Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
format_version: int64
release: timestamp[s]
file_count: int64
total_bytes: int64
files: list<item: struct<path: string, bytes: int64, sha256: string>>
child 0, item: struct<path: string, bytes: int64, sha256: string>
child 0, path: string
child 1, bytes: int64
child 2, sha256: string
slice_id: string
meta: struct<source: string>
child 0, source: string
input_evidence: struct<spot: struct<slice_id: string, barcode: string, species: string, organ: string, spot_key: str (... 1169 chars omitted)
child 0, spot: struct<slice_id: string, barcode: string, species: string, organ: string, spot_key: string, spatial_ (... 426 chars omitted)
child 0, slice_id: string
child 1, barcode: string
child 2, species: string
child 3, organ: string
child 4, spot_key: string
child 5, spatial_context: struct<spot_key: string, slice_id: string, barcode: string, x: double, y: double, array_row: null, a (... 316 chars omitted)
child 0, spot_key: string
child 1, slice_id: string
child 2, barcode: string
child 3, x: double
child 4, y: double
child 5, array_row: null
child 6, array_col: null
child 7, n_neighbors: int64
child 8, neighborhood_consensus: struct<label_agreement_ratio: double, comp_cosine_mean: double>
child 0, label_agreement_ratio: double
child 1, comp_cosine_mean: double
child 9, boundary_label_discordance:
...
child 1, pathway: string
child 2, score: double
child 3, merged_score: double
child 4, hit_count: int64
child 5, enrichment_ratio: double
child 6, precision: double
child 3, quality_flags: struct<fallback_used: bool, low_evidence: bool, pathway_sparse: bool, organ_mismatch_risk: bool>
child 0, fallback_used: bool
child 1, low_evidence: bool
child 2, pathway_sparse: bool
child 3, organ_mismatch_risk: bool
organ: string
barcode: string
species: string
structured_result: struct<cell_type_composition: list<item: struct<cell_type: string, proportion: double, evidence_gene (... 142 chars omitted)
child 0, cell_type_composition: list<item: struct<cell_type: string, proportion: double, evidence_genes: list<item: string>, evidenc (... 21 chars omitted)
child 0, item: struct<cell_type: string, proportion: double, evidence_genes: list<item: string>, evidence_gene_coun (... 9 chars omitted)
child 0, cell_type: string
child 1, proportion: double
child 2, evidence_genes: list<item: string>
child 0, item: string
child 3, evidence_gene_count: int64
child 1, pathway_evidence: struct<reactome_top: list<item: string>, gobp_top: list<item: string>>
child 0, reactome_top: list<item: string>
child 0, item: string
child 1, gobp_top: list<item: string>
child 0, item: string
spot_key: string
to
{'slice_id': Value('string'), 'barcode': Value('string'), 'species': Value('string'), 'organ': Value('string'), 'spot_key': Value('string'), 'input_evidence': {'spot': {'slice_id': Value('string'), 'barcode': Value('string'), 'species': Value('string'), 'organ': Value('string'), 'spot_key': Value('string'), 'spatial_context': {'spot_key': Value('string'), 'slice_id': Value('string'), 'barcode': Value('string'), 'x': Value('float64'), 'y': Value('float64'), 'array_row': Value('null'), 'array_col': Value('null'), 'n_neighbors': Value('int64'), 'neighborhood_consensus': {'label_agreement_ratio': Value('float64'), 'comp_cosine_mean': Value('float64')}, 'boundary_label_discordance': Value('float64'), 'boundary_entropy': Value('float64'), 'local_dominance': Value('float64'), 'neighbors': List({'cell_type': Value('string'), 'proportion': Value('float64'), 'distance': Value('float64')}), 'available': Value('bool')}}, 'inputs': {'top_genes': List(Value('string'))}, 'priors': {'decon_cellmarker': {'celltype_topk': List({'cell_type': Value('string'), 'proportion': Value('float64'), 'evidence_genes': List(Value('string')), 'evidence_gene_count': Value('int64')}), 'dominant_cell_type_optional': Value('string'), 'dominant_cell_type_proportion': Value('float64')}, 'pathway_multidb': {'dbs': List(Value('string')), 'top_gene_count': Value('int64'), 'top_k_merged': Value('int64'), 'min_hit_genes': Value('int64'), 'dominant_pathway_optional': Value('string'), 'merged_topk': List({'db': Value('string'), 'pathway': Value('string'), 'score': Value('float64'), 'merged_score': Value('float64'), 'hit_count': Value('int64'), 'enrichment_ratio': Value('float64'), 'precision': Value('float64')})}}, 'quality_flags': {'fallback_used': Value('bool'), 'low_evidence': Value('bool'), 'pathway_sparse': Value('bool'), 'organ_mismatch_risk': Value('bool')}}, 'structured_result': {'cell_type_composition': List({'cell_type': Value('string'), 'proportion': Value('float64'), 'evidence_genes': List(Value('string')), 'evidence_gene_count': Value('int64')}), 'pathway_evidence': {'reactome_top': List(Value('string')), 'gobp_top': List(Value('string'))}}, 'meta': {'source': Value('string')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
format_version: int64
release: timestamp[s]
file_count: int64
total_bytes: int64
files: list<item: struct<path: string, bytes: int64, sha256: string>>
child 0, item: struct<path: string, bytes: int64, sha256: string>
child 0, path: string
child 1, bytes: int64
child 2, sha256: string
slice_id: string
meta: struct<source: string>
child 0, source: string
input_evidence: struct<spot: struct<slice_id: string, barcode: string, species: string, organ: string, spot_key: str (... 1169 chars omitted)
child 0, spot: struct<slice_id: string, barcode: string, species: string, organ: string, spot_key: string, spatial_ (... 426 chars omitted)
child 0, slice_id: string
child 1, barcode: string
child 2, species: string
child 3, organ: string
child 4, spot_key: string
child 5, spatial_context: struct<spot_key: string, slice_id: string, barcode: string, x: double, y: double, array_row: null, a (... 316 chars omitted)
child 0, spot_key: string
child 1, slice_id: string
child 2, barcode: string
child 3, x: double
child 4, y: double
child 5, array_row: null
child 6, array_col: null
child 7, n_neighbors: int64
child 8, neighborhood_consensus: struct<label_agreement_ratio: double, comp_cosine_mean: double>
child 0, label_agreement_ratio: double
child 1, comp_cosine_mean: double
child 9, boundary_label_discordance:
...
