else:
with loompy.connect(fname) as dsout:
dsout.add_columns(view.layers[:, cells],
col_attrs=view.ca[cells],
row_attrs=view.ra)
for slice_name in slice_names:
fname = os.path.join(tempfolder, slice_name)
logging.info("Cytograph on " + slice_name)
with loompy.connect(fname) as ds:
Cytograph(config=load_config()).fit(ds)
# Use dynamic programming to find the deepest tree (forest), as given by total number of cells along each branch
logging.info("Computing pseudolineage")
clusters = "Clusters"
min_pct = 0.1
# List of matrices giving the bipartite graph between each pair of layers, weighted by number of shared cells
overlaps = []
n_nodes = [] # List of number of nodes (clusters) in each layer
n_cells = [
] # List of arrays giving the number of cells in each cluster
n_layers = len(slice_names)
# Compute the bipartite graphs between layers
for t in range(n_layers):
# Link clusters from layer t to clusters from layer t + 1
logging.info(f"{slice_names[t]}.loom")
with loompy.connect(os.path.join(tempfolder,
slice_names[t])) as ds1:
n_nodes.append(ds1.ca[clusters].max() + 1)
n_cells.append(np.zeros(n_nodes[t]))
for c in range(n_nodes[t]):
n_cells[t][c] = (ds1.ca[clusters] == c).sum()
if t >= n_layers - 1:
break
with loompy.connect(
os.path.join(tempfolder,
slice_names[t + 1])) as ds2:
overlap = np.zeros(
(np.unique(ds1.ca[clusters]).shape[0],
np.unique(ds2.ca[clusters]).shape[0]),
dtype="int")
for i in np.unique(ds1.ca[clusters]):
cells1 = ds1.ca.CellID[ds1.ca[clusters] == i]
for j in np.unique(ds2.ca[clusters]):
cells2 = ds2.ca.CellID[ds2.ca[clusters] == j]
overlap[i, j] = np.intersect1d(cells1,
cells2).shape[0]
overlaps.append(overlap)
# List of arrays keeping track of the depth of the deepest tree starting at each node in the layer
# Depth defined as sum of the number of shared cells along the branch
depths = [np.zeros(n, dtype="int") for n in n_nodes]
edges = [
np.zeros(n, dtype="int") for n in n_nodes[1:]
] # List of arrays giving the predecessor of each cluster (or -1 if no predecessor)
for t in range(0, n_layers - 1):
for i in range(n_nodes[t + 1]):
# Now find the widest deepest branch from any node j in layer t to node i in layer t + 1
# Widest, deepest meaning: greatest sum of depth up to node j in layer t plus number of shared cells
# But disallowing any branch with less than min_pct % shared cells
best_j = -1
best_depth = 0
for j in range(n_nodes[t]):
pct_overlapping = 100 * overlaps[t][j, i] / (
n_cells[t][j] + n_cells[t + 1][i])
if pct_overlapping > min_pct:
depth = depths[t][j] + overlaps[t][j, i]
if depth > best_depth:
best_depth = depth
best_j = j
edges[t][i] = best_j
# Now we have
#
# edges: List of arrays giving the index of the predecessor of each cluster (or -1 if no predecessor exists)
# overlaps: List of matrices giving the number of cells shared between clusters in layer t and t + 1
# n_nodes: List of number of nodes (clusters) in each layer
# n_cells: List of arrays of number of cells in each node (cluster)
# Now position the nodes of each layer such that no edges cross
ypositions = [np.arange(n_nodes[0])]
for t in range(len(edges)):
pos = np.full(n_nodes[t + 1], -1)
for i in range(pos.shape[0]):
prev = edges[t][i]
if (prev) >= 0:
pos[i] = ypositions[t][prev]
