A one sentence summary of purpose and methodology. Used for creating an overview tables.
Repository: openproblems-bio/task_spatial_segmentation
Provide a clear and concise description of your task, detailing the specific problem it aims to solve. Outline the input data types, the expected output, and any assumptions or constraints. Be sure to explain any terminology or concepts that are essential for understanding the task.
Explain the motivation behind your proposed task. Describe the biological or computational problem you aim to address and why it’s important. Discuss the current state of research in this area and any gaps or challenges that your task could help address. This section should convince readers of the significance and relevance of your task.
| Name | Roles | Github | Orcid | |||
|---|---|---|---|---|---|---|
| John Doe | author, maintainer | johndoe | johndoe | john@doe.me | 0000-0000-0000-0000 | johndoe |
flowchart TB
file_common_ist("<a href='https://github.com/openproblems-bio/task_spatial_segmentation#file-format-common-ist-dataset'>Common iST Dataset</a>")
comp_data_processor[/"<a href='https://github.com/openproblems-bio/task_spatial_segmentation#component-type-data-processor'>Data processor</a>"/]
file_spatial_unlabelled("<a href='https://github.com/openproblems-bio/task_spatial_segmentation#file-format-unlabelled'>Unlabelled</a>")
file_spatial_solution("<a href='https://github.com/openproblems-bio/task_spatial_segmentation#file-format-solution'>Solution</a>")
file_scrnaseq_reference("<a href='https://github.com/openproblems-bio/task_spatial_segmentation#file-format-scrna-seq-reference'>scRNA-seq Reference</a>")
comp_control_method[/"<a href='https://github.com/openproblems-bio/task_spatial_segmentation#component-type-control-method'>Control Method</a>"/]
comp_method[/"<a href='https://github.com/openproblems-bio/task_spatial_segmentation#component-type-method'>Method</a>"/]
comp_output_processor[/"<a href='https://github.com/openproblems-bio/task_spatial_segmentation#component-type-output-processor'>Output processor</a>"/]
comp_metric[/"<a href='https://github.com/openproblems-bio/task_spatial_segmentation#component-type-metric'>Metric</a>"/]
file_prediction("<a href='https://github.com/openproblems-bio/task_spatial_segmentation#file-format-predicted-data'>Predicted data</a>")
file_processed_prediction("<a href='https://github.com/openproblems-bio/task_spatial_segmentation#file-format-processed-prediction'>Processed prediction</a>")
file_score("<a href='https://github.com/openproblems-bio/task_spatial_segmentation#file-format-score'>Score</a>")
file_common_scrnaseq("<a href='https://github.com/openproblems-bio/task_spatial_segmentation#file-format-common-sc-dataset'>Common SC Dataset</a>")
file_common_ist---comp_data_processor
comp_data_processor-->file_spatial_unlabelled
comp_data_processor-->file_spatial_solution
comp_data_processor-->file_scrnaseq_reference
file_spatial_unlabelled---comp_control_method
file_spatial_unlabelled---comp_method
file_spatial_unlabelled---comp_output_processor
file_spatial_solution---comp_control_method
file_spatial_solution---comp_metric
comp_control_method-->file_prediction
comp_method-->file_prediction
comp_output_processor-->file_processed_prediction
comp_metric-->file_score
file_prediction---comp_output_processor
file_processed_prediction---comp_metric
file_common_scrnaseq---comp_data_processor
An unprocessed spatial imaging dataset stored as a zarr file.
Example file:
resources_test/common/2023_10x_mouse_brain_xenium_rep1/dataset.zarr
Description:
This dataset contains raw images, labels, points, shapes, and tables as output by a dataset loader.
Format:
SpatialData object
images: 'image', 'image_3D', 'he_image'
labels: 'cell_labels', 'nucleus_labels'
points: 'transcripts'
shapes: 'cell_boundaries', 'nucleus_boundaries'
tables: 'metadata'
coordinate_systems: 'global'
Data structure:
images
| Name | Description |
|---|---|
image |
The raw image data. |
image_3D |
(Optional) The raw 3D image data. |
he_image |
(Optional) H&E image data. |
labels
| Name | Description |
|---|---|
cell_labels |
(Optional) Cell segmentation labels. |
nucleus_labels |
(Optional) Cell segmentation labels. |
points
transcripts: Point cloud data of transcripts.