child 1, pathway: string
child 2, score: double
child 3, merged_score: double
child 4, hit_count: int64
child 5, enrichment_ratio: double
child 6, precision: double
child 3, quality_flags: struct<fallback_used: bool, low_evidence: bool, pathway_sparse: bool, organ_mismatch_risk: bool>
child 0, fallback_used: bool
child 1, low_evidence: bool
child 2, pathway_sparse: bool
child 3, organ_mismatch_risk: bool
organ: string
barcode: string
species: string
structured_result: struct<cell_type_composition: list<item: struct<cell_type: string, proportion: double, evidence_gene (... 142 chars omitted)
child 0, cell_type_composition: list<item: struct<cell_type: string, proportion: double, evidence_genes: list<item: string>, evidenc (... 21 chars omitted)
child 0, item: struct<cell_type: string, proportion: double, evidence_genes: list<item: string>, evidence_gene_coun (... 9 chars omitted)
child 0, cell_type: string
child 1, proportion: double
child 2, evidence_genes: list<item: string>
child 0, item: string
child 3, evidence_gene_count: int64
child 1, pathway_evidence: struct<reactome_top: list<item: string>, gobp_top: list<item: string>>
child 0, reactome_top: list<item: string>
child 0, item: string
child 1, gobp_top: list<item: string>
child 0, item: string
spot_key: string
to
{'slice_id': Value('string'), 'barcode': Value('string'), 'species': Value('string'), 'organ': Value('string'), 'spot_key': Value('string'), 'input_evidence': {'spot': {'slice_id': Value('string'), 'barcode': Value('string'), 'species': Value('string'), 'organ': Value('string'), 'spot_key': Value('string'), 'spatial_context': {'spot_key': Value('string'), 'slice_id': Value('string'), 'barcode': Value('string'), 'x': Value('float64'), 'y': Value('float64'), 'array_row': Value('null'), 'array_col': Value('null'), 'n_neighbors': Value('int64'), 'neighborhood_consensus': {'label_agreement_ratio': Value('float64'), 'comp_cosine_mean': Value('float64')}, 'boundary_label_discordance': Value('float64'), 'boundary_entropy': Value('float64'), 'local_dominance': Value('float64'), 'neighbors': List({'cell_type': Value('string'), 'proportion': Value('float64'), 'distance': Value('float64')}), 'available': Value('bool')}}, 'inputs': {'top_genes': List(Value('string'))}, 'priors': {'decon_cellmarker': {'celltype_topk': List({'cell_type': Value('string'), 'proportion': Value('float64'), 'evidence_genes': List(Value('string')), 'evidence_gene_count': Value('int64')}), 'dominant_cell_type_optional': Value('string'), 'dominant_cell_type_proportion': Value('float64')}, 'pathway_multidb': {'dbs': List(Value('string')), 'top_gene_count': Value('int64'), 'top_k_merged': Value('int64'), 'min_hit_genes': Value('int64'), 'dominant_pathway_optional': Value('string'), 'merged_topk': List({'db': Value('string'), 'pathway': Value('string'), 'score': Value('float64'), 'merged_score': Value('float64'), 'hit_count': Value('int64'), 'enrichment_ratio': Value('float64'), 'precision': Value('float64')})}}, 'quality_flags': {'fallback_used': Value('bool'), 'low_evidence': Value('bool'), 'pathway_sparse': Value('bool'), 'organ_mismatch_risk': Value('bool')}}, 'structured_result': {'cell_type_composition': List({'cell_type': Value('string'), 'proportion': Value('float64'), 'evidence_genes': List(Value('string')), 'evidence_gene_count': Value('int64')}), 'pathway_evidence': {'reactome_top': List(Value('string')), 'gobp_top': List(Value('string'))}}, 'meta': {'source': Value('string')}}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
slice_id string | barcode string | species string | organ string | spot_key string | input_evidence dict | structured_result dict | meta dict |
|---|---|---|---|---|---|---|---|
GSE184510_GSM5591749 | GSE184510_GSM5591749_CCGCTTCGCGGTTAAC-1 | human | brain | GSE184510_GSM5591749 GSE184510_GSM5591749_CCGCTTCGCGGTTAAC-1 | {
"spot": {
"slice_id": "GSE184510_GSM5591749",
"barcode": "GSE184510_GSM5591749_CCGCTTCGCGGTTAAC-1",
"species": "human",
"organ": "brain",
"spot_key": "GSE184510_GSM5591749\tGSE184510_GSM5591749_CCGCTTCGCGGTTAAC-1",
"spatial_context": {
"spot_key": "GSE184510_GSM5591749\tGSE184510_GSM55... | {
"cell_type_composition": [
{
"cell_type": "Excitatory neuron",
"proportion": 0.3222073666605363,
"evidence_genes": [
"SYT1",
"NRGN"
],
"evidence_gene_count": 2
},
{
"cell_type": "Neuron",