ordering = np.argsort(pos)
mapping = dict(zip(ordering, range(len(ordering))))
ypositions.append(
np.array([mapping[i] for i in range(len(ordering))]))
# Make the positions proportional to the number of cells (cumulative)
max_pos = 0
for i, pos in enumerate(ypositions):
with loompy.connect(os.path.join(tempfolder,
slice_names[i])) as ds0:
n_clusters = ds0.ca[clusters].max() + 1
ncells = np.array([(ds0.ca[clusters] == i).sum()
for i in range(n_clusters)])
total = 0
new_pos = np.zeros_like(pos)
for j in range(len(pos)):
cluster = np.where(pos == j)[0]
new_pos[cluster] = total + ncells[cluster] / 2
total += ncells[cluster]
ypositions[i] = new_pos / 1000
max_pos = max(max_pos, max(ypositions[i]))
for i, pos in enumerate(ypositions):
ypositions[i] += (max_pos - np.max(pos)) / 2
# Then position the layers properly in time
xpositions = []
for i in range(n_layers):
with loompy.connect(os.path.join(tempfolder,
slice_names[i])) as ds0:
xpositions.append(np.mean(ds0.ca.PseudoAge))
# Now project each individual cell to the pseudolineage
logging.info("Projecting cells to pseudolineage")
cell_to_xy = {}
for t in range(len(n_nodes) - 1):
with loompy.connect(os.path.join(tempfolder,
slice_names[t])) as ds0:
with loompy.connect(
os.path.join(tempfolder,
slice_names[t + 1])) as ds1:
for i in range(n_nodes[t + 1]):
if edges[t][i] != -1:
y1 = ypositions[t][edges[t][i]]
y2 = ypositions[t + 1][i]
offset = (xpositions[t + 1] -
xpositions[t]) / 4
overlapping_cells = (ds1.ca[clusters] == i) & (
ds1.ca.PseudoAge < slices[t + 2])
crs = np.array(
CatmullRomSpline(
n_points=100).fit_transform(
np.array(
[[slices[t + 1] - offset, y1],
[slices[t + 1], y1],
[slices[t + 2], y2],
[slices[t + 2] + offset,
y2]])))
widths = np.linspace(n_cells[t][edges[t][i]],
n_cells[t + 1][i],
num=100) / 1500
f = interp1d(crs[:, 0],
crs[:, 1],
fill_value="extrapolate")
fw = interp1d(crs[:, 0],
widths,
fill_value="extrapolate")
y = f(
ds1.ca.PseudoAge[overlapping_cells]
) + np.random.normal(
scale=fw(
ds1.ca.PseudoAge[overlapping_cells]) /
6,
size=overlapping_cells.sum())
for i, ix in enumerate(
np.where(overlapping_cells)[0]):
cell_to_xy[ds1.ca.CellID[ix]] = [
ds1.ca.PseudoAge[ix], y[i]
]
# Draw the leftmost pseudoage slice
if t == 0:
for i in range(n_nodes[0]):
y1 = ypositions[0][i]
y2 = ypositions[0][i]
widths = np.linspace(n_cells[t][i],
n_cells[t][i],
num=100) / 1500
overlapping_cells = (ds0.ca[clusters] == i) & (
ds0.ca.PseudoAge < slices[1])
y = y1 + np.random.normal(
scale=widths[0] / 6,
size=overlapping_cells.sum())
for i, ix in enumerate(
np.where(overlapping_cells)[0]):
cell_to_xy[ds1.ca.CellID[ix]] = [
ds0.ca.PseudoAge[ix], y[i]
]
# Draw the rightmost pseudoage slice
if t == len(n_nodes) - 2:
for i in range(n_nodes[-1]):
y1 = ypositions[t][edges[t][i]]
y2 = ypositions[t + 1][i]
widths = np.linspace(n_cells[t][edges[t][i]],
n_cells[t + 1][i],
num=100) / 1500
overlapping_cells = (ds1.ca[clusters] == i) & (
ds1.ca.PseudoAge > slices[-2])
y = y2 + np.random.normal(
scale=widths[-1] / 6,
size=overlapping_cells.sum())
for i, ix in enumerate(
np.where(overlapping_cells)[0]):
cell_to_xy[ds1.ca.CellID[ix]] = [
ds1.ca.PseudoAge[ix], y[i]
]
logging.info(
"Saving pseudolineage projection back in original file")
