Installation#

brisc supports Linux, macOS, and Windows on Python 3.9+. Install it with conda or pip:

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 (both covered below); with pip you must handle those yourself.

R packages#

brisc’s R integration is optional — you need it only for differential expression or for working with Seurat and SingleCellExperiment objects. Skip this section if you do neither.

It runs through ryp, which bridges Python and R via R’s arrow package, so arrow is always required. Each feature then adds one package: limma for differential expression, and Seurat or SingleCellExperiment for the corresponding objects.

conda handles this for you — conda install -c conda-forge brisc installs R, arrow, and Seurat. With pip you do it yourself: install R using CRAN’s per-platform instructions, then run install.packages(c("arrow", "Seurat")) in an R session.

For differential expression, install limma:

conda (Linux/macOS)

conda install -c bioconda bioconductor-limma

BiocManager (all platforms)

R -e 'if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager"); BiocManager::install("limma")'

For SingleCellExperiment data, install it:

conda (Linux/macOS)

conda install -c bioconda bioconductor-singlecellexperiment

BiocManager (all platforms)

R -e 'if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager"); BiocManager::install("SingleCellExperiment")'

BLAS and threading#

A few key steps — nearest-neighbor search, harmonization, and label transfer — rely on BLAS. On machines with x86 processors (most Linux and Windows machines), conda allows SciPy to be installed with MKL BLAS, which is highly optimized. (You can install this manually with conda install "libblas=*=*mkl" scipy, although installing brisc through conda takes care of this for you.)

However, pip’s SciPy comes with OpenBLAS, which is less optimized and only supports up to 64 threads. To check which backend you have:

import brisc
from threadpoolctl import threadpool_info
print(sorted({pool['internal_api'] for pool in threadpool_info()}))

mkl is the fast path; openblas means the 64-thread cap applies.