"proportion": 0.3083282668466332,
"evidence_genes": ... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Human_Brain_10X_10272020_Visium_WholeTranscriptome | Human_Brain_10X_10272020_Visium_WholeTranscriptome_TCGCACCAGGAGGCAG-1 | human | brain | Human_Brain_10X_10272020_Visium_WholeTranscriptome Human_Brain_10X_10272020_Visium_WholeTranscriptome_TCGCACCAGGAGGCAG-1 | {
"spot": {
"slice_id": "Human_Brain_10X_10272020_Visium_WholeTranscriptome",
"barcode": "Human_Brain_10X_10272020_Visium_WholeTranscriptome_TCGCACCAGGAGGCAG-1",
"species": "human",
"organ": "brain",
"spot_key": "Human_Brain_10X_10272020_Visium_WholeTranscriptome\tHuman_Brain_10X_10272020_Visium_W... | {
"cell_type_composition": [
{
"cell_type": "Glial cell",
"proportion": 0.246955146999484,
"evidence_genes": [
"GFAP",
"SLC1A3"
],
"evidence_gene_count": 2
},
{
"cell_type": "Oligodendrocyte progenitor cell",
"proportion": 0.22144562088470396,
... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE197023_GSM5907080 | GSE197023_GSM5907080_TCAACCATGTTCGGGC-1 | human | skin | GSE197023_GSM5907080 GSE197023_GSM5907080_TCAACCATGTTCGGGC-1 | {
"spot": {
"slice_id": "GSE197023_GSM5907080",
"barcode": "GSE197023_GSM5907080_TCAACCATGTTCGGGC-1",
"species": "human",
"organ": "skin",
"spot_key": "GSE197023_GSM5907080\tGSE197023_GSM5907080_TCAACCATGTTCGGGC-1",
"spatial_context": {
"spot_key": "GSE197023_GSM5907080\tGSE197023_GSM590... | {
"cell_type_composition": [
{
"cell_type": "Epidermal cell",
"proportion": 0.3186027607387566,
"evidence_genes": [
"KRT14",
"KRT10",
"KRT1"
],
"evidence_gene_count": 3
},
{
"cell_type": "Basal cell",
"proportion": 0.22919592707131964,
... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE235672_GSM7507311 | GSE235672_GSM7507311_CCCGACCATAGTCCGC-1 | human | brain | GSE235672_GSM7507311 GSE235672_GSM7507311_CCCGACCATAGTCCGC-1 | {
"spot": {
"slice_id": "GSE235672_GSM7507311",
"barcode": "GSE235672_GSM7507311_CCCGACCATAGTCCGC-1",
"species": "human",
"organ": "brain",
"spot_key": "GSE235672_GSM7507311\tGSE235672_GSM7507311_CCCGACCATAGTCCGC-1",
"spatial_context": {
"spot_key": "GSE235672_GSM7507311\tGSE235672_GSM75... | {
"cell_type_composition": [
{
"cell_type": "Excitatory neuron",
"proportion": 0.40481907293188535,
"evidence_genes": [
"NRGN",
"SYT1"
],
"evidence_gene_count": 2
},
{
"cell_type": "Cancer cell",
"proportion": 0.18736222183546836,
"evidence_g... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE210616_GSM6433619 | GSE210616_GSM6433619_ACCCTATGCCATATCG-1 | human | breast | GSE210616_GSM6433619 GSE210616_GSM6433619_ACCCTATGCCATATCG-1 | {
"spot": {
"slice_id": "GSE210616_GSM6433619",
"barcode": "GSE210616_GSM6433619_ACCCTATGCCATATCG-1",
"species": "human",
"organ": "breast",
"spot_key": "GSE210616_GSM6433619\tGSE210616_GSM6433619_ACCCTATGCCATATCG-1",
"spatial_context": {
"spot_key": "GSE210616_GSM6433619\tGSE210616_GSM6... | {
"cell_type_composition": [
{
"cell_type": "Cancer-associated fibroblast",
"proportion": 0.49864285459258584,
"evidence_genes": [
"COL1A1",
"THY1"
],
"evidence_gene_count": 2
},
{
"cell_type": "Fibroblast",
"proportion": 0.3278253147933833,
... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Mouse_Brain_10X_06082023_Visium_Fixed_Frozen_Sagittal_Mouse_Brain | Mouse_Brain_10X_06082023_Visium_Fixed_Frozen_Sagittal_Mouse_Brain_GGCATAGGAGTATGAC-1 | mouse | brain | Mouse_Brain_10X_06082023_Visium_Fixed_Frozen_Sagittal_Mouse_Brain Mouse_Brain_10X_06082023_Visium_Fixed_Frozen_Sagittal_Mouse_Brain_GGCATAGGAGTATGAC-1 | {
"spot": {
"slice_id": "Mouse_Brain_10X_06082023_Visium_Fixed_Frozen_Sagittal_Mouse_Brain",
"barcode": "Mouse_Brain_10X_06082023_Visium_Fixed_Frozen_Sagittal_Mouse_Brain_GGCATAGGAGTATGAC-1",
"species": "mouse",
"organ": "brain",
"spot_key": "Mouse_Brain_10X_06082023_Visium_Fixed_Frozen_Sagittal_M... | {
"cell_type_composition": [
{
"cell_type": "Radial glial cell",
"proportion": 0.3518080253626904,
"evidence_genes": [
"ALDOC",
"SLC1A3",
"FABP7"
],
"evidence_gene_count": 3
},
{
"cell_type": "Neural stem cell",
"proportion": 0.222706109662... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE175540_GSM5924036 | GSE175540_GSM5924036_TACGAGAACTTCACGT-1 | human | kidney | GSE175540_GSM5924036 GSE175540_GSM5924036_TACGAGAACTTCACGT-1 | {
"spot": {
"slice_id": "GSE175540_GSM5924036",
"barcode": "GSE175540_GSM5924036_TACGAGAACTTCACGT-1",
