brisc is a high-performance library for analyzing single-cell data at scale. It prioritizes running as fast as possible on multi-core CPU systems, strict reproducibility, and a clean, user-friendly interface. On datasets of 1 to 20 million cells, it cuts the runtime of common workflows from hours to minutes.
Full documentation is available at brisc.run.
Why brisc?
- Blazing fast — ground-up optimization of core algorithms and effective parallelism.
- Deterministic — every step gives floating-point identical results between runs, regardless of the number of threads used.
- Complete toolkit — preprocessing, dimensionality reduction, harmonization, label transfer, clustering, embedding, pseudobulk differential expression, and plotting.
- Interoperable — reads and writes
.h5ad,.rds,.h5Seurat, and 10x files, and supports interleaving Python and R analyses via ryp without intermediate writes to disk. - Memory-efficient — ~2× lower peak memory than Scanpy by tabulating which cells pass QC, instead of subsetting to them.
- User-friendly — sensible defaults, strict type-checking, and solution-focused error messages.
Installation
brisc supports Linux, macOS, and Windows on Python 3.9+.
conda (recommended)
conda install -c conda-forge brisc
pip
pip install brisc
conda is recommended because it sets up the fast MKL BLAS and some of the R packages brisc uses. With pip, you'll need to handle those yourself: see the installation guide for details, including optional R integration (for differential expression, Seurat, and SingleCellExperiment support) via ryp.
Quick start
Basic workflow
from brisc import SingleCell
sc = SingleCell('data.h5ad')\
.qc()\
.hvg(batch_column='donor')\
.normalize()\
.pca()\
.neighbors()\
.shared_neighbors()\
.cluster(resolution=[0.25, 0.5, 1, 1.5, 2])\
.pacmap()
Label transfer
from brisc import SingleCell
sc_ref = SingleCell('data_ref.h5ad').qc()
sc_query = SingleCell('data_query.h5ad').qc()
sc_ref, sc_query = sc_ref.hvg(sc_query)
sc_ref = sc_ref.normalize()
sc_query = sc_query.normalize()
sc_ref, sc_query = sc_ref.pca(sc_query)
sc_ref, sc_query = sc_ref.harmonize(sc_query)
sc_query = sc_query.label_transfer_from(
sc_ref, 'cell_type')
Pseudobulk differential expression
from brisc import SingleCell
pb = SingleCell('data.h5ad')\
.qc()\
.pseudobulk('sample', 'cell_type')
de = pb\
.qc('condition')\
.library_size()\
.de('~ condition + sex + pmi')
3 maintainers
Release files for brisc 0.1.5
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Built distributions (wheels)
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