HCA Ingest Service Graph Validation Suite

What is this useful for in the scope of the HCA:

1. Enables data wranglers to visually analyze the relationships inside a submission to look for inconsistencies.
2. Provides an automated graph validator for which to create tests using step 1 and can be run fully containerized.

Features

The suite is divided in two separate, extensible parts:

• hydrators enable users to import and populate data into a graph database. The reason not to call them importers is import is a reserved keyword in Python and from importers import importer is a bit confusing. 😵

• actions provide different tools to work with the generated graph. The first and most important is to run a series of tests to validate the constraints Data Wranglers want to impose on submissions. Another action is generating reports and extracting statistics from the graph to send to the submitters. Any other actions can be implemented to extend the suite.

Functionality

So far, the functionality planned is as follows (WIP items are still not fully implemented):

• Hydrators:

  • Ingest Service Spreadsheet.
  • Ingest Service API Submission.
  • BioSamples API (WIP).

• Actions:

  • Opening an interactive visualizer to query the graph.
  • Running tests on the graph.
  • Generating reports for the graph (WIP).

Installation

The Graph Validator Suite requires docker running in the host machine.

From the git repo

git clone git@github.com:ebi-ait/ingest-graph-validator.git
cd ingest-graph-validator
pip install .

From PyPI

A Python package has been published in (PyPI)[https://pypi.org/project/ingest-graph-validator].

The installation into a (virtualenv)[https://packaging.python.org/guides/installing-using-pip-and-virtual-environments] is heavily recommended.

pip install ingest-graph-validator

If you install the Graph Validator Suite this way, you should head to the github repo to get the graph tests and the graph reports.

Usage

Step by step usage for data wranglers

1. Ensure Docker is installed and running

2. Once installed from the Python package, start the backend by opening a terminal and typing:

   ingest-graph-validator init

   Keep in mind, first time executing the init command will take longer as it has to pull the Neo4j Docker image from dockerhub.

3. Import a spreadsheet:

   ingest-graph-validator hydrate xls <spreadsheet filename>

4. Go to http://localhost:7474 in a browser to open the frontend.

5. Connect to the backend (you do not need to change any fields, leave username/password empty):

   

6. You can then start writing cypher queries in the input field on top of the web frontend to visualize the graph. For example:

   MATCH p=(n) RETURN p

   Will show the entire graph. Keep in mind this will crash the browser on huge datasets.

Note The server backend will continue running in the background, and you only need to open the browser again to continue your work. If you want to shutdown the backend, open a terminal and type:

ingest-graph-validator shutdown

How to run tests

In order to run the tests in the graph_test_set directory, you need to run the following commands:

1. In a shell, run:

ingest-graph-validator init

If you want to run using an ingest submission uuid:

ingest-graph-validator hydrate ingest <ingest_submission_uuid>

If you want to run using a spreadsheet

ingest-graph-validator hydrate xls <path/to/spreadsheet>

then

ingest-graph-validator action test <path_to_tests>

More help

The Graph Validator Suite uses a CLI similar to git. Running a command without specifying anything else will show help for that command. At each level, the commands have different arguments and options. Running any subcommand with -h or --help with give you more information about it.

The root level commands are:

• ingest-graph-validator init starts the database backend and enables a frontend visualizer to query the database, in http://localhost:7474 by default.

• ingest-graph-validator hydrate shows the list of available hydrators.

• ingest-graph-validator actions shows the list of available actions.

• ingest-graph-validator shutdown stops the backend.

Containerized execution

WIP

Extra stuff

Useful cypher queries

Show all nodes and relationships

MATCH p = (n)
RETURN p

Show all nodes and relationship excluding some

MATCH p = (n)
WHERE NOT n:LABEL AND NOT n:LABEL
RETURN p

Expands paths from a node

This one will be shown with an example. The example selects the donor CBTM-376C from Meyer's Tissue Stability dataset, and expands the paths to show all biomaterials, processes and files linked to it.

Note: Make sure to strictly define only one node to use as the source, otherwise it will be confusing.

Note: You have to be careful not to include nodes that would link your path to another one. For example, protocol or project are linked to more than one experimental design.

