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 update_state(self, step, status_code, status_message, values):
state = GeneSetEnrichment.State(step=step,
status_code=status_code,
status_message=status_message,
values=values)
logger.debug("Status: {0}".format(state.get_status_message()))
if state.get_values() is None:
return s_pb2.GeneSetEnrichmentReply(
progress=s_pb2.Progress(value=state.get_step(),
status=state.get_status_message()),
isDone=False,
cellValues=s_pb2.CellColorByFeaturesReply(color=[],
vmax=[],
maxVmax=[],
cellIndices=[]),
)
else:
vmax = np.zeros(3)
max_vmax = np.zeros(3)
aucs = state.get_values()
_vmax = data.get_99_and_100_percentiles(values=aucs)
vmax[0] = _vmax[0]
max_vmax[0] = _vmax[1]
vals = aucs / vmax[0]
vals = (((constant.UPPER_LIMIT_RGB - constant.LOWER_LIMIT_RGB) *
(vals - min(vals))) /
(1 - min(vals))) + constant.LOWER_LIMIT_RGB
hex_vec = [
"null" if r == g == b == 0 else "{0:02x}{1:02x}{2:02x}".format(
int(r), int(g), int(b)) for r, g, b in zip(
vals, np.zeros(len(aucs)), np.zeros(len(aucs)))
]
cell_indices = list(range(self.loom.get_nb_cells()))
return s_pb2.GeneSetEnrichmentReply(
progress=s_pb2.Progress(value=state.get_step(),
status=state.get_status_message()),
isDone=True,
cellValues=s_pb2.CellColorByFeaturesReply(
color=hex_vec,
vmax=vmax,
maxVmax=max_vmax,
cellIndices=cell_indices),
)
def getCellColorByFeatures(self, request, context):
start_time = time.time()
try:
loom = self.lfh.get_loom(loom_file_path=request.loomFilePath)
except ValueError:
return
cell_color_by_features = ccbf.CellColorByFeatures(loom=loom)
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)
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()
print("Debug: %s seconds elapsed ---" % (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 setAnnotationFeature(self, feature):
md_annotation_values = self.loom.get_meta_data_annotation_by_name(
name=feature)["values"]
ca_annotation = self.loom.get_ca_attr_by_name(name=feature)
ca_annotation_as_int = list(
map(lambda x: md_annotation_values.index(str(x)), ca_annotation))
num_annotations = max(ca_annotation_as_int)
if num_annotations <= len(Constant.BIG_COLOR_LIST):
self.hex_vec = list(
map(lambda x: Constant.BIG_COLOR_LIST[x],
ca_annotation_as_int))
else:
raise ValueError(
"The annotation {0} has too many unique values.".format(
feature))
# Set the reply
reply = s_pb2.CellColorByFeaturesReply(
color=self.hex_vec,
vmax=self.v_max,
legend=s_pb2.ColorLegend(
values=md_annotation_values,
colors=Constant.BIG_COLOR_LIST[:len(md_annotation_values)]))
self.setReply(reply=reply)
def setAnnotationFeature(self,
feature: str,
annotations: Optional[List[Annotation]] = None,
logic: str = "OR"):
md_annotation_values = self.loom.get_meta_data_annotation_by_name(
name=feature)["values"]
ca_annotation = self.loom.get_ca_attr_by_name(name=feature)
ca_annotation_as_int = list(
map(lambda x: md_annotation_values.index(str(x)), ca_annotation))
self.hex_vec = to_colours(ca_annotation_as_int)
if annotations is not None:
cellIndices = self.loom.get_anno_cells(annotations=annotations,
logic=logic)
self.hex_vec = np.array(self.hex_vec)[cellIndices]
reply = s_pb2.CellColorByFeaturesReply(
color=self.hex_vec,
vmax=self.v_max,
legend=s_pb2.ColorLegend(values=md_annotation_values,
colors=to_colours(
range(len(md_annotation_values)))),
)
self.setReply(reply=reply)
def setClusteringFeature(self, request, feature, n, secret=None):
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))
if numClusters <= 245:
self.legend = set()
clustering_meta = self.loom.get_meta_data_clustering_by_id(
int(clusteringID), secret=secret)
cluster_dict = {
int(x["id"]): x["description"]
for x in clustering_meta["clusters"]
}
for i in self.loom.get_clustering_by_id(clusteringID):
self.hex_vec.append(constant.BIG_COLOR_LIST[i])
self.legend.add(
(cluster_dict[i], constant.BIG_COLOR_LIST[i]))
self.legend = s_pb2.ColorLegend(
values=[x[0] for x in self.legend],
colors=[x[1] for x in self.legend])
else:
interval = int(RGB_COLORS / numClusters)
self.hex_vec = [
hex(i)[2:].zfill(6)
for i in range(0, numClusters, interval)
]
if len(request.annotation) > 0:
cellIndices = self.loom.get_anno_cells(
annotations=request.annotation,
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:
cellIndices = self.loom.get_anno_cells(
annotations=request.annotation, logic=request.logic)
clusterCol = clusterCol[cellIndices]
self.features.append(clusterCol)
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)