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')