crispyx

Motivation

Genome-wide CRISPR screens routinely produce datasets with hundreds of thousands of cells and tens of thousands of genes. Standard single-cell analysis toolkits (Scanpy, Pertpy) load the entire count matrix into memory, requiring large RAM allocations and often making routine workflows impractical on laptops or shared compute nodes.

crispyx solves this by streaming data directly from on-disk AnnData (.h5ad) files. Quality control, normalisation, pseudo-bulk aggregation, and differential expression all operate without materialising the full matrix.

Features

• Streaming QC & preprocessing – Filter cells, perturbations, and genes; normalise and log-transform; CSC-aware streaming with format_mismatch_policy; all without loading the full matrix into memory
• Pseudo-bulk aggregation – Absolute profiles over multiple grouping columns (for example, perturbation × batch), strict count sums or mean log1p expression, optional deterministic bootstrap sampling, and explicit within-batch effect calculation
• Differential expression – t-test, Wilcoxon rank-sum (including batch-stratified / van Elteren test via batch_column), and negative binomial GLM with apeGLM LFC shrinkage; multi-core support and adaptive memory management; per-condition low-expression filtering to exclude genes that are near-zero in both groups
• Dimension reduction – Memory-efficient PCA and KNN graph construction on backed data
• Scanpy-compatible API & plotting – Familiar cx.pp, cx.pb, cx.tl, and cx.pl namespaces; Scanpy-style rank genes plots, volcano, MA, PCA, UMAP, QC summaries, and overlap heatmaps
• Data preparation utilities – Edit backed metadata without loading X; standardise gene names; normalise perturbation labels; auto-detect metadata columns
• HPC-ready – Resume/checkpoint for long-running jobs; configurable memory_limit_gb; Docker and Singularity support

Quick Start

import crispyx as cx

# Open dataset without loading into memory
adata = cx.read_h5ad_ondisk("data/demo_benchmark.h5ad")

# Quality control with adaptive thresholds
adata = cx.pp.qc_summary(
    adata,
    perturbation_column="perturbation",
    min_genes=5,
    min_cells_per_perturbation=5,
)

# Differential expression
adata = cx.tl.rank_genes_groups(
    adata,
    perturbation_column="perturbation",
    method="wilcoxon",  # or "t-test", "nb_glm"
)

# Access results
print(adata.uns["rank_genes_groups"])
de_results = adata.uns["rank_genes_groups"].load()

For the full workflow (normalisation, PCA, pseudo-bulk, NB-GLM, LFC shrinkage, plotting, data preparation utilities), see the Usage Guide and the tutorial notebook.

Performance

Benchmarked across 12 CRISPR screen datasets (21k–1.97M cells), crispyx (v0.0.1) consistently outperforms Scanpy, Pertpy/PyDESeq2, and edgeR in both speed and memory:

Speed and memory ratios are computed only over runs where both tools completed, so they exclude the largest screens — precisely the cases where crispyx's advantage is greatest.

crispyx completed all 89 runs across all 12 datasets. Pertpy/PyDESeq2 completed on 4 of 12 datasets and edgeR on none, both failing by timeout or memory limit. Scanpy completed at least one task on every dataset, but timed out or exceeded the memory cap on 12 of its 36 runs.

See benchmarking/ for full results and reproduction scripts.

Installation

pip install crispyx

For development (editable install with all extras):

git clone https://github.com/jinhongdu-lab/crispyx.git
cd crispyx
pip install -e ".[test,benchmark,docs]"

crispyx supports Python 3.10–3.12 and is compatible with recent releases of the scientific stack, including anndata >= 0.13 and pandas >= 3.0 (where string metadata is stored on disk using the nullable-string encoding).

Benchmarking

cd benchmarking
./run_benchmark.sh config/Adamson.yaml       # single dataset
./run_benchmark.sh config/*.yaml             # all datasets

See benchmarking/README.md for configuration options and output structure.

Testing

pytest

Documentation

sphinx-build docs docs/_build

Acknowledgements

crispyx builds on the foundational work of Scanpy (Wolf et al., 2018), Pertpy, PyDESeq2 (Muzellec et al., 2023), and AnnData (Virshup et al., 2024). We gratefully acknowledge these projects for establishing the single-cell analysis ecosystem in Python; crispyx extends their APIs and algorithmic designs to enable memory-efficient, streaming computation for large-scale CRISPR screen datasets.

Contributing

Suggestions, bug reports, and contributions are welcome! Please open an issue or submit a pull request.

License

crispyx 0.0.9 and later is released under a Modified MIT License — every MIT freedom, with no fee or royalty, plus two attribution conditions that apply to commercial use. See the LICENSE for the terms. crispyx 0.0.8 and earlier remains under the unmodified MIT License. If you use crispyx in research, please cite it — see CITATION.cff.