Benchmarking approaches for preprocessing imaging-based spatial transcriptomics
Repository: openproblems-bio/task_ist_preprocessing
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 |
|---|---|
| Louis Kümmerle | author, maintainer |
| Malte D. Luecken | author |
| Daniel Strobl | author |
| Robrecht Cannoodt | author |
flowchart TB
file_common_ist("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-common-ist-dataset'>Common iST Dataset</a>")
comp_data_preprocessor[/"<a href='https://github.com/openproblems-bio/task_ist_preprocessing#component-type-data-preprocessor'>Data preprocessor</a>"/]
file_raw_ist("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-raw-ist-dataset'>Raw iST Dataset</a>")
file_scrnaseq_reference("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-scrna-seq-reference'>scRNA-seq Reference</a>")
comp_method_segmentation[/"<a href='https://github.com/openproblems-bio/task_ist_preprocessing#component-type-segmentation'>Segmentation</a>"/]
comp_method_transcript_assignment[/"<a href='https://github.com/openproblems-bio/task_ist_preprocessing#component-type-assignment'>Assignment</a>"/]
comp_method_cell_type_annotation[/"<a href='https://github.com/openproblems-bio/task_ist_preprocessing#component-type-cell-type-annotation'>Cell Type Annotation</a>"/]
comp_method_expression_correction[/"<a href='https://github.com/openproblems-bio/task_ist_preprocessing#component-type-expression-correction'>Expression correction</a>"/]
comp_metric[/"<a href='https://github.com/openproblems-bio/task_ist_preprocessing#component-type-metric'>Metric</a>"/]
file_segmentation("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-segmentation'>Segmentation</a>")
file_transcript_assignments("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-transcript-assignment'>Transcript Assignment</a>")
file_spatial_with_cell_types("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-spatial-with-cell-types'>Spatial with Cell Types</a>")
file_spatial_corrected_counts("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-spatial-corrected'>Spatial Corrected</a>")
file_score("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-score'>Score</a>")
comp_method_calculate_cell_volume[/"<a href='https://github.com/openproblems-bio/task_ist_preprocessing#component-type-calculate-cell-volume'>Calculate Cell Volume</a>"/]
comp_method_count_aggregation[/"<a href='https://github.com/openproblems-bio/task_ist_preprocessing#component-type-count-aggregation'>Count Aggregation</a>"/]
file_cell_volumes("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-cell-volumes'>Cell Volumes</a>")
file_spatial_aggregated_counts("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-aggregated-counts'>Aggregated Counts</a>")
comp_method_normalization[/"<a href='https://github.com/openproblems-bio/task_ist_preprocessing#component-type-normalization'>Normalization</a>"/]
comp_method_qc_filter[/"<a href='https://github.com/openproblems-bio/task_ist_preprocessing#component-type-qc-filter'>QC Filter</a>"/]
file_spatial_normalized_counts("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-spatial-normalized'>Spatial Normalized</a>")
file_spatial_qc_col("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-qc-columns'>QC Columns</a>")
file_common_scrnaseq("<a href='https://github.com/openproblems-bio/task_ist_preprocessing#file-format-common-sc-dataset'>Common SC Dataset</a>")
file_common_ist---comp_data_preprocessor
comp_data_preprocessor-->file_raw_ist
comp_data_preprocessor-->file_scrnaseq_reference
file_raw_ist---comp_method_segmentation
file_raw_ist---comp_method_transcript_assignment
file_scrnaseq_reference-.-comp_method_transcript_assignment
file_scrnaseq_reference-.-comp_method_cell_type_annotation
file_scrnaseq_reference-.-comp_method_expression_correction
file_scrnaseq_reference---comp_metric
comp_method_segmentation-->file_segmentation
comp_method_transcript_assignment-->file_transcript_assignments
comp_method_cell_type_annotation-->file_spatial_with_cell_types
comp_method_expression_correction-->file_spatial_corrected_counts
comp_metric-->file_score
file_segmentation-.-comp_method_transcript_assignment
file_transcript_assignments-.-comp_method_cell_type_annotation
file_transcript_assignments---comp_metric
file_transcript_assignments---comp_method_calculate_cell_volume
file_transcript_assignments---comp_method_count_aggregation
file_spatial_with_cell_types---comp_method_expression_correction
file_spatial_corrected_counts---comp_metric
comp_method_calculate_cell_volume-->file_cell_volumes
comp_method_count_aggregation-->file_spatial_aggregated_counts
file_cell_volumes-.-comp_method_normalization
file_spatial_aggregated_counts---comp_method_normalization
file_spatial_aggregated_counts---comp_method_qc_filter
comp_method_normalization-->file_spatial_normalized_counts
comp_method_qc_filter-->file_spatial_qc_col
file_spatial_normalized_counts---comp_method_cell_type_annotation
file_spatial_qc_col---comp_metric
file_common_scrnaseq---comp_data_preprocessor
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:
Data structure:
Preprocess a common dataset for the benchmark.
