3dtk — the 3D'omics ToolKit

Find, summarise, export, and download records from the 3D'omics data catalogue — from a command line and a Python API, with no credentials and no server. 3dtk reads a published, checksummed SQLite artefact deposited on Zenodo under a citable DOI.

Documentation: https://3dtk.readthedocs.io/

pip install 3dtk
3dtk database sync          # fetch the catalogue once (59 MB, checksum-verified)
3dtk microsamples query --experiment-id G --sex female --columns context

pip install 3dtk → import py3dtk

Python identifiers cannot begin with a digit, so import 3dtk is a SyntaxError. The distribution and the console script are 3dtk; the import package is py3dtk — the same split as scikit-learn → sklearn.

import py3dtk

with py3dtk.Database() as db:
    genomes = db.genomes.query(quality="high", genus="Faeciplasma")

The data

The catalogue is built from Airtable by 3d-omics/database-build and published to Zenodo. 3dtk is a consumer: it never talks to Airtable, never rebuilds data, and holds no secrets.

A pinned build never silently follows "latest". The concept DOI is for citation; the version DOI is what the code pins, together with the artefact's SHA-256. A download that fails verification is discarded, never used.

Where the catalogue comes from

3dtk resolves the catalogue in this order, and the first hit wins:

1. an explicit --db / path= argument,
2. the PY3DTK_DB environment variable,
3. the user cache (3dtk database sync, or a lazy first-use download),
4. a bundled package resource, if one was shipped.

The published wheel bundles no catalogue, so the first command that needs data downloads it once into the cache. 3dtk database info reports which source was used; 3dtk database where prints the path without downloading anything.

The hierarchy

experiments → specimens → macrosamples → cryosections → microsamples
                                    ↘ genomes (per experiment)
                                    ↘ counts (genome × sample matrices)

Each level is a CLI group with the same action grammar, so learning one teaches the rest:

counts adds matrices and export.

What 3dtk adds over the raw catalogue

Denormalisation. The catalogue stores each level separately. 3dtk joins them, so one query answers a cross-level question:

3dtk microsamples query --experiment-id G --sex female --columns context

returns each microsample with its host species, treatment, cryosection position and spatial coordinates — a five-table join, expressed as one command.

Dense count-matrix export. The count tables are sparse: zeros are dropped, about five-sixths of the microsample cells. 3dtk counts export rebuilds the dense matrix, restoring the zeros and following the original row and column order recorded in matrix_axes:

3dtk counts export --cryosection-id G005bI205A --csv --output-file counts.csv \
                   --coordinates coords.csv

A genome whose row is entirely zero keeps its place — which is exactly why the axes come from matrix_axes and not from SELECT DISTINCT over the sparse rows. --coordinates writes a companion sample table aligned to the matrix columns, so the pair drops straight into a spatial analysis.

Matrices within an experiment share a genome axis, so they merge:

3dtk counts export --experiment-id G --level micro --csv --output-file G.csv

Spatial coordinates. x_coord/y_coord (laser-microdissection stage) and pixel_x/pixel_y (image) are carried through query output, filterable as a bounding box, and available alongside abundances in an export:

3dtk microsamples query --x-min 13000 --x-max 14000 --y-min 18000 --y-max 19000 \
                        --columns spatial

Downloading sequencing data

The catalogue stores ENA browser links and run accessions, not file URLs, so fetch resolves them through the ENA Portal API — in batches, so a 500-microsample fetch issues a handful of requests rather than 500. ENA publishes an MD5 per file, and every download is verified against it as well as against a streaming gzip integrity check.

3dtk microsamples fetch --cryosection-id G005bI205A --output-dir data
3dtk microsamples fetch --experiment-id G --script download.sh   # batch instead

Every file is logged to an append-only JSONL manifest. Metabolomics macrosamples point at MetaboLights rather than ENA; 3dtk surfaces those accessions rather than pretending it can download them.

Python API

import py3dtk

with py3dtk.Database() as db:
    db.specimens.count(sex="female")
    db.genomes.values("phylum", limit=5)
    db.microsamples.stats(experiment_id="G")

    matrix = db.counts.export(experiment_id="G", level="micro")
    coords = db.counts.coordinates(matrix)

Every collection supports query, count, values and stats; macrosamples and microsamples add fetch; counts adds matrices, export and coordinates.

Documentation

Full documentation: https://3dtk.readthedocs.io/

Development

git clone https://github.com/3d-omics/3dtk
cd 3dtk
python -m pip install -e ".[dev]"
python -m pytest

The test suite builds a miniature catalogue in a temp directory and makes no network calls, so it runs offline and in seconds.

Related repositories

3dtk follows the design of ehitk, the Earth Hologenome Initiative's toolkit.

Licence

GPLv3 — see LICENSE. The data is CC-BY-4.0; see the catalogue's Zenodo record.