deeplife

Official DeepLife Python toolkit for TwinCell and related analysis: call the Open API from Python, pseudo-bulk with deeplife.pseudobulk, and run sample-level differential expression with deeplife.differential_expression (suitable for pseudo-bulk, bulk, or other compatible count tables). TwinCell code is split into focused subpackages under deeplife.twincell (HTTP client, workflows, preprocess, validation); deeplife.twincell itself still exposes a flat import surface for notebooks.

Source: github.com/deeplifeai/deeplife · Documentation: twincell.deeplife.co/docs · Python: 3.12+ (see pyproject.toml).

Install

pip install deeplife

API key: you need a DeepLife key (usually dl_…). Create or copy one from the TwinCell console (sign in, then open API keys / Keys). In code, set DEEPLIFE_API_KEY in the environment or pass api_key= when constructing the client. API documentation for your deployment is usually at {base_url}/docs when enabled (the client defaults to the production Open API host; pass base_url= for another environment).

Network: the TwinCell Open API is not on the public Internet today. It runs in DeepLife’s cluster and is reachable only when your machine has access to that network—for example by being signed into your organization’s Tailscale tailnet (or whatever access path your team documents). Local-only features (pseudo-bulk, differential expression on .h5ad) do not require API connectivity.

From a git clone (contributors):

uv sync --group dev

Documentation

Hosted docs (same content as the Documentation link on PyPI):

Source: docs/index.md.

Package layout

TwinCell (deeplife.twincell)

Other packages

Install with pip install deeplife. Import deeplife.twincell (flat or by submodule), deeplife.pseudobulk, and deeplife.differential_expression.

Minimal API usage

End-to-end flow: prepare .h5ad or AnnData yourself → create_prediction (single file) or create_prediction_split (control + perturbed + DEGs; validated locally) → poll or watch_prediction → read results (and optional influence / causal helpers).

import os
from deeplife.twincell import DeepLifeClient

client = DeepLifeClient(api_key=os.environ["DEEPLIFE_API_KEY"])
prediction = client.create_prediction(dataset="twincell_ready.h5ad")
final = client.wait_for_prediction(prediction_id=prediction.prediction_id)
print(final.status)

The same client is available explicitly as from deeplife.twincell.http import DeepLifeClient (recommended in application code so imports stay close to the HTTP layer).

Defaults: the client uses the toolkit’s configured API base URL; pass base_url= for another environment. You must be able to reach that host from your network (see Network under Install). Retries apply to safe GET-style calls (polling), not duplicate uploads on POST. For TLS/proxy issues, use tls_verify= and trust_env= on the client—see DeepLifeClient in deeplife.twincell.http.client.

For richer AnnData preparation (QC, column mapping, pseudo-bulk), use deeplife.twincell.preprocess or the tutorial notebooks.

Tutorials

Tutorial .ipynb files are published on the docs site (interactive + download). getting-started-single-cell.ipynb covers Kang PBMC pseudo-bulk, PyDESeq2, and TwinCell target validation. getting-started-bulk.ipynb is the bulk RNA-seq companion.

Run locally from a git clone:

pip install "deeplife[notebook]" jupyterlab
git clone https://github.com/deeplifeai/deeplife
cd deeplife
jupyter lab tutorials/

Set DEEPLIFE_API_KEY (or use the notebook getpass prompt) before the TwinCell API steps. Network access to the Open API is required for those cells — see Install.

Contributors can use uv sync --group dev --extra notebook instead of pip if you prefer the locked lockfile.

After pip install -U deeplife, confirm imports match the layout you expect; the latest release is always on PyPI.

Development

uv sync --group dev
make check-all    # or: ruff, mypy, pytest — see Makefile

CI: .github/workflows/ci.yml runs on pushes and PRs to main / master: uv sync --frozen --group dev, Ruff, mypy, pytest, uv build, twine check --strict. .github/dependabot.yml bumps GitHub Actions weekly.

Releases to PyPI: .github/workflows/pypi-publish.yml runs the same checks, then publishes with OIDC trusted publishing (GitHub environment pypi, trusted publisher configured on the deeplife PyPI project). Bump version in pyproject.toml, push to main, then either push a tag matching v* or run the workflow manually from the Actions tab.