"species": "human",
"organ": "kidney",
"spot_key": "GSE175540_GSM5924036\tGSE175540_GSM5924036_TACGAGAACTTCACGT-1",
"spatial_context": {
"spot_key": "GSE175540_GSM5924036\tGSE175540_GSM5... | {
"cell_type_composition": [
{
"cell_type": "Mesenchymal progenitor cell",
"proportion": 0.3351342539713866,
"evidence_genes": [
"VIM",
"COL1A2"
],
"evidence_gene_count": 2
},
{
"cell_type": "Interstitial cell",
"proportion": 0.24891767655496946,
... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Human_Prostate_10X_06092021_Visium_cancer | Human_Prostate_10X_06092021_Visium_cancer_AGCTCCATATATGTTC-1 | human | prostate | Human_Prostate_10X_06092021_Visium_cancer Human_Prostate_10X_06092021_Visium_cancer_AGCTCCATATATGTTC-1 | {
"spot": {
"slice_id": "Human_Prostate_10X_06092021_Visium_cancer",
"barcode": "Human_Prostate_10X_06092021_Visium_cancer_AGCTCCATATATGTTC-1",
"species": "human",
"organ": "prostate",
"spot_key": "Human_Prostate_10X_06092021_Visium_cancer\tHuman_Prostate_10X_06092021_Visium_cancer_AGCTCCATATATGTT... | {
"cell_type_composition": [
{
"cell_type": "Fibroblast",
"proportion": 1,
"evidence_genes": [
"FBLN1",
"PTGDS",
"DCN"
],
"evidence_gene_count": 3
}
],
"pathway_evidence": {
"reactome_top": [
"Smooth Muscle Contraction",
"Muscle Contrac... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Human_Brain_Louise_02152023_Visium_Br6471_ant | Human_Brain_Louise_02152023_Visium_Br6471_ant_GGGTTTAGGATAGGAT-1 | human | brain | Human_Brain_Louise_02152023_Visium_Br6471_ant Human_Brain_Louise_02152023_Visium_Br6471_ant_GGGTTTAGGATAGGAT-1 | {
"spot": {
"slice_id": "Human_Brain_Louise_02152023_Visium_Br6471_ant",
"barcode": "Human_Brain_Louise_02152023_Visium_Br6471_ant_GGGTTTAGGATAGGAT-1",
"species": "human",
"organ": "brain",
"spot_key": "Human_Brain_Louise_02152023_Visium_Br6471_ant\tHuman_Brain_Louise_02152023_Visium_Br6471_ant_GG... | {
"cell_type_composition": [
{
"cell_type": "Stem cell",
"proportion": 0.5273881292857266,
"evidence_genes": [
"CLU",
"GFAP",
"ALDOC"
],
"evidence_gene_count": 3
},
{
"cell_type": "Astrocyte",
"proportion": 0.14182589056059372,
"evide... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE243981_GSM7845914 | GSE243981_GSM7845914_AAACTCGTGATATAAG-1 | human | liver | GSE243981_GSM7845914 GSE243981_GSM7845914_AAACTCGTGATATAAG-1 | {
"spot": {
"slice_id": "GSE243981_GSM7845914",
"barcode": "GSE243981_GSM7845914_AAACTCGTGATATAAG-1",
"species": "human",
"organ": "liver",
"spot_key": "GSE243981_GSM7845914\tGSE243981_GSM7845914_AAACTCGTGATATAAG-1",
"spatial_context": {
"spot_key": "GSE243981_GSM7845914\tGSE243981_GSM78... | {
"cell_type_composition": [
{
"cell_type": "Hepatocyte",
"proportion": 0.3413147552714047,
"evidence_genes": [
"ALB",
"SERPINA1",
"FGB",
"APOA2",
"CYP3A4",
"APOE",
"TTR",
"TF",
"TAT",
"A2M"
],
"evidence_... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE223560_GSM6963525 | GSE223560_GSM6963525_AGGGTGGATAGTGCAT-1 | mouse | liver | GSE223560_GSM6963525 GSE223560_GSM6963525_AGGGTGGATAGTGCAT-1 | {
"spot": {
"slice_id": "GSE223560_GSM6963525",
"barcode": "GSE223560_GSM6963525_AGGGTGGATAGTGCAT-1",
"species": "mouse",
"organ": "liver",
"spot_key": "GSE223560_GSM6963525\tGSE223560_GSM6963525_AGGGTGGATAGTGCAT-1",
"spatial_context": {
"spot_key": "GSE223560_GSM6963525\tGSE223560_GSM69... | {
"cell_type_composition": [
{
"cell_type": "Hepatocyte",
"proportion": 0.5614868879270165,
"evidence_genes": [
"ALB",
"TTR",
"APOA2",
"APOE",
"APOA1",
"FGB",
"APOC1",
"SERPINA1C",
"FGG",
"GC"
],
"evidenc... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE197317_GSM5914544 | GSE197317_GSM5914544_CAATGGATCTCTACCA-1 | human | pancreas | GSE197317_GSM5914544 GSE197317_GSM5914544_CAATGGATCTCTACCA-1 | {
"spot": {
"slice_id": "GSE197317_GSM5914544",
"barcode": "GSE197317_GSM5914544_CAATGGATCTCTACCA-1",
"species": "human",
"organ": "pancreas",
"spot_key": "GSE197317_GSM5914544\tGSE197317_GSM5914544_CAATGGATCTCTACCA-1",
"spatial_context": {
"spot_key": "GSE197317_GSM5914544\tGSE197317_GS... | {
"cell_type_composition": [
{
"cell_type": "Acinar cell",
"proportion": 0.39256292155379124,
"evidence_genes": [
"CPA1",
"PRSS1",
"CTRB2",
"CTRC",
"CTRB1"
],
"evidence_gene_count": 5
},
{
"cell_type": "B cell",