logging.info(ds.ca)
return cell_to_xy
xy = np.zeros((ds.shape[1], 2))
for i, cellid in enumerate(cell_to_xy.keys()):
j = np.where(ds.ca.CellID == cellid)[0]
xy[j] = cell_to_xy[cellid]
ds.ca.PseudoLineage = xy
def fit(self,
ds: loompy.LoomConnection,
plot_file: str = None,
report_file: str = None) -> np.ndarray:
"""
Fit a classifier and use it to determine cluster predictive power
Args:
ds Dataset
plot_file Filename for optional plot
report_file Filename for optional report
Returns:
Matrix of classification probabilities, shape (n_cells, n_labels)
"""
if "ClusterName" in ds.ca:
cluster_names = [
str(ds.ca.ClusterName[ds.ca.Clusters == lbl][0])
for lbl in np.unique(ds.ca.Clusters)
]
else:
cluster_names = [str(lbl) for lbl in np.unique(ds.ca.Clusters)]
genes = np.where(ds.ra.Selected == 1)[0]
data = ds.sparse(rows=genes).T
hpf = HPF(k=ds.ca.HPF.shape[1],
validation_fraction=0.05,
min_iter=10,
max_iter=200,
compute_X_ppv=False)
hpf.fit(data)
theta = (hpf.theta.T / hpf.theta.sum(axis=1)).T
train_X, test_X, train_Y, test_Y = train_test_split(theta,
ds.ca.Clusters,
test_size=0.2)
classifier = RandomForestClassifier(max_depth=30)
classifier.fit(train_X, train_Y)
self.report = classification_report(test_Y,
classifier.predict(test_X),
labels=np.unique(ds.ca.Clusters),
target_names=cluster_names)
self.proba = classifier.predict_proba(theta)
if plot_file is not None:
plt.figure()
agg = npg.aggregate(ds.ca.Clusters,
self.proba,
axis=0,
func="mean")
plt.imshow(agg, cmap="viridis")
plt.xticks(np.arange(len(cluster_names)),
cluster_names,
rotation="vertical",
fontsize=7)
plt.yticks(np.arange(len(cluster_names)),
cluster_names,
rotation="horizontal",
fontsize=7)
plt.xlabel("Predicted cluster")
plt.ylabel("Ground truth cluster")
plt.title("Cluster quality (predictive power)")
cbar = plt.colorbar()
cbar.set_label('Probability of predicted cluster', rotation=90)
plt.savefig(plot_file, bbox_inches="tight")
plt.close()
if report_file is not None:
with open(report_file, "w") as f:
f.write(self.report)
return self.proba
def fit(self, ds: loompy.LoomConnection) -> None:
logging.info(f"Normalizing and selecting {self.n_genes} genes")
normalizer = Normalizer(False)
normalizer.fit(ds)
genes = FeatureSelectionByVariance(self.n_genes,
mask=self.mask).fit(ds)
self.genes = genes
if self.factorization == 'PCA' or self.factorization == 'both' or self.batch_keys is not None:
factorization = "PCA"
else:
factorization = "HPF"
if factorization == "PCA":
n_components = min(50, ds.shape[1])
logging.info("PCA projection to %d components", n_components)
pca = PCA(genes,
max_n_components=self.n_factors,
test_significance=False,
batch_keys=self.batch_keys)
transformed = pca.fit_transform(ds, normalizer)
else:
data = ds.sparse(rows=genes).T
# Subsample to lowest number of UMIs
if "TotalUMI" in ds.ca:
totals = ds.ca.TotalUMI
else:
totals = ds.map([np.sum], axis=1)[0]
min_umis = int(np.min(totals))
logging.debug(f"Subsampling to {min_umis} UMIs")
temp = data.toarray()
for c in range(temp.shape[0]):
temp[c, :] = np.random.binomial(temp[c, :].astype('int32'),
min_umis / totals[c])
data = sparse.coo_matrix(temp)
# HPF factorization
hpf = HPF(k=self.n_factors,
validation_fraction=0.05,
min_iter=10,