The first two lines are used to select one single node from which to expand. The third line expands the path using these parameters:

1. n, the starting node or nodes (preferably one for your first queries).
2. "", the relationship filter. We are not filtering by any relations in this query.
3. "-project|-protocol", the label filter. We are excluding (hence the minus sign) any nodes with the labels project or (that is represented by the |) protocol.
4. 0 is the minimum depth. Normally 0. Otherwise the starting nodes get excluded.
5. -1 is used to determine the maximum depth for the path expansion. -1 means no limit. If you would set a 1 here, the result would be the CBTM-376C donor and its first level neighbours.

MATCH (n:donor_organism)
WHERE n.`biomaterial_core.biomaterial_id` = "CBTM-376C"
CALL apoc.path.expand(n, "", "-project|-protocol", 0, -1) YIELD path
RETURN path

List of Ingest submission IDs

• KidneySingleCellAtlas: d5410c6e-612d-421a-a66f-2de5e04dd050
• HumanColonicMesenchymeIBD: c51efa69-a348-46d4-9997-c7a11915d53b
• scRNAseqSystemicComparison: d1610c4a-76c6-4b69-af63-c74af869fa75
• TissueStability: fd52efcc-6924-4c8a-b68c-a299aea1d80f
• demo HPSI human cerebral organoids: 6f33a6c6-170d-460b-bbba-9668e9d77aaf
• Mouse Melanoma: 4756b97c-3666-4ff0-9c48-611dc06b7740
• Multiplexed scRNA-seq with barcoded antibodies: 6931d821-9cd2-4ae1-acf7-62cd3b90082e
• Reprogrammed_Dendritic_Cells: 8c45a848-ab32-4928-8dd6-567b75eaf7e1
• HPSI human cerebral organoids: fce97270-fce0-4744-8a4e-a93d95521852
• Tabula Muris: 14df1f92-155c-4da2-97fc-85601dee64da
• snRNA-seq_for_human_retina: 7d515499-b5b6-4083-95cd-6a393344865e
• Healthy and type 2 diabetes pancreas: c81f7d54-a27f-4212-a6df-88dde947f7cc
• Single cell transcriptome analysis of human pancreas: 5cd8b827-e4fa-4ffa-86f5-f0f8302baee9
• SingleCellLiverLandscape: 1e2601a5-8938-446c-bbb3-1f37a84b11da
• WongAdultRetina: 668791ed-deec-4470-b23a-9b80fd133e1c
• Single cell RNAseq characterization of cell types produced over : 0948a727-228f-4cfc-857e-6243c6aed08d
• Kidney biopsy scRNA-seq: 588281cb-93e2-4888-b75e-5cee5e39fc7f
• BM_PC: 67abd7a1-644b-48d0-b8b8-251d8195a742
• Human Hematopoietic Profiling: 7dbcf5ae-f8d7-487c-a3d4-794d8639a1e2
• Mouse Endoderm Project: 0fb44736-c50f-49ab-bcdc-0985e596b955
• Drop-seq, DroNc-seq, Fluidigm C1 Comparison: bf3116e5-1af1-46c2-8bbd-44dac49d1e7f
• 1M Immune Cells: 85e72912-9f91-4489-8169-3b43cc65a16a
• 1M Neurons: 064d36ca-ea4d-428f-b30f-0cf5e5350a9d
• MouseGastrulationAtlas: 19c5c89a-7bfb-4b4d-9ff5-3ef009304ba5
• Fetal/Maternal Interface: 46fe8bd7-329b-4f09-b227-5ee48c109c16
• Tissue stability: 02e89f20-84c8-4daa-aaeb-80f4a85733ff
• CD4+ cytotoxic T lymphocytes: 3bb965cb-2a6f-4802-a4f2-52d0423e6ecc
• Diabetic Nephropathy snRNA-seq: a69c731b-9db7-45e3-938f-daf2e4636a6e
• HDCA-Sweden-10x: 071fc37f-1a01-4cf7-a6f5-2662c42c12b6
• HumanTissueTcellActivation: 8b5feb5e-9039-4c54-9e79-053e490c141a
• HumanMousePancreas: 7b4cd093-bfa5-477e-9c95-69bafc1cb6bf

Releasing a new version to PyPI

You should have a maintainer role access for this project in PyPI.

1. Bump the version and create a tag

   make patch|minor|major
   

2. Upload to PyPI

   make release
   

Credits

This package was created with Cookiecutter.