scsplice 2.0.0

Single-cell alternative-splicing analysis on AnnData (Python port of R splikit)

scsplice

Single-cell alternative-splicing analysis for the scverse ecosystem.

scsplice is the Python port of the R package splikit. It analyses splice-junction count data in single-cell RNA-seq, treating each event as a pair of inclusion (M1) and exclusion (M2) counts derived from local junction variants (LJVs). The package is AnnData-native — junctions live on the var axis, M1 and M2 sit in layers, and downstream analysis composes naturally with scanpy.

Status

v1.0. v1.0 scope is intentionally narrow:

• scs.io.read_starsolo — ingest STARsolo Solo.out/SJ/ for one or more samples.
• scs.tl.make_m2 — build the exclusion matrix from M1 + LJV grouping.
• scs.pp.highly_variable_events — per-library binomial-deviance HVE selection.
• scs.tl.pseudo_correlation — beta-binomial Cox-Snell / Nagelkerke pseudo-R² against an external matrix.

HVG, plotting, and silhouette utilities from the R package are intentionally omitted — scanpy, pyranges, and sklearn already cover those.

Installation

scsplice ships a C++ extension built via scikit-build-core + pybind11. Eigen3 (header-only) is required at install time; OpenMP is optional but strongly recommended for multi-threaded kernels.

From PyPI (once v2.0 is published)

pip install scsplice

From source

git clone https://github.com/Arshammik/scsplice
cd scsplice
pip install .

System dependencies before running pip install:

Ubuntu / Debian

sudo apt install libeigen3-dev libomp-dev

macOS (Homebrew)

brew install eigen libomp
# tell CMake where Apple Clang's OpenMP lives
export OpenMP_ROOT="$(brew --prefix libomp)"
export LDFLAGS="-L${OpenMP_ROOT}/lib"
export CPPFLAGS="-I${OpenMP_ROOT}/include"

HPC cluster (Compute Canada / Sharcnet pattern)

module load eigen/3.4.0
# any modern GCC with OpenMP (gcc/12+) on the system module path

Editable install (development)

pip install -e ".[dev]"

This installs the package, all test dependencies, the docs toolchain (mkdocs-material, mkdocstrings[python], mkdocs-jupyter), and ruff / pre-commit. C++ edits require re-running pip install -e .; pure-Python edits take effect immediately.

Quick start

import scsplice as scs
import scanpy as sc

# (1) Ingest STARsolo splice-junction counts (M1) and LJV grouping
adata = scs.io.read_starsolo(
    sj_dirs=["sample1/Solo.out/SJ", "sample2/Solo.out/SJ"],
    sample_ids=["s1", "s2"],
)

# (2) Build exclusion matrix (M2) from inclusion counts + junction grouping
scs.tl.make_m2(adata, n_threads=8)

# (3) Identify highly variable events per library using binomial deviance
scs.pp.highly_variable_events(adata, min_row_sum=50, n_threads=8)

# Optional: compose with scanpy on the splicing embedding
# (PCA / neighbors / leiden over logit(M1 / (M1 + M2)))

Numerical equivalence

scsplice reproduces R splikit results to a documented tolerance on a fixed reference dataset (M2 bit-exact; HVE deviance rtol=1e-10; pseudo-correlation rtol=1e-7). The cross-language regression suite, R reference fixtures, and end-to-end M1/M2 validation pipeline live on the validation branch.

Documentation

Full documentation is available at https://arshammik.github.io/scsplice/.

Topics include:

• Getting Started — installation and first workflow
• Tutorials — step-by-step notebooks
• How-to Guides — recipes for common tasks
• Reference — complete API documentation
• Explanation — conceptual background and design

License

MIT.