max_iter=200,
compute_X_ppv=False,
n_threads=self.n_threads)
hpf.fit(data)
transformed = (
hpf.theta.T / hpf.theta.sum(axis=1)
).T # Normalize so the sums are one because JSD requires it
# KNN in latent space
logging.info(f"Computing KNN (k={self.k_pooling}) in latent space")
with warnings.catch_warnings():
warnings.simplefilter(
"ignore", category=NumbaPerformanceWarning
) # Suppress warnings about numba not being able to parallelize code
warnings.simplefilter(
"ignore", category=NumbaPendingDeprecationWarning
) # Suppress warnings about future deprecations
warnings.simplefilter(
"ignore", category=SparseEfficiencyWarning
) # Suppress warnings about setting the diagonal to 1
nn = NNDescent(data=transformed,
metric=(jensen_shannon_distance
if factorization == "HPF" else "euclidean"))
indices, distances = nn.query(transformed, k=self.k_pooling)
# Note: we convert distances to similarities here, to support Poisson smoothing below
knn = sparse.csr_matrix(
(np.ravel(distances), np.ravel(indices),
np.arange(0, distances.shape[0] * distances.shape[1] + 1,
distances.shape[1])),
(transformed.shape[0], transformed.shape[0]))
max_d = knn.data.max()
knn.data = (max_d - knn.data) / max_d
knn.setdiag(
1
) # This causes a sparse efficiency warning, but it's not a slow step relative to everything else
self.knn = knn
def aggregate_loom(ds: loompy.LoomConnection,
out_file: str,
select: np.ndarray,
group_by: str,
aggr_by: str,
aggr_ca_by: Dict[str, str],
return_matrix: bool = False) -> np.ndarray:
"""
Aggregate a Loom file by applying aggregation functions to the main matrix as well as to the column attributes
Args:
ds The Loom file
out_file The name of the output Loom file (will be appended to if it exists)
select Bool array giving the columns to include (or None, to include all)
group_by The column attribute to group by
aggr_by The aggregation function for the main matrix
aggr_ca_by The aggregation functions for the column attributes (or None to skip)
Remarks:
aggr_by gives the aggregation function for the main matrix
aggr_ca_by is a dictionary with column attributes as keys and aggregation functionas as values
Aggregation functions can be any valid aggregation function from here: https://github.com/ml31415/numpy-groupies
In addition, you can specify:
"tally" to count the number of occurences of each value of a categorical attribute
"""
ca = {} # type: Dict[str, np.ndarray]
if select is not None:
raise ValueError("The 'select' argument is deprecated")
labels = (ds.ca[group_by]).astype('int')
_, zero_strt_sort_noholes_lbls = np.unique(labels, return_inverse=True)
n_groups = len(set(labels))
if aggr_ca_by is not None:
for key in ds.col_attrs.keys():
if key not in aggr_ca_by:
continue
func = aggr_ca_by[key]
if func == "tally":
for val in set(ds.col_attrs[key]):
ca[key + "_" + val] = npg.aggregate(
zero_strt_sort_noholes_lbls,
(ds.col_attrs[key] == val).astype('int'),
func="sum",
fill_value=0)
elif func == "mode":
def mode(x):
return scipy.stats.mode(x)[0][0]
ca[key] = npg.aggregate(zero_strt_sort_noholes_lbls,
ds.col_attrs[key],
func=mode,
fill_value=0).astype('str')
elif func == "mean":
ca[key] = npg.aggregate(zero_strt_sort_noholes_lbls,
ds.col_attrs[key],
func=func,
fill_value=0)
