scTenifoldpy 0.2.0

Python implementation of scTenifoldNet and scTenifoldKnk workflows.

scTenifoldpy

scTenifoldpy is a Python implementation of scTenifoldNet and scTenifoldKnk workflows.

Installation

uv venv
uv pip install scTenifoldpy

or:

pip install scTenifoldpy

Optional extras:

uv venv
uv pip install "scTenifoldpy[scanpy]"
uv pip install "scTenifoldpy[parallel-ray]"
pip install "scTenifoldpy[scanpy]"
pip install "scTenifoldpy[parallel-ray]"

scTenifoldpy has no R runtime dependency. The principal-component network step is implemented in Python and uses sklearn.utils.extmath.randomized_svd.

Docker

Build the default runtime image:

docker build -t sctenifoldpy .

Run the CLI from the container, mounting the current directory as the working directory:

docker run --rm -v "$PWD:/workspace" sctenifoldpy scTenifold --help

PowerShell:

docker run --rm -v "${PWD}:/workspace" sctenifoldpy scTenifold --help

Optional extras can be included at build time:

docker build --build-arg EXTRAS=scanpy -t sctenifoldpy:scanpy .
docker build --build-arg EXTRAS=parallel-ray -t sctenifoldpy:ray .

Class API

from scTenifold.data import get_test_df
from scTenifold import scTenifoldNet

df_1 = get_test_df(n_cells=1000)
df_2 = get_test_df(n_cells=1000)

sc = scTenifoldNet(
    df_1,
    df_2,
    "X",
    "Y",
    qc_kws={"min_lib_size": 10},
    nc_kws={"backend": "serial", "n_jobs": 1},
)
result = sc.build()

High-Level API

from scTenifold import compare_networks, virtual_knockout

result = compare_networks(
    df_1,
    df_2,
    qc_kws={"min_lib_size": 10, "plot": False},
    network_kws={"n_nets": 3, "n_samp_cells": 100},
    backend="joblib-threading",
    n_jobs=4,
)

knockout = virtual_knockout(
    df_1,
    ko_genes=["NG-1"],
    qc_kws={"min_lib_size": 10, "min_percent": 0.001},
)

Parallel Backends

Network construction defaults to deterministic serial execution:

from scTenifold import make_networks

networks = make_networks(df_1, backend="serial", n_jobs=1)
networks = make_networks(df_1, backend="joblib-loky", n_jobs=4)

Supported backends are serial, joblib-loky, joblib-threading, and ray. Ray is optional and requires scTenifoldpy[parallel-ray].

CLI

scTenifold config -t 1 -p ./net_config.yml
scTenifold net -c ./net_config.yml -o ./output_folder
scTenifold knk -c ./knk_config.yml -o ./output_folder

Citation

Please cite the original scTenifoldNet paper if you use this package in scientific work: https://www.sciencedirect.com/science/article/pii/S2666389920301872