Arguments:
| Name | Type | Description |
|---|---|---|
--input_ist |
file |
An unprocessed spatial imaging dataset stored as a zarr file. |
--input_scrnaseq |
file |
An unprocessed dataset as output by a dataset loader. |
--output_ist |
file |
(Output) A spatial transcriptomics dataset, preprocessed for this benchmark. |
--output_scrnaseq |
file |
(Output) A single-cell reference dataset, preprocessed for this benchmark. |
A spatial transcriptomics dataset, preprocessed for this benchmark.
Example file:
resources_test/task_ist_preprocessing/mouse_brain_combined/raw_ist.zarr
Description:
This dataset contains preprocessed images, labels, points, shapes, and tables for spatial transcriptomics data.
Format:
Data structure:
A single-cell reference dataset, preprocessed for this benchmark.
Example file:
resources_test/task_ist_preprocessing/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', '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. |
A segmentation of the spatial data into cells
Arguments:
| Name | Type | Description |
|---|---|---|
--input |
file |
A spatial transcriptomics dataset, preprocessed for this benchmark. |
--output |
file |
(Output) A segmentation of a spatial transcriptomics dataset. |
Assigning transcripts to cells
Arguments:
| Name | Type | Description |
|---|---|---|
--input_ist |
file |
A spatial transcriptomics dataset, preprocessed for this benchmark. |
--input_segmentation |
file |
(Optional) A segmentation of a spatial transcriptomics dataset. |
--input_scrnaseq |
file |
(Optional) A single-cell reference dataset, preprocessed for this benchmark. |
--output |
file |
(Output) A spatial transcriptomics dataset with assigned transcripts. |
Annotating cell types in spatial data
Arguments:
| Name | Type | Description |
|---|---|---|
--input_spatial_normalized_counts |
file |
Normalized counts. |
--input_transcript_assignments |
file |
(Optional) A spatial transcriptomics dataset with assigned transcripts. |
--input_scrnaseq_reference |
file |
(Optional) A single-cell reference dataset, preprocessed for this benchmark. |
--celltype_key |
string |
(Optional) NA. Default: cell_type. |
--output |
file |
(Output) Normalized counts with cell type annotations. |
Correcting expression levels in spatial data
Arguments:
| Name | Type | Description |
|---|---|---|
--input_spatial_with_cell_types |
file |
Normalized counts with cell type annotations. |
--input_scrnaseq_reference |
file |
(Optional) A single-cell reference dataset, preprocessed for this benchmark. |
--output |
file |
(Output) Corrected spatial data counts with cell type annotations. |
A metric for evaluating iST preprocessing methods
Arguments:
| Name | Type | Description |
|---|---|---|
--input |
file |
Corrected spatial data counts with cell type annotations. |
--input_qc_col |
file |
QC columns for spatial data. |
--input_sc |
file |
A single-cell reference dataset, preprocessed for this benchmark. |
--input_transcript_assignments |
file |
A spatial transcriptomics dataset with assigned transcripts. |
--output |
file |
(Output) Metric score file. |
A segmentation of a spatial transcriptomics dataset
Example file:
resources_test/task_ist_preprocessing/mouse_brain_combined/segmentation.zarr
Description:
This dataset contains a segmentation of the spatial transcriptomics data.
