def setGeneFeature(self, request, feature, n):
if feature != "":
if len(request.annotation) > 0:
annotations = [
Annotation(name=ann.name, values=ann.values)
for ann in request.annotation
]
else:
annotations = None
vals, self.cellIndices = self.loom.get_gene_expression(
gene_symbol=feature,
log_transform=request.hasLogTransform,
cpm_normalise=request.hasCpmTransform,
annotation=annotations,
logic=request.logic,
)
if request.vmax[n] != 0.0:
self.v_max[n] = request.vmax[n]
else:
self.v_max[n], self.max_v_max[
n] = data.get_99_and_100_percentiles(vals)
vals = CellColorByFeatures.normalise_vals(vals, self.v_max[n],
request.vmin[n])
self.features.append(vals)
else:
self.features.append(np.zeros(self.n_cells))
def setRegulonFeature(self, request, feature, n):
if feature != "":
if len(request.annotation) > 0:
annotations = [
Annotation(name=ann.name, values=ann.values)
for ann in request.annotation
]
else:
annotations = None
vals, self.cellIndices = self.loom.get_auc_values(
regulon=feature, annotation=annotations, logic=request.logic)
if request.vmax[n] != 0.0:
self.v_max[n] = request.vmax[n]
else:
self.v_max[n], self.max_v_max[
n] = data.get_99_and_100_percentiles(vals)
if request.scaleThresholded:
vals = np.array([
auc if auc >= request.threshold[n] else 0 for auc in vals
])
vals = CellColorByFeatures.normalise_vals(
vals, self.v_max[n], request.vmin[n])
self.features.append(vals)
else:
self.features.append([
constant.UPPER_LIMIT_RGB
if auc >= request.threshold[n] else 0 for auc in vals
])
else:
self.features.append(np.zeros(self.n_cells))
def getCellColorByFeatures(self, request, context):
start_time = time.time()
try:
loom = self.lfh.get_loom(loom_file_path=Path(request.loomFilePath))
except ValueError:
return
cell_color_by_features = ccbf.CellColorByFeatures(loom=loom)
if len(request.annotation) > 0:
annotations = [
Annotation(name=ann.name, values=ann.values)
for ann in request.annotation
]
else:
annotations = None
for n, feature in enumerate(request.feature):
if request.featureType[n] == "gene":
cell_color_by_features.setGeneFeature(request=request,
feature=feature,
n=n)
elif request.featureType[n] == "regulon":
cell_color_by_features.setRegulonFeature(request=request,
feature=feature,
n=n)
elif request.featureType[n] == "annotation":
cell_color_by_features.setAnnotationFeature(
feature=feature,
annotations=annotations,
logic=request.logic)
return cell_color_by_features.getReply()
elif request.featureType[n] == "metric":
cell_color_by_features.setMetricFeature(request=request,
feature=feature,
n=n)
elif request.featureType[n].startswith("Clustering: "):
cell_color_by_features.setClusteringFeature(request=request,
feature=feature,
n=n)
if cell_color_by_features.hasReply():
return cell_color_by_features.getReply()
else:
cell_color_by_features.addEmptyFeature()
logger.debug(
"{0:.5f} seconds elapsed getting colours ---".format(time.time() -
start_time))
return s_pb2.CellColorByFeaturesReply(
color=None,
compressedColor=cell_color_by_features.get_compressed_hex_vec(),
hasAddCompressionLayer=True,
vmax=cell_color_by_features.get_v_max(),
maxVmax=cell_color_by_features.get_max_v_max(),
cellIndices=cell_color_by_features.get_cell_indices(),
)
def labels() -> Generator[FeatureLabel, None, None]:
for i, annotation in enumerate(values):
coords = loom.get_coordinates(
coordinatesID=embedding, annotation=[Annotation(name=feature, values=[annotation])]
)
yield FeatureLabel(
label=annotation,
colour=colours[i],
coordinate=Coordinate(x=np.mean(coords["x"]), y=np.mean(coords["y"])),
)
def getCoordinates(self, request, context):
# request content
loom = self.lfh.get_loom(loom_file_path=Path(request.loomFilePath))
if len(request.annotation) > 0:
annotations = [
Annotation(name=ann.name, values=ann.values)
for ann in request.annotation
]
else:
annotations = None
c = loom.get_coordinates(coordinatesID=request.coordinatesID,
annotation=annotations,
logic=request.logic)
return s_pb2.CoordinatesReply(x=c["x"],
y=c["y"],
cellIndices=c["cellIndices"])
def setClusteringFeature(self, request, feature, n):
clusteringID = None
clusterID = None
logger.debug("Getting clustering: {0}".format(request.feature[n]))
for clustering in self.meta_data["clusterings"]:
if clustering["name"] == re.sub("^Clustering: ", "",
request.featureType[n]):
clusteringID = str(clustering["id"])
if request.feature[n] == "All Clusters":
numClusters = max(
self.loom.get_clustering_by_id(clusteringID))
legend = set()
cluster_names_dict = self.loom.get_cluster_names(
int(clusteringID))
for i in self.loom.get_clustering_by_id(clusteringID):
if i == -1:
self.hex_vec.append("XX" * 3)
continue
colour = constant.BIG_COLOR_LIST[i % len(
constant.BIG_COLOR_LIST)]
self.hex_vec.append(colour)
legend.add((cluster_names_dict[i], colour))
values, colors = zip(*legend)
self.legend = s_pb2.ColorLegend(values=values,
colors=colors)
if len(request.annotation) > 0:
annotations = [
Annotation(name=ann.name, values=ann.values)
for ann in request.annotation
]
cellIndices = self.loom.get_anno_cells(
annotations=annotations, logic=request.logic)
self.hex_vec = np.array(self.hex_vec)[cellIndices]
# Set the reply and break the for loop
reply = s_pb2.CellColorByFeaturesReply(color=self.hex_vec,
vmax=self.v_max,
legend=self.legend)
self.setReply(reply=reply)
break
else:
for cluster in clustering["clusters"]:
if request.feature[n] == cluster["description"]:
clusterID = int(cluster["id"])
if clusteringID is None and clusterID is None and len(
request.feature[n]) > 0:
error_message = "The cluster '{0}' does not exist in the current active .loom. Clear the query to continue with SCope.".format(
request.feature[n])
self.setReply(
s_pb2.CellColorByFeaturesReply(error=s_pb2.ErrorReply(
type="Value Error", message=error_message)))
if clusteringID is not None and clusterID is not None:
clusterIndices = self.loom.get_clustering_by_id(
clusteringID) == clusterID
clusterCol = np.array(
[constant.UPPER_LIMIT_RGB if x else 0 for x in clusterIndices])
if len(request.annotation) > 0:
annotations = [
Annotation(name=ann.name, values=ann.values)
for ann in request.annotation
]
cellIndices = self.loom.get_anno_cells(annotations=annotations,
logic=request.logic)
clusterCol = clusterCol[cellIndices]
self.features.append(clusterCol)