elif func == "first":
ca[key] = npg.aggregate(zero_strt_sort_noholes_lbls,
ds.col_attrs[key],
func=func,
fill_value=ds.col_attrs[key][0])
m = np.empty((ds.shape[0], n_groups))
for (_, selection, view) in ds.scan(axis=0):
vals_aggr = npg.aggregate(zero_strt_sort_noholes_lbls,
view[:, :],
func=aggr_by,
axis=1,
fill_value=0)
m[selection, :] = vals_aggr
if return_matrix:
return m
loompy.create_append(out_file, m, ds.ra, ca, fill_values="auto")
def fit(self, ds: loompy.LoomConnection) -> Tuple[sparse.coo_matrix, sparse.coo_matrix, np.ndarray]:
"""
Discover the manifold
Args:
n_genes Number of genes to use for manifold learning (ignored if genes is not None)
gtsnse Use graph t-SNE for layout (default: standard tSNE)
alpha The scale parameter for multiscale KNN
genes List of genes to use for manifold learning
Returns:
knn The multiscale knn graph as a sparse matrix, with k = 100
mknn Mutual knn subgraph, with k = 20
pos 2D projection (t-SNE or gt-SNE) as ndarray with shape (n_cells, 2)
"""
n_valid = np.sum(ds.col_attrs["_Valid"] == 1)
n_total = ds.shape[1]
logging.info("%d of %d cells were valid", n_valid, n_total)
logging.info("%d of %d genes were valid", np.sum(ds.row_attrs["_Valid"] == 1), ds.shape[0])
cells = np.where(ds.col_attrs["_Valid"] == 1)[0]
logging.info("Normalization")
normalizer = cg.Normalizer(False)
normalizer.fit(ds)
if self.filter_cellcycle is not None:
cell_cycle_genes = np.array(open(self.filter_cellcycle).read().split())
mask = np.in1d(ds.ra.Gene, cell_cycle_genes)
if np.sum(mask) == 0:
logging.warn("None cell cycle genes where filtered, check your gene list")
else:
mask = None
if self.genes is None:
logging.info("Selecting up to %d genes", self.n_genes)
genes = cg.FeatureSelection(self.n_genes).fit(ds, mu=normalizer.mu, sd=normalizer.sd, mask=mask)
temp = np.zeros(ds.shape[0])
temp[genes] = 1
ds.set_attr("_Selected", temp, axis=0)
logging.info("%d genes selected", temp.sum())
n_components = min(50, n_valid)
logging.info("PCA projection to %d components", n_components)
pca = cg.PCAProjection(genes, max_n_components=n_components, layer=self.layer)
pca_transformed = pca.fit_transform(ds, normalizer, cells=cells)
transformed = pca_transformed
logging.info("Generating KNN graph")
k = min(10, n_valid - 1)
nn = NearestNeighbors(n_neighbors=k, algorithm="ball_tree", n_jobs=4)
nn.fit(transformed)
knn = nn.kneighbors_graph(mode='connectivity')
knn = knn.tocoo()
mknn = knn.minimum(knn.transpose()).tocoo()
logging.info("Louvain-Jaccard clustering")
lj = cg.LouvainJaccard(resolution=1)
labels = lj.fit_predict(knn)
# Make labels for excluded cells == -1
labels_all = np.zeros(ds.shape[1], dtype='int') + -1
labels_all[cells] = labels
ds.set_attr("Clusters", labels_all, axis=1)
n_labels = np.max(labels) + 1
logging.info("Found " + str(n_labels) + " LJ clusters")
logging.info("Marker selection")
(genes, _, _) = cg.MarkerSelection(n_markers=int(500 / n_labels), mask=mask).fit(ds)
else:
genes = self.genes
temp = np.zeros(ds.shape[0])
temp[genes] = 1
ds.set_attr("_Selected", temp, axis=0)
logging.info("%d genes selected", temp.sum())
n_components = min(50, n_valid)
logging.info("PCA projection to %d components", n_components)