Format:
Data structure:
A spatial transcriptomics dataset with assigned transcripts
Example file:
resources_test/task_ist_preprocessing/mouse_brain_combined/transcript_assignments.zarr
Description:
This dataset contains the spatial transcriptomics data with assigned transcripts.
Format:
Data structure:
Normalized counts with cell type annotations
Example file:
resources_test/task_ist_preprocessing/mouse_brain_combined/spatial_with_cell_types.h5ad
Description:
This file contains the normalized counts of the spatial transcriptomics data and cell type annotations.
Format:
AnnData object
obs: 'cell_id', 'centroid_x', 'centroid_y', 'centroid_z', 'n_counts', 'n_genes', 'volume', 'cell_type'
var: 'gene_name', 'n_counts', 'n_cells'
layers: 'counts', 'normalized'
Data structure:
| Slot | Type | Description |
|---|---|---|
obs["cell_id"] |
string |
Unique identifier for the cell (from assignment step). |
obs["centroid_x"] |
string |
X coordinate of the cell. |
obs["centroid_y"] |
string |
Y coordinate of the cell. |
obs["centroid_z"] |
string |
(Optional) Z coordinate of the cell. |
obs["n_counts"] |
string |
Number of counts in the cell. |
obs["n_genes"] |
string |
Number of genes in the cell. |
obs["volume"] |
string |
Volume of the cell. |
obs["cell_type"] |
string |
Cell type of the cell. |
var["gene_name"] |
string |
Name of the gene. |
var["n_counts"] |
string |
Number of counts of the gene. |
var["n_cells"] |
string |
Number of cells expressing the gene. |
layers["counts"] |
integer |
Raw counts. |
layers["normalized"] |
integer |
Normalized counts. |
Corrected spatial data counts with cell type annotations
Example file:
resources_test/task_ist_preprocessing/mouse_brain_combined/spatial_corrected_counts.h5ad
Description:
This file contains the corrected counts of the spatial transcriptomics data and cell type annotations.
Format:
AnnData object
obs: 'cell_id', 'centroid_x', 'centroid_y', 'centroid_z', 'n_counts', 'n_genes', 'volume', 'cell_type'
var: 'gene_name', 'n_counts', 'n_cells'
layers: 'counts', 'normalized', 'normalized', 'normalized_uncorrected'
Data structure:
| Slot | Type | Description |
|---|---|---|
obs["cell_id"] |
string |
Unique identifier for the cell (from assignment step). |
obs["centroid_x"] |
string |
X coordinate of the cell. |
obs["centroid_y"] |
string |
Y coordinate of the cell. |
obs["centroid_z"] |
string |
(Optional) Z coordinate of the cell. |
obs["n_counts"] |
string |
Number of counts in the cell. |
obs["n_genes"] |
string |
Number of genes in the cell. |
obs["volume"] |
string |
Volume of the cell. |
obs["cell_type"] |
string |
Cell type of the cell. |
var["gene_name"] |
string |
Name of the gene. |
var["n_counts"] |
string |
Number of counts of the gene. |
var["n_cells"] |
string |
Number of cells expressing the gene. |
layers["counts"] |
integer |
Raw counts. |
layers["normalized"] |
integer |
Normalized counts. |
layers["normalized"] |
double |
(Optional) Corrected normalized expression. |
layers["normalized_uncorrected"] |
double |
(Optional) Uncorrected normalized expression. |
Metric score file
Example file:
resources_test/task_ist_preprocessing/mouse_brain_combined/score.h5ad
Format:
AnnData object
uns: 'metric_ids', 'metric_values'
Data structure:
| Slot | Type | Description |
|---|---|---|
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’. |
Calculate the volume of cells
Arguments:
| Name | Type | Description |
|---|---|---|
--input |
file |
A spatial transcriptomics dataset with assigned transcripts. |
--output |
file |
(Output) An obs column of cell volumes calculated from spatial transcriptomics data. |
Aggregating counts of transcripts within cells
Arguments:
| Name | Type | Description |
|---|---|---|
--input |
file |
A spatial transcriptomics dataset with assigned transcripts. |
--output |
file |
(Output) Unprocessed raw counts after aggregation of transcripts to cells. |
An obs column of cell volumes calculated from spatial transcriptomics data.
