Catalog columns

Contents

Catalog columns#

Every column of the catalog schema. “Facet” columns are categorical filters (Catalog.facets()); “range” columns are numeric and filter with __gte / __lte.

Column	Dtype	Facet	Range
`uid`	`string`
`canonical_key`	`string`
`study_id`	`string`	yes
`collection_name`	`string`
`issue_url`	`string`
`organism`	`string`	yes
`organism_id`	`string`
`tissue`	`string`	yes
`tissue_id`	`string`
`disease`	`string`	yes
`disease_id`	`string`
`development_stage`	`string`	yes
`technology`	`string`	yes
`assay`	`string`	yes
`technology_version`	`string`
`panel_size`	`Int64`		yes
`validation_status`	`string`	yes
`tier`	`string`	yes
`license`	`string`	yes
`license_url`	`string`
`license_noncommercial`	`boolean`	yes
`license_redistributable`	`boolean`
`license_unknown`	`boolean`
`n_obs`	`Int64`		yes
`n_features`	`Int64`		yes
`total_counts`	`Float64`		yes
`zarr_url`	`string`
`card_url`	`string`
`vitessce_url`	`string`
`size_bytes`	`Int64`		yes
`container_tag`	`string`
`sddb_version`	`string`
`published_at`	`string`
`slug`	`string`
`title`	`string`
`pathological`	`boolean`
`product`	`string`
`modality`	`string`
`biomaterial_type`	`string`
`chemistry_version`	`string`
`staining_method`	`list[string]`
`preservation_method`	`string`
`hne_image`	`boolean`
`if_image`	`boolean`
`ftu_annotation`	`boolean`
`mpp`	`Float64`		yes
`image_width`	`Int64`		yes
`image_height`	`Int64`		yes
`magnification`	`string`
`sdata_repr`	`string`
`n_transcripts`	`Int64`		yes
`capture_area`	`string`
`publication_date`	`string`
`doi`	`string`	yes
`pmid`	`string`
`publication_year`	`string`
`publication_verify_status`	`string`
`sample_id`	`string`
`donor_id`	`string`
`database_version`	`string`
`sddb_id`	`string`
`disease_details`	`string`
`default_table`	`string`
`dataset_url`	`string`
`thumbnail_url`	`string`
`collections`	`list[string]`
`sample_title`	`string`
`timepoint`	`string`
`timepoint_kind`	`string`
`timepoint_days`	`Float64`		yes
`treatment`	`string`
`genotype`	`string`
`condition`	`string`

Study and condition columns#

All optional: older catalogs without them still load, and they are null when the source has no such metadata.

Dataset columns#

Column	Description
`study_id`	Identifier of the study the dataset belongs to.
`sample_title`	Free-text title of the individual sample.
`timepoint`	Sampling timepoint as written in the source (e.g. `day 7`).
`timepoint_kind`	Kind of timepoint (e.g. days post injury, age, treatment duration).
`timepoint_days`	Timepoint normalised to days (numeric; supports range filters).
`treatment`	Treatment or perturbation applied to the sample.
`genotype`	Genotype of the sample.
`condition`	Condition label for the sample (e.g. control vs. disease).

Collection columns (`collections.parquet`)#

Column	Description
`overall_design`	Study-level design summary text.
`pubmed_id`	PubMed ID of the associated paper.
`source_url`	URL of the study’s source record.
`parent`	Slug of the parent collection, if any.
`source`	Origin of the collection (e.g. GEO, HEST).
`year`	Publication year (integer).
`contrast_axes`	JSON object `{axis: {value: count}}` of the condition axes that vary across members.
`assays`	Distinct assays across member datasets.
`modalities`	Distinct modalities across member datasets.
`organisms`	Distinct organisms across member datasets.
`tissues`	Distinct tissues across member datasets.
`diseases`	Distinct diseases across member datasets.
`timepoint_kinds`	Distinct timepoint kinds across member datasets.
`condition_values`	Distinct condition values across member datasets.