"proportion"... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Human_Brain_Louise_02152023_Visium_Br2720_ant | Human_Brain_Louise_02152023_Visium_Br2720_ant_TTCTTATCCGCTGGGT-1 | human | brain | Human_Brain_Louise_02152023_Visium_Br2720_ant Human_Brain_Louise_02152023_Visium_Br2720_ant_TTCTTATCCGCTGGGT-1 | {
"spot": {
"slice_id": "Human_Brain_Louise_02152023_Visium_Br2720_ant",
"barcode": "Human_Brain_Louise_02152023_Visium_Br2720_ant_TTCTTATCCGCTGGGT-1",
"species": "human",
"organ": "brain",
"spot_key": "Human_Brain_Louise_02152023_Visium_Br2720_ant\tHuman_Brain_Louise_02152023_Visium_Br2720_ant_TT... | {
"cell_type_composition": [
{
"cell_type": "Neuron",
"proportion": 0.42420681102887803,
"evidence_genes": [
"THY1",
"MAP1B",
"NCDN"
],
"evidence_gene_count": 3
},
{
"cell_type": "Oligodendrocyte",
"proportion": 0.34531296493226216,
"... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain | Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain_CGAGTCGAGCAGCTAG-1 | mouse | brain | Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain_CGAGTCGAGCAGCTAG-1 | {
"spot": {
"slice_id": "Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain",
"barcode": "Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain_CGAGTCGAGCAGCTAG-1",
"species": "mouse",
"organ": "brain",
"spot_key": "Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain\tMouse_Brain_... | {
"cell_type_composition": [
{
"cell_type": "Olfactory ensheathing glia",
"proportion": 0.30275502650330943,
"evidence_genes": [
"ATP1A2",
"PLP1"
],
"evidence_gene_count": 2
},
{
"cell_type": "Myelinating oligodendrocyte",
"proportion": 0.268748070... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE192742_GSM5764416 | GSE192742_GSM5764416_TTGCTCCCATACCGGA-1 | mouse | liver | GSE192742_GSM5764416 GSE192742_GSM5764416_TTGCTCCCATACCGGA-1 | {
"spot": {
"slice_id": "GSE192742_GSM5764416",
"barcode": "GSE192742_GSM5764416_TTGCTCCCATACCGGA-1",
"species": "mouse",
"organ": "liver",
"spot_key": "GSE192742_GSM5764416\tGSE192742_GSM5764416_TTGCTCCCATACCGGA-1",
"spatial_context": {
"spot_key": "GSE192742_GSM5764416\tGSE192742_GSM57... | {
"cell_type_composition": [
{
"cell_type": "Hepatocellular cell",
"proportion": 0.5097177992221719,
"evidence_genes": [
"AHSG",
"CYP2E1"
],
"evidence_gene_count": 2
},
{
"cell_type": "Hepatocyte",
"proportion": 0.4902822007778282,
"evidence_... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Human_Heart_Kuppe_10082022_Visium_Visium_12_CK290 | Human_Heart_Kuppe_10082022_Visium_Visium_12_CK290_TCGAATATCCCGCAGG-1 | human | heart | Human_Heart_Kuppe_10082022_Visium_Visium_12_CK290 Human_Heart_Kuppe_10082022_Visium_Visium_12_CK290_TCGAATATCCCGCAGG-1 | {
"spot": {
"slice_id": "Human_Heart_Kuppe_10082022_Visium_Visium_12_CK290",
"barcode": "Human_Heart_Kuppe_10082022_Visium_Visium_12_CK290_TCGAATATCCCGCAGG-1",
"species": "human",
"organ": "heart",
"spot_key": "Human_Heart_Kuppe_10082022_Visium_Visium_12_CK290\tHuman_Heart_Kuppe_10082022_Visium_Vi... | {
"cell_type_composition": [
{
"cell_type": "Cardiomyocyte",
"proportion": 0.738669538021156,
"evidence_genes": [
"MYH7",
"TTN",
"MYL2",
"TNNI3",
"NPPA",
"ACTC1",
"MB",
"TNNT2",
"DCN"
],
"evidence_gene_count": 9
... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE203424_GSM6171787 | GSE203424_GSM6171787_CGTTGCCCGCGTGGGA-1 | mouse | brain | GSE203424_GSM6171787 GSE203424_GSM6171787_CGTTGCCCGCGTGGGA-1 | {
"spot": {
"slice_id": "GSE203424_GSM6171787",
"barcode": "GSE203424_GSM6171787_CGTTGCCCGCGTGGGA-1",
"species": "mouse",
"organ": "brain",
"spot_key": "GSE203424_GSM6171787\tGSE203424_GSM6171787_CGTTGCCCGCGTGGGA-1",
"spatial_context": {
"spot_key": "GSE203424_GSM6171787\tGSE203424_GSM61... | {
"cell_type_composition": [
{
"cell_type": "Myelinating oligodendrocyte",
"proportion": 0.3252797310886858,
"evidence_genes": [
"MBP",
"PLP1"
],
"evidence_gene_count": 2
},
{
"cell_type": "Quiescent neural stem cell",
"proportion": 0.2765253912315... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE254829_GSM8058255 | GSE254829_GSM8058255_TCTTACCGGAACTCGT-1 | human | pancreas | GSE254829_GSM8058255 GSE254829_GSM8058255_TCTTACCGGAACTCGT-1 | {
"spot": {
"slice_id": "GSE254829_GSM8058255",
"barcode": "GSE254829_GSM8058255_TCTTACCGGAACTCGT-1",
"species": "human",
"organ": "pancreas",
"spot_key": "GSE254829_GSM8058255\tGSE254829_GSM8058255_TCTTACCGGAACTCGT-1",
"spatial_context": {
"spot_key": "GSE254829_GSM8058255\tGSE254829_GS... | {
"cell_type_composition": [