Example file:
resources_test/task_ist_preprocessing/mouse_brain_combined/cell_volumes.h5ad
Description:
An obs column of cell volumes calculated from spatial transcriptomics data.
Format:
AnnData object
obs: 'volume'
Data structure:
| Slot | Type | Description |
|---|---|---|
obs["volume"] |
string |
The volume of the cell. |
Unprocessed raw counts after aggregation of transcripts to cells
Example file:
resources_test/task_ist_preprocessing/mouse_brain_combined/spatial_aggregated_counts.h5ad
Description:
This file contains the raw counts after aggregating transcripts to cells.
Format:
AnnData object
obs: 'cell_id', 'centroid_x', 'centroid_y', 'centroid_z', 'n_counts', 'n_genes'
var: 'gene_name', 'n_counts', 'n_cells'
layers: 'counts'
Data structure:
| Slot | Type | Description |
|---|---|---|
obs["cell_id"] |
string |
Unique identifier for the cell (from assignment step). |
obs["centroid_x"] |
string |
X coordinate of the cell. |
obs["centroid_y"] |
string |
Y coordinate of the cell. |
obs["centroid_z"] |
string |
(Optional) Z coordinate of the cell. |
obs["n_counts"] |
string |
Number of counts in the cell. |
obs["n_genes"] |
string |
Number of genes in the cell. |
var["gene_name"] |
string |
Name of the gene. |
var["n_counts"] |
string |
Number of counts of the gene. |
var["n_cells"] |
string |
Number of cells expressing the gene. |
layers["counts"] |
integer |
Raw aggregated counts. |
Normalizing spatial transcriptomics data
Arguments:
| Name | Type | Description |
|---|---|---|
--input_spatial_aggregated_counts |
file |
Unprocessed raw counts after aggregation of transcripts to cells. |
--input_cell_volumes |
file |
(Optional) An obs column of cell volumes calculated from spatial transcriptomics data. |
--output |
file |
(Output) Normalized counts. |
Filtering cells based on QC metrics
Arguments:
| Name | Type | Description |
|---|---|---|
--input |
file |
Unprocessed raw counts after aggregation of transcripts to cells. |
--output |
file |
(Output) QC columns for spatial data. |
Normalized counts
Example file:
resources_test/task_ist_preprocessing/mouse_brain_combined/spatial_normalized_counts.h5ad
Description:
This file contains the normalized counts of the spatial transcriptomics data.
Format:
AnnData object
obs: 'cell_id', 'centroid_x', 'centroid_y', 'centroid_z', 'n_counts', 'n_genes'
var: 'gene_name', 'n_counts', 'n_cells'
layers: 'counts', 'normalized'
Data structure:
| Slot | Type | Description |
|---|---|---|
obs["cell_id"] |
string |
Unique identifier for the cell (from assignment step). |
obs["centroid_x"] |
string |
X coordinate of the cell. |
obs["centroid_y"] |
string |
Y coordinate of the cell. |
obs["centroid_z"] |
string |
(Optional) Z coordinate of the cell. |
obs["n_counts"] |
string |
Number of counts in the cell. |
obs["n_genes"] |
string |
Number of genes in the cell. |
var["gene_name"] |
string |
Name of the gene. |
var["n_counts"] |
string |
Number of counts of the gene. |
var["n_cells"] |
string |
Number of cells expressing the gene. |
layers["counts"] |
integer |
Raw aggregated counts. |
layers["normalized"] |
integer |
Normalized expression values. |
QC columns for spatial data
Example file:
resources_test/task_ist_preprocessing/mouse_brain_combined/spatial_qc_col.h5ad
Description:
This file contains the QC-filter column for spatial data.
Format:
AnnData object
obs: 'passed_QC'
Data structure:
| Slot | Type | Description |
|---|---|---|
obs["passed_QC"] |
string |
Whether the cell passed the quality control. |
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. |