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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,68 @@ | ||
| import gzip | ||
| import json | ||
| import os | ||
| from collections import defaultdict | ||
|
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| from backend.cellguide.common.config import CellGuideConfig | ||
| from backend.cellguide.common.constants import COMPUTATIONAL_MARKER_GENES_FOLDERNAME, MARKER_GENE_PRESENCE_FILENAME | ||
| from backend.cellguide.common.providers.s3_provider import S3Provider | ||
| from backend.cellguide.common.utils import get_object_key | ||
|
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| def _defaultdict_to_dict(d): | ||
| if isinstance(d, defaultdict): | ||
| # Convert the defaultdict to a dict and recursively apply this function | ||
| return {key: _defaultdict_to_dict(value) for key, value in d.items()} | ||
| else: | ||
| return d | ||
|
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| def _initialize_cellguide_marker_gene_dict(): | ||
| bucket = CellGuideConfig().bucket | ||
| s3_provider = S3Provider() | ||
|
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| latest_snapshot_identifier = ( | ||
| s3_provider.download_file(bucket_name=bucket, object_key=get_object_key(object="latest_snapshot_identifier")) | ||
| .decode("utf-8") | ||
| .strip() | ||
| ) | ||
| compressed_data = s3_provider.download_file( | ||
| bucket_name=bucket, | ||
| object_key=get_object_key( | ||
| object=f"{latest_snapshot_identifier}/{COMPUTATIONAL_MARKER_GENES_FOLDERNAME}/{MARKER_GENE_PRESENCE_FILENAME}" | ||
| ), | ||
| ) | ||
| marker_gene_data = json.loads(gzip.decompress(compressed_data).decode("utf-8")) | ||
| data = defaultdict(lambda: defaultdict(lambda: defaultdict(list))) | ||
|
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| for gene in marker_gene_data: | ||
| for organism in marker_gene_data[gene]: | ||
| for tissue in marker_gene_data[gene][organism]: | ||
| for marker in marker_gene_data[gene][organism][tissue]: | ||
| data[organism][tissue][marker["cell_type_id"]].append( | ||
| {"marker_score": marker["marker_score"], "me": marker["me"], "pc": marker["pc"], "gene": gene} | ||
| ) | ||
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| data = _defaultdict_to_dict(data) | ||
|
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| for organism in data: | ||
| for tissue in data[organism]: | ||
| for cell_type in data[organism][tissue]: | ||
| data[organism][tissue][cell_type].sort(key=lambda x: -x["marker_score"]) | ||
|
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| return data | ||
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| _marker_gene_data_cache = None | ||
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| def get_marker_gene_data(): | ||
| global _marker_gene_data_cache | ||
| if _marker_gene_data_cache is None: | ||
| if os.getenv("DEPLOYMENT_STAGE") != "test": | ||
| # Initialize the marker gene data from the latest snapshot only if not in test mode | ||
| _marker_gene_data_cache = _initialize_cellguide_marker_gene_dict() | ||
| else: | ||
| # Initialize an empty structure if in test mode | ||
| _marker_gene_data_cache = {} | ||
| return _marker_gene_data_cache |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,23 @@ | ||
| from flask import jsonify, make_response | ||
|
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| from backend.cellguide.api.common.data import get_marker_gene_data | ||
|
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| def get(): | ||
| """ | ||
| Retrieve all CellGuide marker gene data. | ||
| This function handles the retrieval of all CellGuide marker gene data. | ||
| Returns: | ||
| Flask Response: JSON data of the marker genes. | ||
| The response structure is a nested dictionary with the following structure: | ||
| - organisms --> tissues --> cell types --> list(marker genes). | ||
| Organisms and tissues are labels, cell types are IDs, and marker genes are dictionaries with the following keys: | ||
| - `marker_score`: The score indicating the strength of the marker gene for the cell type. | ||
| - `me`: Mean expression of the gene across the cells of the specified type. | ||
| - `pc`: Percentage of cells within the specified type that express the gene. | ||
| - `gene`: The gene symbol associated with the marker gene data. | ||
| """ | ||
| return make_response(jsonify(get_marker_gene_data()), 200) |
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