| Column | Type | Description |
|---|---|---|
x |
float |
x-coordinate of the point. |
y |
float |
y-coordinate of the point. |
z |
float |
(Optional) z-coordinate of the point. |
feature_name |
categorical |
Name of the feature. |
cell_id |
integer |
(Optional) Unique identifier of the cell. |
nucleus_id |
integer |
(Optional) Unique identifier of the nucleus. |
cell_type |
string |
(Optional) Cell type of the cell. |
qv |
float |
(Optional) Quality value of the point. |
transcript_id |
long |
Unique identifier of the transcript. |
overlaps_nucleus |
boolean |
(Optional) Whether the point overlaps with a nucleus. |
shapes
cell_boundaries: Cell boundaries.
| Column | Type | Description |
|---|---|---|
geometry |
object |
Geometry of the cell boundary. |
nucleus_boundaries: Nucleus boundaries.
| Column | Type | Description |
|---|---|---|
geometry |
object |
Geometry of the nucleus boundary. |
tables
metadata: Metadata of spatial dataset.
| Slot | Type | Description |
|---|---|---|
obs["cell_id"] |
string |
A unique identifier for the cell. |
var["gene_ids"] |
string |
Unique identifier for the gene. |
var["feature_types"] |
string |
Type of the feature. |
obsm["spatial"] |
double |
Spatial coordinates of the cell. |
uns["dataset_id"] |
string |
A unique identifier for the dataset. |
uns["dataset_name"] |
string |
A human-readable name for the dataset. |
uns["dataset_url"] |
string |
Link to the original source of the dataset. |
uns["dataset_reference"] |
string |
Bibtex reference of the paper in which the dataset was published. |
uns["dataset_summary"] |
string |
Short description of the dataset. |
uns["dataset_description"] |
string |
Long description of the dataset. |
uns["dataset_organism"] |
string |
The organism of the sample in the dataset. |
uns["segmentation_id"] |
string |
A unique identifier for the segmentation. |
coordinate_systems
| Name | Description |
|---|---|
global |
Coordinate system of the replicate. |
A data processor.
Arguments:
| Name | Type | Description |
|---|---|---|
--input_sp |
file |
An unprocessed spatial imaging dataset stored as a zarr file. |
--input_sc |
file |
An unprocessed dataset as output by a dataset loader. |
--output_spatial_unlabelled |
file |
(Output) Preprocessed spatial transcriptomics data without segmentation labels for method input. |
--output_spatial_solution |
file |
(Output) Ground truth segmentation labels and cell assignments for method evaluation. |
--output_scrnaseq_reference |
file |
(Output) A single-cell reference dataset, preprocessed for this benchmark. |
Preprocessed spatial transcriptomics data without segmentation labels for method input.
Example file:
resources_test/task_spatial_segmentation/mouse_brain_combined/spatial_unlabelled.zarr
Description:
This dataset contains preprocessed images and transcript point clouds for spatial transcriptomics data. Ground truth segmentation labels are intentionally excluded to prevent methods from cheating.
Format:
SpatialData object
images: 'morphology_mip'
points: 'transcripts'
tables: 'table'
coordinate_systems: 'global'
Data structure:
images
| Name | Description |
|---|---|
morphology_mip |
The raw morphology image (maximum intensity projection). |
points
transcripts: Point cloud data of transcripts.
| Column | Type | Description |
|---|---|---|
x |
float |
x-coordinate of the point. |
y |
float |
y-coordinate of the point. |
z |
float |
(Optional) z-coordinate of the point. |
feature_name |
categorical |
Name of the feature. |
qv |
float |
(Optional) Quality value of the point. |
transcript_id |
long |
Unique identifier of the transcript. |
overlaps_nucleus |
boolean |
(Optional) Whether the point overlaps with the nucleus (derived from morphology). |
tables
table: Metadata of spatial dataset.
| Slot | Type | Description |
|---|---|---|
var["feature_id"] |
string |
(Optional) Unique identifier for the feature, usually a ENSEMBL gene id. |
var["feature_name"] |
string |
A human-readable name for the feature, usually a gene symbol. |
uns["dataset_id"] |
string |
A unique identifier for the dataset. |
uns["dataset_name"] |
string |
A human-readable name for the dataset. |
uns["dataset_url"] |
string |
Link to the original source of the dataset. |
uns["dataset_reference"] |
string |
Bibtex reference of the paper in which the dataset was published. |
uns["dataset_summary"] |
string |
Short description of the dataset. |
uns["dataset_description"] |
string |
Long description of the dataset. |
uns["dataset_organism"] |
string |
The organism of the sample in the dataset. |
uns["orig_dataset_id"] |
string |
The identifier of the original dataset from which this dataset was derived (if applicable). |
coordinate_systems
| Name | Description |
|---|---|
global |
Coordinate system of the replicate. |
Ground truth segmentation labels and cell assignments for method evaluation.
