This text describes and differentiates the dataset from other datasets in the same collection. It is strongly recommended that each dataset title in a collection is unique and does not depend on other metadata such as a different assay to disambiguate it from other datasets in the collection.

Cells of the adult human heart collection is "All — Cells of the adult human heart".

To be able to link to other studies from the same lab as sometimes labs tend to process samples similarly and generate libraries similarly, resulting in less strong batch effects

Values must refer to cell metadata keys in obs. Together, these keys define the batches that a normalization or integration algorithm should be aware of. For example if "patient" and "seqBatch" are keys of vectors of cell metadata, either ["patient"], ["seqBatch"], or ["patient", "seqBatch"] are valid values.

The value must match a key to an embedding in obsm for the embedding to display by default in CELLxGENE Explorer.

Other technical or experimental covariates that could affect the quality or batch of the sample. Must not contain identifiers. This field is designed to capture potential challenges for data integration not captured elsewhere.

Identification number of the sample. This is the fundamental unit of sampling the tissue (the specimen taken from the subject), which can be the same as the 'donor_ID', but is often different if multiple samples are taken from the same subject. Note: this is NOT a unit of multiplexing of donor samples, which should be stored in "library".

Fundamental unit of sampling of the tissue.

It is strongly recommended that this identifier be designed so that it is unique to: a given individual within the collection of datasets that includes this dataset, and a given individual across all collections in CELLxGENE Discover.

It is strongly recommended that "pooled" be used for observations from a sample of multiple individuals that were not confidently assigned to a single individual through demultiplexing.
It is strongly recommended that "unknown" ONLY be used for observations in a dataset when it is not known which observations are from the same individual.

Fundamental unit of biological variation of the data

The protocols.io URL (if none exists, please use the BioRxiv URL) for the full experimental protocol; or if multiple protocols exist please list them e.g. sample preparation protocol / sequencing protocol.

https://www.biorxiv.org/conte nt/early/2017/09/24/193219

Useful to look up protocol data that can provide insight on batch effects. As protocols can sometimes apply to a subset of the study, we capture this at a sample level. This information may not always be available.

To be able to link to other studies from the same institution as sometimes samples from different labs in the same institute are processed via similar core facilities. Thus batch effects may be smaller for datasets from the same institute even if other factors differ.

It is strongly recommended that this identifier be designed so that it is unique to a given site within the collection of datasets that includes this site (for example, the labels 'site1', 'site2' may appear in other datasets thus rendering them indistinguishable).

To understand whether the collection site contributes to batch effects

Time point when the sample was collected. This field is only needed if multiple samples from the same subject are available and collected at different time points. Sample collection dates (e.g. 23/09/22) cannot be used due to patient data protection, only relative time points should be used here (e.g. day3).

Explains variability in the data between samples from the same subject

The unique ID that is used to track libraries in the investigator's institution (should align with the publication).

A way to track the unit of data generation. This should include sample pooling

The unique ID used to track libraries from one of the following public data repositories: EGAX*, GSM*, SRX*, ERX*

Links a dataset back to the source from which it was ingested, optional only if this is the same as the library_ID.

Encoding of author knowledge on any further information related to likely batch effects.

Batch run by different personnel on different days

Space for author intuition of batch effects in their dataset

"NCBITaxon:9606" for Homo sapiens or "NCBITaxon:10090" for Mus musculus.

Strong biological effect that needs to be considered for batch covariate selection

Manner of death classification based on the Hardy Scale or 'unknown' or 'not applicable':
Category 1 = Violent and fast death Deaths due to accident, blunt force trauma or suicide, terminal phase estimated at < 10 min.
Category 2 = Fast death of natural causes -Sudden unexpected deaths of people who had been reasonably healthy, after a terminal phase estimated at < 1 hr (with sudden death from a myocardial infarction as a model cause of death for this category)
Category 3 = Intermediate death - Death after a terminal phase of 1 to 24 hrs (not classifiable as 2 or 4); patients who were ill but death was unexpected
Category 4 = Slow death - Death after a long illness, with a terminal phase longer than 1 day (commonly cancer or chronic pulmonary disease); deaths that are not unexpected
Category 0 =Ventilator Case - All cases on a ventilator immediately before death
Unknown = The cause of death is unknown
Not applicable = Subject is alive

Manner of death can affect cellular profiles.

The study subgroup that the participant belongs to. This indicates whether the participant was a surgical donor (this includes patients providing blood samples or biopsies), a postmortem donor, or an organ donor.

The source of the sample (whether the sample comes from alive subject; an organ donor; or deceased subject) can result in different cellular profiles and hence batch effects.

PATO:0000383 for female, PATO:0000384 for male

Likely biological effect. Need to know if we have a balanced dataset or if sex is collinear with the dataset.

brush; scraping; biopsy; surgical resection; blood draw; body fluid; other

Main contributor to batch effects

Source of batch effect & dataset exclusion criteria

Main contributor to batch effects

The detailed anatomical location of the sample, please provide a specific UBERON term.

If tissue_type is "tissue" or "organoid", this must be the most accurate child of UBERON:0001062 for anatomical entity. If tissue_type is "cell culture" this must follow the requirements for cell_type_ontology_term_id.

Major biological effect that needs to be assessed for sufficient coverage in the atlas datasets.

The detailed anatomical location of the sample - this does not have to tie to an ontology term.