pca = cg.PCAProjection(genes, max_n_components=n_components, layer=self.layer)
pca_transformed = pca.fit_transform(ds, normalizer, cells=cells)
transformed = pca_transformed
logging.info("Generating KNN graph")
k = min(10, n_valid - 1)
nn = NearestNeighbors(n_neighbors=k, algorithm="ball_tree", n_jobs=4)
nn.fit(transformed)
knn = nn.kneighbors_graph(mode='connectivity')
knn = knn.tocoo()
mknn = knn.minimum(knn.transpose()).tocoo()
logging.info("Louvain-Jaccard clustering")
lj = cg.LouvainJaccard(resolution=1)
labels = lj.fit_predict(knn)
# Make labels for excluded cells == -1
labels_all = np.zeros(ds.shape[1], dtype='int') + -1
labels_all[cells] = labels
ds.set_attr("Clusters", labels_all, axis=1)
n_labels = np.max(labels) + 1
logging.info("Found " + str(n_labels) + " LJ clusters")
logging.info("Marker selection")
(genes, _, _) = cg.MarkerSelection(n_markers=int(500 / n_labels)).fit(ds)
# Select cells across clusters more uniformly, preventing a single cluster from dominating the PCA
cells_adjusted = cg.cap_select(labels, cells, int(n_valid * 0.2))
n_components = min(50, cells_adjusted.shape[0])
logging.info("PCA projection to %d components", n_components)
pca = cg.PCAProjection(genes, max_n_components=n_components)
pca.fit(ds, normalizer, cells=cells_adjusted)
# Note that here we're transforming all cells; we just did the fit on the selection
transformed = pca.transform(ds, normalizer, cells=cells)
k = min(100, n_valid - 1)
logging.info("Generating multiscale KNN graph (k = %d)", k)
nn = NearestNeighbors(n_neighbors=k, algorithm="ball_tree", n_jobs=4)
nn.fit(transformed)
knn = nn.kneighbors(return_distance=False) # shape: (n_cells, k)
n_cells = knn.shape[0]
a = np.tile(np.arange(n_cells), k)
b = np.reshape(knn.T, (n_cells * k,))
w = np.repeat(1 / np.power(np.arange(1, k + 1), self.alpha), n_cells)
knn = sparse.coo_matrix((w, (a, b)), shape=(n_cells, n_cells))
threshold = w > 0.05
mknn = sparse.coo_matrix((w[threshold], (a[threshold], b[threshold])), shape=(n_cells, n_cells))
mknn = mknn.minimum(mknn.transpose()).tocoo()
perplexity = min(k, (n_valid - 1) / 3 - 1)
if self.gtsne:
logging.info("gt-SNE layout")
# Note that perplexity argument is ignored in this case, but must still be given
# because bhtsne will check that it has a valid value
tsne_pos = cg.TSNE(perplexity=perplexity).layout(transformed, knn=knn.tocsr())
else:
logging.info("t-SNE layout")
tsne_pos = cg.TSNE(perplexity=perplexity).layout(transformed)
tsne_all = np.zeros((ds.shape[1], 2), dtype='int') + np.min(tsne_pos, axis=0)
tsne_all[cells] = tsne_pos
# Transform back to the full set of cells
knn = sparse.coo_matrix((knn.data, (cells[knn.row], cells[knn.col])), shape=(n_total, n_total))
mknn = sparse.coo_matrix((mknn.data, (cells[mknn.row], cells[mknn.col])), shape=(n_total, n_total))
return (knn, mknn, tsne_all)
def fit(
self, ds: loompy.LoomConnection
) -> Tuple[sparse.coo_matrix, sparse.coo_matrix, np.ndarray]:
"""
Discover the manifold
Returns:
knn The knn graph as a sparse matrix
mknn Mutual knn subgraph
pos 2D projection (gt-SNE) as ndarray with shape (n_cells, 2)
"""
n_cells = ds.shape[1]
logging.info("Processing all %d cells", n_cells)
logging.info("Validating genes")
nnz = ds.map([np.count_nonzero], axis=0)[0]