{
"cell_type": "Acinar cell",
"proportion": 1,
"evidence_genes": [
"PRSS1",
"CTRC",
"CPA1",
"AMY2B",
"CTRB2",
"CTRB1"
],
"evidence_gene_count": 6
}
],
"pathway_evidence": {
"reactome_top": [
... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE214611_GSM6613086 | GSE214611_GSM6613086_CCGACGGGCATGAGGT-1 | mouse | heart | GSE214611_GSM6613086 GSE214611_GSM6613086_CCGACGGGCATGAGGT-1 | {
"spot": {
"slice_id": "GSE214611_GSM6613086",
"barcode": "GSE214611_GSM6613086_CCGACGGGCATGAGGT-1",
"species": "mouse",
"organ": "heart",
"spot_key": "GSE214611_GSM6613086\tGSE214611_GSM6613086_CCGACGGGCATGAGGT-1",
"spatial_context": {
"spot_key": "GSE214611_GSM6613086\tGSE214611_GSM66... | {
"cell_type_composition": [
{
"cell_type": "Embryonic myocardial cell",
"proportion": 0.5087535356039902,
"evidence_genes": [
"MYL2",
"TNNT2",
"TNNC1"
],
"evidence_gene_count": 3
},
{
"cell_type": "Cardiomyocyte",
"proportion": 0.491246464... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE197023_GSM5907088 | GSE197023_GSM5907088_TGGTTGGAGGATCCTG-1 | human | skin | GSE197023_GSM5907088 GSE197023_GSM5907088_TGGTTGGAGGATCCTG-1 | {
"spot": {
"slice_id": "GSE197023_GSM5907088",
"barcode": "GSE197023_GSM5907088_TGGTTGGAGGATCCTG-1",
"species": "human",
"organ": "skin",
"spot_key": "GSE197023_GSM5907088\tGSE197023_GSM5907088_TGGTTGGAGGATCCTG-1",
"spatial_context": {
"spot_key": "GSE197023_GSM5907088\tGSE197023_GSM590... | {
"cell_type_composition": [
{
"cell_type": "Fibroblast",
"proportion": 0.7461008773184116,
"evidence_genes": [
"DCN",
"CFD",
"MMP2",
"FBLN1",
"COL6A2",
"COL1A2",
"CXCL12"
],
"evidence_gene_count": 7
},
{
"cell_typ... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE202322_GSM6108351 | GSE202322_GSM6108351_TTCGACGGGAAGGGCG-1 | mouse | lung | GSE202322_GSM6108351 GSE202322_GSM6108351_TTCGACGGGAAGGGCG-1 | {
"spot": {
"slice_id": "GSE202322_GSM6108351",
"barcode": "GSE202322_GSM6108351_TTCGACGGGAAGGGCG-1",
"species": "mouse",
"organ": "lung",
"spot_key": "GSE202322_GSM6108351\tGSE202322_GSM6108351_TTCGACGGGAAGGGCG-1",
"spatial_context": {
"spot_key": "GSE202322_GSM6108351\tGSE202322_GSM610... | {
"cell_type_composition": [
{
"cell_type": "Alveolar type II (ATII) cell",
"proportion": 0.30351578009288177,
"evidence_genes": [
"SFTPC",
"LYZ2",
"CD74",
"SFTPB"
],
"evidence_gene_count": 4
},
{
"cell_type": "Alveolar pneumocyte Type II... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Human_Ovary_10X_03282022_Visium | Human_Ovary_10X_03282022_Visium_TGCAGCTACGTACTTC-1 | human | ovary | Human_Ovary_10X_03282022_Visium Human_Ovary_10X_03282022_Visium_TGCAGCTACGTACTTC-1 | {
"spot": {
"slice_id": "Human_Ovary_10X_03282022_Visium",
"barcode": "Human_Ovary_10X_03282022_Visium_TGCAGCTACGTACTTC-1",
"species": "human",
"organ": "ovary",
"spot_key": "Human_Ovary_10X_03282022_Visium\tHuman_Ovary_10X_03282022_Visium_TGCAGCTACGTACTTC-1",
"spatial_context": {
"spot_... | {
"cell_type_composition": [
{
"cell_type": "B cell",
"proportion": 0.43328221408933854,
"evidence_genes": [
"JCHAIN",
"IGKC"
],
"evidence_gene_count": 2
},
{
"cell_type": "Cancer cell",
"proportion": 0.3584575866128404,
"evidence_genes": [
... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE222410_GSM6922985 | GSE222410_GSM6922985_TAGCGTTGGGTCTTAC-1 | mouse | brain | GSE222410_GSM6922985 GSE222410_GSM6922985_TAGCGTTGGGTCTTAC-1 | {
"spot": {
"slice_id": "GSE222410_GSM6922985",
"barcode": "GSE222410_GSM6922985_TAGCGTTGGGTCTTAC-1",
"species": "mouse",
"organ": "brain",
"spot_key": "GSE222410_GSM6922985\tGSE222410_GSM6922985_TAGCGTTGGGTCTTAC-1",
"spatial_context": {
"spot_key": "GSE222410_GSM6922985\tGSE222410_GSM69... | {
"cell_type_composition": [
{
"cell_type": "Myelinating oligodendrocyte",
"proportion": 0.3291864150640807,
"evidence_genes": [
"MBP",
"PLP1",
"MAG"
],
"evidence_gene_count": 3
},
{
"cell_type": "Mature oligodendrocyte",
"proportion": 0.18... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Human_Brain_Louise_02152023_Visium_Br8492_mid | Human_Brain_Louise_02152023_Visium_Br8492_mid_TATTCCGAGCTGTTAT-1 | human | brain | Human_Brain_Louise_02152023_Visium_Br8492_mid Human_Brain_Louise_02152023_Visium_Br8492_mid_TATTCCGAGCTGTTAT-1 | {
"spot": {
"slice_id": "Human_Brain_Louise_02152023_Visium_Br8492_mid",
"barcode": "Human_Brain_Louise_02152023_Visium_Br8492_mid_TATTCCGAGCTGTTAT-1",
"species": "human",
