ingest-validation-tools

HuBMAP data upload guidelines, and tools which check that uploads adhere to those guidelines. Assay documentation is on Github Pages.

HuBMAP has three distinct metadata processes:

• Donor metadata is handled by Jonathan Silverstein on an adhoc basis: He works with whatever format the TMC can provide, and aligns it with controlled vocabularies.
• Sample metadata is handled by Chris Briggs and Bill Shirey. The standard operating procedure is outlined here.
• Dataset uploads should be validated first by the TMCs. Dataset upload validation is the focus of this repo. Details below.

For assay type working groups:

Before we can write code to validate a particular assay type, there are some prerequisites:

• A document describing the experimental techniques involved.
• A list of the metadata fields for this type, along with descriptions and constraints.
• A list of the files to be expected in each dataset directory, along with descriptions. Suggestions for describing directories.

When all the parts are finalized,

• The document will be translated into markdown, and added here.
• The list of fields will be translated into a table schema, like those here.
• The list of files will be translated into a directory schema, like those here.

Stability

Once approved, both the list of metadata fields and the list of files is fixed. The metadata for a particular assay type needs to be consistent for all datasets, as does the set of files which comprise a dataset. Edits to descriptions are welcome, as are improved validations.

HuBMAP HIVE members: For questions about the stability of metadata, contact Nils Gehlenborg (@ngehlenborg), or add him as a reviewer on the PR. For the stability of directory structures, contact Phil Blood (@pdblood).

For data submitters and curators:

Checkout the repo and install dependencies:

python --version  # Should be Python3.
git clone https://github.com/hubmapconsortium/ingest-validation-tools.git
cd ingest-validation-tools
# Optionally, set up venv or conda, then:
pip install -r requirements.txt
src/validate_upload.py --help

You should see the documention for validate_upload.py

Now run it against one of the included examples, giving the path to an upload directory:

src/validate_upload.py \
  --local_directory examples/dataset-examples/bad-tsv-formats/upload \
  --output as_text

You should now see this (extensive) error message. This example TSV has been constructed with a mistake in every column, just to demonstrate the checks which are available. Hopefully, more often your experience will be like this:

src/validate_upload.py \
  --local_directory examples/dataset-examples/good-codex-akoya/upload

No errors!

Documentation and metadata TSV templates for each assay type are here. Addition help for certain common error messages is available here

Validating single TSVs:

If you don't have an entire upload directory at hand, you can validate individual metadata, antibodies, contributors, or sample TSVs:

src/validate_tsv.py \
  --schema metadata \
  --path examples/dataset-examples/good-scatacseq-v1/upload/metadata.tsv

No errors!

Running plugin tests:

Additional plugin tests can also be run. These additional tests confirm that the files themselves are valid, not just that the directory structures are correct. These additional tests are in a separate repo, and have their own dependencies.

# Starting from ingest-validation-tools...
cd ..
git clone https://github.com/hubmapconsortium/ingest-validation-tests.git
cd ingest-validation-tests
pip install -r requirements.txt

# Back to ingest-validation-tools...
cd ../ingest-validation-tools
src/validate_upload.py \
  --local_directory examples/dataset-examples/good-codex-akoya/upload \
  --plugin_directory ../ingest-validation-tests/src/ingest_validation_tests/

For developers and contributors:

A good example is of programatic usage is validate-upload.py; In a nutshell:

upload = Upload(directory_path=path)
report = ErrorReport(upload.get_errors())
print(report.as_text())

(If it would be useful for this to be installable with pip, please file an issue.)

To make contributions, checkout the project, cd, venv, and then:

pip install -r requirements.txt
pip install -r requirements-dev.txt
./test.sh

After making tweaks to the schema, you will need to regenerate the docs: The test error message will tell you what to do.

For releases we're just using git tags:

$ git tag v0.0.x
$ git push origin v0.0.x

Repo structure

Checking in the built documentation is not the typical approach, but has worked well for this project:

• It's a sanity check when making schema changes. Since the schema for an assay actually comes for multiple sources, having the result of include resolution checked in makes it possible to catch unintended changes.
• It simplifies administration, since a separate static documentation site is not required.
• It enables easy review of the history of a schema, since the usual git/github tools can be used.

Upload process and upload directory structure

Data upload to HuBMAP is composed of discrete phases:

• Upload preparation and validation
• Upload and re-validation
• Restructuring
• Re-re-validation and pipeline runs

Uploads are based on directories containing at a minimum:

• one or more *-metadata.tsv files.
• top-level dataset directories in a 1-to-1 relationship with the rows of the TSVs.

The type of a metadata TSV is determined by reading the first row.

The antibodies_path (for applicable types), contributors_path, and data_path are relative to the location of the TSV. The antibodies and contributors TSV will typically be at the top level of the upload, but if they are applicable to only a single dataset, they can be placed within that dataset's extras/ directory.

You can validate your upload directory locally, then upload it to Globus, and the same validation will be run there.