Example file:
resources_test/task_spatial_segmentation/mouse_brain_combined/spatial_solution.zarr
Description:
This dataset contains the ground truth cell and nucleus segmentation labels, cell boundaries, and a reference table matching each cell to its label region.
Format:
SpatialData object
labels: 'cell_labels', 'nucleus_labels'
points: 'transcripts'
shapes: 'cell_boundaries', 'nucleus_boundaries'
tables: 'table'
Data structure:
labels
| Name | Description |
|---|---|
cell_labels |
Ground truth cell segmentation labels. |
nucleus_labels |
(Optional) Ground truth nucleus segmentation labels. |
points
transcripts: Point cloud data of transcripts with ground truth cell
assignments.
| Column | Type | Description |
|---|---|---|
x |
float |
x-coordinate of the point. |
y |
float |
y-coordinate of the point. |
z |
float |
(Optional) z-coordinate of the point. |
feature_name |
categorical |
Name of the feature. |
cell_id |
integer |
Ground truth cell assignment (0 = background). |
transcript_id |
long |
Unique identifier of the transcript. |
shapes
cell_boundaries: Ground truth cell boundary shapes.
| Column | Type | Description |
|---|---|---|
geometry |
object |
Geometry of the cell boundary. |
nucleus_boundaries: Ground truth nucleus boundary shapes.
| Column | Type | Description |
|---|---|---|
geometry |
object |
Geometry of the nucleus boundary. |
tables
table: Reference cell metadata table.
| Slot | Type | Description |
|---|---|---|
obs["cell_id"] |
integer |
Unique cell identifier, matching instance IDs in the label images. |
obs["region"] |
string |
Name of the label image this cell belongs to (e.g. ‘cell_labels’). |
obs["cell_area"] |
double |
(Optional) Area of the cell in pixels. |
obs["transcript_counts"] |
integer |
(Optional) Total number of transcripts assigned to this cell. |
var["feature_id"] |
string |
(Optional) Unique identifier for the feature, usually a ENSEMBL gene id. |
var["feature_name"] |
string |
A human-readable name for the feature, usually a gene symbol. |
uns["dataset_id"] |
string |
A unique identifier for the dataset. |
uns["orig_dataset_id"] |
string |
The identifier of the original dataset from which this dataset was derived (if applicable). |
A single-cell reference dataset, preprocessed for this benchmark.
Example file:
resources_test/task_spatial_segmentation/mouse_brain_combined/scrnaseq_reference.h5ad
Description:
This dataset contains preprocessed counts and metadata for single-cell RNA-seq data.
Format:
AnnData object
obs: 'cell_type'
var: 'feature_id', 'feature_name', 'hvg'
obsm: 'X_pca'
obsp: 'knn_distances', 'knn_connectivities'
varm: 'pca_loadings'
layers: 'counts', 'normalized', 'normalized_log', 'normalized_log_scaled'
uns: 'dataset_id', 'dataset_name', 'dataset_url', 'dataset_reference', 'dataset_summary', 'dataset_description', 'dataset_organism'
Data structure:
| Slot | Type | Description |
|---|---|---|
obs["cell_type"] |
string |
Classification of the cell type based on its characteristics and function within the tissue or organism. |
var["feature_id"] |
string |
(Optional) Unique identifier for the feature, usually a ENSEMBL gene id. |
var["feature_name"] |
string |
A human-readable name for the feature, usually a gene symbol. |
var["hvg"] |
boolean |
Whether or not the feature is considered to be a ‘highly variable gene’. |
obsm["X_pca"] |
double |
The resulting PCA embedding. |
obsp["knn_distances"] |
double |
K nearest neighbors distance matrix. |
obsp["knn_connectivities"] |
double |
K nearest neighbors connectivities matrix. |
varm["pca_loadings"] |
double |
The PCA loadings matrix. |
layers["counts"] |
integer |
Raw counts. |
layers["normalized"] |
double |
Normalized expression values. |
layers["normalized_log"] |
double |
Log1p normalized expression values. |
layers["normalized_log_scaled"] |
double |
Log1p normalized expression values scaled to unit variance and zero mean. |
uns["dataset_id"] |
string |
A unique identifier for the dataset. This is different from the obs.dataset_id field, which is the identifier for the dataset from which the cell data is derived. |
uns["dataset_name"] |
string |
A human-readable name for the dataset. |
uns["dataset_url"] |
string |
(Optional) Link to the original source of the dataset. |
uns["dataset_reference"] |
string |
(Optional) Bibtex reference of the paper in which the dataset was published. |
uns["dataset_summary"] |
string |
Short description of the dataset. |
uns["dataset_description"] |
string |
Long description of the dataset. |
uns["dataset_organism"] |
string |
(Optional) The organism of the sample in the dataset. |
Quality control methods for verifying the pipeline.