To help the integration team understand the anatomical location of the sample, specifically to solve the problem when the UBERON ontology terms are insufficiently precise.

ambient temperature; cut slide; fresh; frozen at -70C; frozen at -80C; frozen at -150C; frozen in liquid nitrogen; frozen in vapor phase; paraffin block; RNAlater at 4C; RNAlater at 25C; RNAlater at -20C; other

Main contributor to batch effects

This must be "cell", "nucleus", or "na".
This must be the correct type for the corresponding assay:
10x transcription profiling [EFO:0030080] and its children = "cell" or "nucleus"
ATAC-seq [EFO:0007045] and its children = "nucleus"
BD Rhapsody Whole Transcriptome Analysis [EFO:0700003] = "cell"
BD Rhapsody Targeted mRNA [EFO:0700004] = "cell"
CEL-seq2 [EFO:0010010] = "cell" or "nucleus"
CITE-seq [EFO:0009294] and its children = "cell"
DroNc-seq [EFO:0008720] = "nucleus"
Drop-seq [EFO:0008722] = "cell" or "nucleus"
GEXSCOPE technology [EFO:0700011] = "cell" or "nucleus"
inDrop [EFO:0008780] = "cell" or "nucleus"

Major source of batch effect & dataset exclusion criteria

Specifies the cell types targeted for enrichment or depletion beyond the selection of live cells.

This must be a Cell Ontology (CL) term (http://www.ebi.ac.uk/ols4/ontologies/cl). For cells that are enriched, list the CL code followed by a "+". For cells that were depleted, list the CL code followed by a "-". If no enrichment or depletion occurred, please use 'na' (not applicable)

If cell lineages were filtered, this may be a dataset exclusion criterion

Is a measure of sample quality that could be used to explain outlier samples

Can explain the number of doublets found in samples

Year of sample collection. Should not be detailed further(to exact month and day), to prevent identifiability.

May explain whether a dataset was separated into smaller batches.

This must be an EFO term and either:
"EFO:0002772" for assay by molecule or preferably its most accurate child
"EFO:0010183" for single cell library construction or preferably its most accurate child
An assay based on 10X Genomics products should either be "EFO:0008995" for 10x technology or preferably its most accurate child. An assay based on SMART (Switching Mechanism at the 5' end of the RNA Template) or SMARTer technology SHOULD either be "EFO:0010184" for Smart-like or preferably its most accurate child.
Recommended:
10x 3' v2 "EFO:0009899"
10x 3' v3 "EFO:0009922"
10x 5' v1 "EFO:0011025"
10x 5' v2 "EFO:0009900"
Smart-seq2 "EFO:0008931"
Visium Spatial Gene Expression "EFO:0010961"

Major source of batch effect and dataset filtering criterion

Indicating which samples' libraries were prepared in the same chip/plate/etc., e.g. batch1, batch2.

Sample preparation is a major source of batch effects.

The identifier (or accession number) that indicates which samples' libraries were sequenced in the same run.

Library sequencing is a major source of batch effects

May be a source of batch effect that has to be tested

"subClassOf" : ["EFO:0002699"] - https://www.ebi.ac.uk/ols/ontologies/efo/terms?iri=http%3A%2F%2Fwww.ebi.ac.uk%2Fefo%2FEFO_0002699

This captures potential strand hopping which may cause data quality issues

This must be True if this is the canonical instance of this cellular observation and False if not. This is commonly False for meta-analyses reusing data or for secondary views of data.

This helps to ensure samples are not used twice.

Possible source of batch effect and confounder for some biological analysis

http://www.ensembl.org/info/website/archives/index.html) or NCBI/RefSeq

Possible source of batch effect and confounder for some biological analysis

Protocol used for alignment analysis, please specify which version was used e.g. cell ranger 2.0, 2.1.1 etc.

cell ranger 3.0.1; kallisto bustools; GSNAP

Affects which cells are filtered per dataset, and which reads (introns and exons or only exons) are counted as part of the reported transcriptome. This can convey batch effects.

Affects the number of reads per cell called in a sample

Encoding of author intuition of cellular annotation in their dataset.

This must be a Cell Ontology (CL) term (http://www.ebi.ac.uk/ols4/ontologies/cl).
If no appropriate high-level term can be found or the cell type is unknown, then it is strongly recommended to use 'unknown'.
The following terms must not be used: "CL:0000255" for eukaryotic cell; "CL:0000257" for Eumycetozoan cell; "CL:0000548" for animal cell

Encoding of cell type to help alignment with other datasets.

CellxGene mandatory fields (not in the HCA Tier 1 schema)

If organism_ontolology_term_id is "NCBITaxon:9606" for Homo sapiens, this should be an HsapDv term. If organism_ontolology_term_id is "NCBITaxon:10090" for Mus musculus, this should be an HsapDv term.

Requirements for data contributors adhering to GDPR or like standards: HCA requests that you do not submit year-specific terms. For convenience, below are some broader age bracket ontology terms:
Embryonic stage = A term from the set of Carnegie stages 1-23 = (up to 8 weeks after conception; e.g. HsapDv:0000003)
Fetal development = A term from the set of 9 to 38 week post-fertilization human stages = (9 weeks after conception and before birth; e.g. HsapDv:0000046)
Post natal =
Years 0-14 HsapDv:0000264
Years 15-19 HsapDv:0000268
Years 20-29 HsapDv:0000237
Years 30-39 HsapDv:0000238
Years 40-49 HsapDv:0000239
Years 50-59 HsapDv:0000240
Years 60-69 HsapDv:0000241
Years 70-79 HsapDv:0000242
Years 80-89 HsapDv:0000243

The publication digital object identifier (doi) for the protocol. If no pre-print nor publication exists, please write 'not applicable'.

To enable data to be linked to the publication.