valid_genes = np.logical_and(nnz > 5,
nnz < ds.shape[1] * 0.5).astype("int")
ds.ra._Valid = valid_genes
logging.info("%d of %d genes were valid", np.sum(ds.ra._Valid == 1),
ds.shape[0])
logging.info("Normalization")
normalizer = cg.Normalizer(False)
normalizer.fit(ds)
logging.info("Selecting up to %d genes", self.n_genes)
genes = cg.FeatureSelection(self.n_genes).fit(ds,
mu=normalizer.mu,
sd=normalizer.sd)
logging.info("Loading data for selected genes")
data = np.zeros((n_cells, genes.shape[0]))
for (ix, selection, view) in ds.scan(axis=1):
data[selection - ix, :] = view[genes, :].T
logging.info("Computing initial subspace KNN")
subspaces = np.ones(data.shape)
knn = subspace_knn_graph(data, subspaces)
mknn = knn.minimum(knn.transpose()).tocoo()
for t in range(5):
logging.info(f"Refining subspace KNN (iteration {t + 1})")
logging.info("Louvain clustering")
graph = nx.from_scipy_sparse_matrix(mknn)
partitions = community.best_partition(graph)
labels = np.array(
[partitions[key] for key in range(mknn.shape[0])])
ds.ca.Clusters = labels
n_labels = np.max(labels) + 1
logging.info(f"Found {n_labels} clusters")
logging.info("Marker selection")
(_, enrichment, _) = cg.MarkerSelection(n_markers=10,
findq=False).fit(ds)
subspaces = np.zeros(data.shape)
for ix in range(enrichment.shape[1]):
for j in range(n_cells):
subspaces[j,
np.argsort(-enrichment[:, ix])[:self.n_genes //
n_labels]] = 1
knn = subspace_knn_graph(data, subspaces)
mknn = knn.minimum(knn.transpose()).tocoo()
perplexity = min(self.k, (n_cells - 1) / 3 - 1)
logging.info("gt-SNE layout")
# Note that perplexity argument is ignored in this case, but must still be given
# because bhtsne will check that it has a valid value
tsne_pos = cg.TSNE(perplexity=perplexity).layout(data, knn=knn.tocsr())
return (knn, mknn, tsne_pos)
def _fit(self, ds: loompy.LoomConnection) -> Tuple[np.ndarray, np.ndarray]:
"""
Finds n_markers genes per cluster using enrichment score
Args:
ds (LoomConnection): Dataset
Returns:
ndarray of selected genes (list of ints)
ndarray of enrichment scores
"""
labels = ds.ca[self.labels_attr]
n_labels = max(labels) + 1
n_cells = ds.shape[1]
# Number of cells per cluster
sizes = np.bincount(labels, minlength=n_labels)
# Number of nonzero values per cluster
nnz = cg.aggregate_loom(ds, None, None, self.labels_attr, np.count_nonzero, None, return_matrix=True)
# Mean value per cluster
means = cg.aggregate_loom(ds, None, None, self.labels_attr, "mean", None, return_matrix=True)
# 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 "_Valid" not in ds.ra:
logging.info("Recomputing the list of valid genes")
nnz = ds.map([np.count_nonzero], axis=0)[0]
valid_genes = np.logical_and(nnz > 10, nnz < ds.shape[1] * 0.6)
ds.ra._Valid = valid_genes.astype('int')
included: List[int] = []
if self.mask is None:
excluded = set(np.where(ds.row_attrs["_Valid"] == 0)[0])
else:
excluded = set(np.where(np.logical_and(ds.row_attrs["_Valid"] == 0, self.mask))[0])
for ix in range(max(labels) + 1):
enriched = np.argsort(enrichment[:, ix])[::-1]
n = 0
count = 0
while count < self.n_markers:
if enriched[n] in excluded:
n += 1
continue
included.append(enriched[n])
excluded.add(enriched[n])
n += 1
count += 1
return (np.array(included), enrichment)