Arguments:
| Name | Type | Description |
|---|---|---|
--input |
file |
Preprocessed spatial transcriptomics data without segmentation labels for method input. |
--input_solution |
file |
Ground truth segmentation labels and cell assignments for method evaluation. |
--output |
file |
(Output) A predicted dataset as output by a method. |
A method.
Arguments:
| Name | Type | Description |
|---|---|---|
--input |
file |
Preprocessed spatial transcriptomics data without segmentation labels for method input. |
--output |
file |
(Output) A predicted dataset as output by a method. |
An output processor for the prediction.
Arguments:
| Name | Type | Description |
|---|---|---|
--input_prediction |
file |
A predicted dataset as output by a method. |
--input_spatial_unlabelled |
file |
Preprocessed spatial transcriptomics data without segmentation labels for method input. |
--output |
file |
(Output) A processed predicted dataset, ready to be used as input for the evaluation. |
A task template metric.
Arguments:
| Name | Type | Description |
|---|---|---|
--input_prediction |
file |
A processed predicted dataset, ready to be used as input for the evaluation. |
--input_solution |
file |
Ground truth segmentation labels and cell assignments for method evaluation. |
--output |
file |
(Output) File indicating the score of a metric. |
A predicted dataset as output by a method.
Example file:
resources_test/task_spatial_segmentation/mouse_brain_combined/prediction.zarr
Format:
SpatialData object
labels: 'segmentation'
tables: 'table'
Data structure:
labels
| Name | Description |
|---|---|
segmentation |
Segmentation of the data. |
tables
table: AnnData table.
| Slot | Type | Description |
|---|---|---|
uns["dataset_id"] |
string |
A unique identifier for the dataset. |
uns["method_id"] |
string |
A unique identifier for the method. |
A processed predicted dataset, ready to be used as input for the evaluation.
Example file:
resources_test/task_spatial_segmentation/mouse_brain_combined/processed_prediction.zarr
Format:
SpatialData object
labels: 'segmentation'
tables: 'table'
Data structure:
labels
| Name | Description |
|---|---|
segmentation |
Segmentation of the data. |
tables
table: AnnData table.
| Slot | Type | Description |
|---|---|---|
obs["cell_id"] |
string |
Cell ID. |
obs["region"] |
string |
Region. |
var["feature_id"] |
string |
(Optional) Unique identifier for the feature, usually a ENSEMBL gene id. |
var["feature_name"] |
string |
A human-readable name for the feature, usually a gene symbol. |
var["hvg"] |
boolean |
Whether or not the feature is considered to be a ‘highly variable gene’. |
layers["counts"] |
integer |
Raw counts. |
layers["normalized"] |
double |
Normalized expression values. |
layers["normalized_log"] |
double |
Log1p normalized expression values. |
layers["normalized_log_scaled"] |
double |
Log1p normalized expression values scaled to unit variance and zero mean. |
uns["dataset_id"] |
string |
A unique identifier for the dataset. |
uns["method_id"] |
string |
A unique identifier for the method. |
File indicating the score of a metric.
