This release is a pre-release and may not be stable for production use.
ARIELSpatial
ARIEL is a library for spatial multiomic integration with modular encoders, graph backbones, multimodal aggregators, probabilistic decoders, loss composition, clustering, profiling, and reproducible seeding.
What this release contains
Version 0.1.0 ships the data-preparation and seeding layers:
ARIELSpatial.preprocessing: RNA preprocessing with Seurat SCTransform and PCA, ADT CLR normalization and PCA, in-tissue spot filtering with spatial coordinates, and barcode and sparse-matrix utilities.ARIELSpatial.seed: process-wide seeding and namespaced Python, NumPy, and PyTorch random generators, including DataLoader worker seeding.
Installation
Releases are currently alpha pre-releases, so pip needs --pre:
pip install --pre arielspatial
The package is imported as ARIELSpatial:
import ARIELSpatial
Requirements
-
Python 3.10 or newer on Linux.
-
An R installation with the Seurat, SeuratObject, sctransform, Matrix, dplyr, and SingleCellExperiment packages. RNA preprocessing runs Seurat SCTransform through
rpy2andanndata2ri. -
Optional GPU acceleration: an NVIDIA GPU with a CUDA 13 runtime and the
gpuextra, which installscupy-cuda13xandrapids-singlecell-cu13:pip install --pre "arielspatial[gpu]"
License
MIT. See LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for arielspatial 0.1.0a14
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