"organ": "brain",
"spot_key": "Human_Brain_Louise_02152023_Visium_Br8492_mid\tHuman_Brain_Louise_02152023_Visium_Br8492_mid_TA... | {
"cell_type_composition": [
{
"cell_type": "Neuron",
"proportion": 0.38089845268015504,
"evidence_genes": [
"NRGN",
"SNAP25",
"UCHL1",
"MAP1B",
"ENO2",
"YWHAG",
"SYP",
"MBP",
"YWHAH"
],
"evidence_gene_count": 9
... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE254829_GSM8058247 | GSE254829_GSM8058247_GTAGCCAAACATGGGA-1 | human | pancreas | GSE254829_GSM8058247 GSE254829_GSM8058247_GTAGCCAAACATGGGA-1 | {
"spot": {
"slice_id": "GSE254829_GSM8058247",
"barcode": "GSE254829_GSM8058247_GTAGCCAAACATGGGA-1",
"species": "human",
"organ": "pancreas",
"spot_key": "GSE254829_GSM8058247\tGSE254829_GSM8058247_GTAGCCAAACATGGGA-1",
"spatial_context": {
"spot_key": "GSE254829_GSM8058247\tGSE254829_GS... | {
"cell_type_composition": [
{
"cell_type": "Beta cell(β cell)",
"proportion": 0.34167412756751475,
"evidence_genes": [
"CD99",
"INS"
],
"evidence_gene_count": 2
},
{
"cell_type": "Mesenchymal cell",
"proportion": 0.22296237898631607,
"eviden... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain | Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain_TAGCTCGCTCGATCCA-1 | mouse | brain | Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain_TAGCTCGCTCGATCCA-1 | {
"spot": {
"slice_id": "Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain",
"barcode": "Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain_TAGCTCGCTCGATCCA-1",
"species": "mouse",
"organ": "brain",
"spot_key": "Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain\tMouse_Brain_... | {
"cell_type_composition": [
{
"cell_type": "Neural stem cell",
"proportion": 0.26750165866175113,
"evidence_genes": [
"SNAP25",
"NDRG4",
"APOE",
"SNHG11"
],
"evidence_gene_count": 4
},
{
"cell_type": "Olfactory ensheathing glia",
"... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE221571_GSM6886501 | GSE221571_GSM6886501_CCTAACTAAGGCTCTA-1 | mouse | brain | GSE221571_GSM6886501 GSE221571_GSM6886501_CCTAACTAAGGCTCTA-1 | {
"spot": {
"slice_id": "GSE221571_GSM6886501",
"barcode": "GSE221571_GSM6886501_CCTAACTAAGGCTCTA-1",
"species": "mouse",
"organ": "brain",
"spot_key": "GSE221571_GSM6886501\tGSE221571_GSM6886501_CCTAACTAAGGCTCTA-1",
"spatial_context": {
"spot_key": "GSE221571_GSM6886501\tGSE221571_GSM68... | {
"cell_type_composition": [
{
"cell_type": "Olfactory ensheathing glia",
"proportion": 0.26642080941501795,
"evidence_genes": [
"PLP1",
"ATP1A2"
],
"evidence_gene_count": 2
},
{
"cell_type": "Myelinating oligodendrocyte",
"proportion": 0.251231043... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE148612_GSM5026148 | GSE148612_GSM5026148_AGCTTGATCTTAACTT-1 | mouse | brain | GSE148612_GSM5026148 GSE148612_GSM5026148_AGCTTGATCTTAACTT-1 | {
"spot": {
"slice_id": "GSE148612_GSM5026148",
"barcode": "GSE148612_GSM5026148_AGCTTGATCTTAACTT-1",
"species": "mouse",
"organ": "brain",
"spot_key": "GSE148612_GSM5026148\tGSE148612_GSM5026148_AGCTTGATCTTAACTT-1",
"spatial_context": {
"spot_key": "GSE148612_GSM5026148\tGSE148612_GSM50... | {
"cell_type_composition": [
{
"cell_type": "Myelinating oligodendrocyte",
"proportion": 0.28501534504982184,
"evidence_genes": [
"MBP",
"PLP1"
],
"evidence_gene_count": 2
},
{
"cell_type": "Mature oligodendrocyte",
"proportion": 0.2591092136497852... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE236424_GSM7536082 | GSE236424_GSM7536082_TCAAATTGTTGTGCCG-1 | mouse | liver | GSE236424_GSM7536082 GSE236424_GSM7536082_TCAAATTGTTGTGCCG-1 | {
"spot": {
"slice_id": "GSE236424_GSM7536082",
"barcode": "GSE236424_GSM7536082_TCAAATTGTTGTGCCG-1",
"species": "mouse",
"organ": "liver",
"spot_key": "GSE236424_GSM7536082\tGSE236424_GSM7536082_TCAAATTGTTGTGCCG-1",
"spatial_context": {
"spot_key": "GSE236424_GSM7536082\tGSE236424_GSM75... | {
"cell_type_composition": [
{
"cell_type": "Hepatocyte",
"proportion": 0.5168526419416759,
"evidence_genes": [
"ALB",
"APOA2",
"TTR",
"APOE",
"APOC1",
"SERPINA1C",
"APOA1",
"MUP3",
"CYP2E1",
"GC"
],
"evi... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE223559_GSM6963120 | GSE223559_GSM6963120_GCTTCCCGTAAGCTCC-1 | human | liver | GSE223559_GSM6963120 GSE223559_GSM6963120_GCTTCCCGTAAGCTCC-1 | {
"spot": {
"slice_id": "GSE223559_GSM6963120",
"barcode": "GSE223559_GSM6963120_GCTTCCCGTAAGCTCC-1",