Example file:
resources_test/task_spatial_segmentation/mouse_brain_combined/score.h5ad
Format:
AnnData object
uns: 'dataset_id', 'normalization_id', 'method_id', 'metric_ids', 'metric_values'
Data structure:
| Slot | Type | Description |
|---|---|---|
uns["dataset_id"] |
string |
A unique identifier for the dataset. |
uns["normalization_id"] |
string |
Which normalization was used. |
uns["method_id"] |
string |
A unique identifier for the method. |
uns["metric_ids"] |
string |
One or more unique metric identifiers. |
uns["metric_values"] |
double |
The metric values obtained for the given prediction. Must be of same length as ‘metric_ids’. |
An unprocessed dataset as output by a dataset loader.
Example file:
resources_test/common/2023_yao_mouse_brain_scrnaseq_10xv2/dataset.h5ad
Description:
This dataset contains raw counts and metadata as output by a dataset loader.
The format of this file is mainly derived from the CELLxGENE schema v4.0.0.
Format:
AnnData object
obs: 'cell_type', 'cell_type_level2', 'cell_type_level3', 'cell_type_level4', 'dataset_id', 'assay', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage', 'development_stage_ontology_term_id', 'disease', 'disease_ontology_term_id', 'donor_id', 'is_primary_data', 'organism', 'organism_ontology_term_id', 'self_reported_ethnicity', 'self_reported_ethnicity_ontology_term_id', 'sex', 'sex_ontology_term_id', 'suspension_type', 'tissue', 'tissue_ontology_term_id', 'tissue_general', 'tissue_general_ontology_term_id', 'batch', 'soma_joinid'
var: 'feature_id', 'feature_name', 'soma_joinid', 'hvg', 'hvg_score'
obsm: 'X_pca'
obsp: 'knn_distances', 'knn_connectivities'
varm: 'pca_loadings'
layers: 'counts', 'normalized'
uns: 'dataset_id', 'dataset_name', 'dataset_url', 'dataset_reference', 'dataset_summary', 'dataset_description', 'dataset_organism'
Data structure:
| Slot | Type | Description |
|---|---|---|
obs["cell_type"] |
string |
Classification of the cell type based on its characteristics and function within the tissue or organism. |
obs["cell_type_level2"] |
string |
(Optional) Classification of the cell type based on its characteristics and function within the tissue or organism. |
obs["cell_type_level3"] |
string |
(Optional) Classification of the cell type based on its characteristics and function within the tissue or organism. |
obs["cell_type_level4"] |
string |
(Optional) Classification of the cell type based on its characteristics and function within the tissue or organism. |
obs["dataset_id"] |
string |
(Optional) Identifier for the dataset from which the cell data is derived, useful for tracking and referencing purposes. |
obs["assay"] |
string |
(Optional) Type of assay used to generate the cell data, indicating the methodology or technique employed. |
obs["assay_ontology_term_id"] |
string |
(Optional) Experimental Factor Ontology (EFO:) term identifier for the assay, providing a standardized reference to the assay type. |
obs["cell_type_ontology_term_id"] |
string |
(Optional) Cell Ontology (CL:) term identifier for the cell type, offering a standardized reference to the specific cell classification. |
obs["development_stage"] |
string |
(Optional) Stage of development of the organism or tissue from which the cell is derived, indicating its maturity or developmental phase. |
obs["development_stage_ontology_term_id"] |
string |
(Optional) Ontology term identifier for the developmental stage, providing a standardized reference to the organism’s developmental phase. If the organism is human (organism_ontology_term_id == 'NCBITaxon:9606'), then the Human Developmental Stages (HsapDv:) ontology is used. If the organism is mouse (organism_ontology_term_id == 'NCBITaxon:10090'), then the Mouse Developmental Stages (MmusDv:) ontology is used. Otherwise, the Uberon (UBERON:) ontology is used. |
obs["disease"] |
string |
(Optional) Information on any disease or pathological condition associated with the cell or donor. |
obs["disease_ontology_term_id"] |
string |
(Optional) Ontology term identifier for the disease, enabling standardized disease classification and referencing. Must be a term from the Mondo Disease Ontology (MONDO:) ontology term, or PATO:0000461 from the Phenotype And Trait Ontology (PATO:). |
obs["donor_id"] |
string |
(Optional) Identifier for the donor from whom the cell sample is obtained. |
obs["is_primary_data"] |
boolean |
(Optional) Indicates whether the data is primary (directly obtained from experiments) or has been computationally derived from other primary data. |
obs["organism"] |
string |
(Optional) Organism from which the cell sample is obtained. |
obs["organism_ontology_term_id"] |
string |
(Optional) Ontology term identifier for the organism, providing a standardized reference for the organism. Must be a term from the NCBI Taxonomy Ontology (NCBITaxon:) which is a child of NCBITaxon:33208. |
obs["self_reported_ethnicity"] |
string |
(Optional) Ethnicity of the donor as self-reported, relevant for studies considering genetic diversity and population-specific traits. |
obs["self_reported_ethnicity_ontology_term_id"] |
string |
(Optional) Ontology term identifier for the self-reported ethnicity, providing a standardized reference for ethnic classifications. If the organism is human (organism_ontology_term_id == 'NCBITaxon:9606'), then the Human Ancestry Ontology (HANCESTRO:) is used. |
obs["sex"] |
string |
(Optional) Biological sex of the donor or source organism, crucial for studies involving sex-specific traits or conditions. |
obs["sex_ontology_term_id"] |
string |
(Optional) Ontology term identifier for the biological sex, ensuring standardized classification of sex. Only PATO:0000383, PATO:0000384 and PATO:0001340 are allowed. |
obs["suspension_type"] |
string |
(Optional) Type of suspension or medium in which the cells were stored or processed, important for understanding cell handling and conditions. |
obs["tissue"] |
string |
(Optional) Specific tissue from which the cells were derived, key for context and specificity in cell studies. |
obs["tissue_ontology_term_id"] |
string |
(Optional) Ontology term identifier for the tissue, providing a standardized reference for the tissue type. For organoid or tissue samples, the Uber-anatomy ontology (UBERON:) is used. The term ids must be a child term of UBERON:0001062 (anatomical entity). For cell cultures, the Cell Ontology (CL:) is used. The term ids cannot be CL:0000255, CL:0000257 or CL:0000548. |
obs["tissue_general"] |
string |
(Optional) General category or classification of the tissue, useful for broader grouping and comparison of cell data. |
obs["tissue_general_ontology_term_id"] |
string |
(Optional) Ontology term identifier for the general tissue category, aiding in standardizing and grouping tissue types. For organoid or tissue samples, the Uber-anatomy ontology (UBERON:) is used. The term ids must be a child term of UBERON:0001062 (anatomical entity). For cell cultures, the Cell Ontology (CL:) is used. The term ids cannot be CL:0000255, CL:0000257 or CL:0000548. |
obs["batch"] |
string |
(Optional) A batch identifier. This label is very context-dependent and may be a combination of the tissue, assay, donor, etc. |
obs["soma_joinid"] |
integer |
(Optional) If the dataset was retrieved from CELLxGENE census, this is a unique identifier for the cell. |
var["feature_id"] |
string |
(Optional) Unique identifier for the feature, usually a ENSEMBL gene id. |
var["feature_name"] |
string |
A human-readable name for the feature, usually a gene symbol. |
var["soma_joinid"] |
integer |
(Optional) If the dataset was retrieved from CELLxGENE census, this is a unique identifier for the feature. |
var["hvg"] |
boolean |
Whether or not the feature is considered to be a ‘highly variable gene’. |
var["hvg_score"] |
double |
A score for the feature indicating how highly variable it is. |
obsm["X_pca"] |
double |
The resulting PCA embedding. |
obsp["knn_distances"] |
double |
K nearest neighbors distance matrix. |
obsp["knn_connectivities"] |
double |
K nearest neighbors connectivities matrix. |
varm["pca_loadings"] |
double |
The PCA loadings matrix. |
layers["counts"] |
integer |
Raw counts. |
layers["normalized"] |
integer |
Normalized expression values. |
uns["dataset_id"] |
string |
A unique identifier for the dataset. This is different from the obs.dataset_id field, which is the identifier for the dataset from which the cell data is derived. |
uns["dataset_name"] |
string |
A human-readable name for the dataset. |
uns["dataset_url"] |
string |
(Optional) Link to the original source of the dataset. |
uns["dataset_reference"] |
string |
(Optional) Bibtex reference of the paper in which the dataset was published. |
uns["dataset_summary"] |
string |
Short description of the dataset. |
uns["dataset_description"] |
string |
Long description of the dataset. |
uns["dataset_organism"] |
string |
(Optional) The organism of the sample in the dataset. |