"species": "human",
"organ": "liver",
"spot_key": "GSE223559_GSM6963120\tGSE223559_GSM6963120_GCTTCCCGTAAGCTCC-1",
"spatial_context": {
"spot_key": "GSE223559_GSM6963120\tGSE223559_GSM69... | {
"cell_type_composition": [
{
"cell_type": "Hepatocyte",
"proportion": 0.26692977232050813,
"evidence_genes": [
"ALB",
"SERPINA1",
"APOA2",
"FGB",
"TTR",
"APOE",
"TF"
],
"evidence_gene_count": 7
},
{
"cell_type": ... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE249729_GSM7962127 | GSE249729_GSM7962127_GTCGTACCATCTCGGG-1 | human | skin | GSE249729_GSM7962127 GSE249729_GSM7962127_GTCGTACCATCTCGGG-1 | {
"spot": {
"slice_id": "GSE249729_GSM7962127",
"barcode": "GSE249729_GSM7962127_GTCGTACCATCTCGGG-1",
"species": "human",
"organ": "skin",
"spot_key": "GSE249729_GSM7962127\tGSE249729_GSM7962127_GTCGTACCATCTCGGG-1",
"spatial_context": {
"spot_key": "GSE249729_GSM7962127\tGSE249729_GSM796... | {
"cell_type_composition": [
{
"cell_type": "Epidermal cell",
"proportion": 0.23883179014127734,
"evidence_genes": [
"KRT10",
"KRT1"
],
"evidence_gene_count": 2
},
{
"cell_type": "Plasma cell",
"proportion": 0.23868940648773418,
"evidence_gen... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
Human_Brain_Louise_02152023_Visium_Br8667_mid | Human_Brain_Louise_02152023_Visium_Br8667_mid_TCCCTCTTCTCAAGGG-1 | human | brain | Human_Brain_Louise_02152023_Visium_Br8667_mid Human_Brain_Louise_02152023_Visium_Br8667_mid_TCCCTCTTCTCAAGGG-1 | {
"spot": {
"slice_id": "Human_Brain_Louise_02152023_Visium_Br8667_mid",
"barcode": "Human_Brain_Louise_02152023_Visium_Br8667_mid_TCCCTCTTCTCAAGGG-1",
"species": "human",
"organ": "brain",
"spot_key": "Human_Brain_Louise_02152023_Visium_Br8667_mid\tHuman_Brain_Louise_02152023_Visium_Br8667_mid_TC... | {
"cell_type_composition": [
{
"cell_type": "Microglial cell",
"proportion": 0.4530076167102381,
"evidence_genes": [
"FTH1",
"FTL",
"COX8A",
"HSPA8",
"GNAS",
"COX6A1",
"CSTB",
"EEF1A1",
"NDUFA6",
"PEBP1"
],
... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
GSE227019_GSM7090086 | GSE227019_GSM7090086_GCTATCGCGGCGCAAC-1 | human | ovary | GSE227019_GSM7090086 GSE227019_GSM7090086_GCTATCGCGGCGCAAC-1 | {
"spot": {
"slice_id": "GSE227019_GSM7090086",
"barcode": "GSE227019_GSM7090086_GCTATCGCGGCGCAAC-1",
"species": "human",
"organ": "ovary",
"spot_key": "GSE227019_GSM7090086\tGSE227019_GSM7090086_GCTATCGCGGCGCAAC-1",
"spatial_context": {
"spot_key": "GSE227019_GSM7090086\tGSE227019_GSM70... | {
"cell_type_composition": [
{
"cell_type": "Stromal cell",
"proportion": 0.3669548639124295,
"evidence_genes": [
"COL1A2",
"LUM",
"DCN"
],
"evidence_gene_count": 3
},
{
"cell_type": "Fibroblast",
"proportion": 0.35399971622347187,
"e... | {
"source": "cm_only_sample + pathway_multidb_join"
} |
End of preview.
HistAgent tutorial and Agent Module data
This repository contains the public data used by the HistAgent tutorials and the configurable Agent Module.
Contents
HistAgent-data/
├── tutorials/ # data loaded by the five tutorial notebooks
├── chat/
│ ├── spots.jsonl # 9,150 spatial transcriptomics spot records
│ ├── config.example.env # Agent Module data-path configuration
│ └── slice_images/ # three RCC tissue images for the slice selector
└── manifest.json # byte sizes and SHA-256 checksums
The tutorial files include benchmark summaries, spot-level examples, plotted WSI panels, a pretrained ABMIL example and the atlas-retrieval examples.
The Agent Module JSONL contains ranked genes, cell-state summaries, pathways and spatial context for each public demo spot. The tissue images use public study accessions as filenames so the server can match them to their slices.
Download
from huggingface_hub import snapshot_download
root = snapshot_download(
"wli13/HistAgent-data",
repo_type="dataset",
)
Tutorial notebooks expect:
DATA_DIR = root / "tutorials"
For the Agent Module:
HISTAGENT_INPUT_JSONL=<root>/chat/spots.jsonl
HISTAGENT_ATLAS_SQLITE=
HISTAGENT_SLICE_IMAGE_DIRS=<root>/chat/slice_images
The public bundle does not require the optional full-atlas SQLite file.
Natural-language retrieval uses the 9,150 rich spot records in spots.jsonl.
Provenance and use
The bundle contains derived research outputs and examples assembled from the public studies cited by the HistAgent manuscript. Source-study terms continue to apply to the underlying data.
- Downloads last month
- -