anndataoom-fast
Multithreaded C++ kernels for single-cell preprocessing, used by
omicverse as its default
backend='cpp'.
The kernels reproduce omicverse/scanpy's own algorithms — same QC metrics, size factors, Pearson-residual HVGs, scaling, PCA, UMAP graph and layout, Leiden and COSG markers — but run on all cores, and give the same result for any number of threads (fixed row blocks merged in a fixed order, no fast-math).
- Out-of-core (anndataoom) h5ad
files are read by the C++ side directly: h5py describes the chunk layout
once, then every thread
preads and inflates (libdeflate) its own chunks, bypassing libhdf5's global lock. - In memory scipy CSR matrices are wrapped without copying.
Install
pip install anndataoom-fast
Wheels are built for Linux (x86_64, aarch64), macOS (x86_64, arm64) and Windows (x86_64), CPython 3.9–3.13.
Use with omicverse
Nothing to do: omicverse uses it by default.
import omicverse as ov
ov.settings.backend = "cpp" # default; "python" restores the original code
ov.pp.qc(adata, backend="python") # or switch per call
Standalone
import anndataoom_fast as aof
aof.set_num_threads(16)
idx, dist = aof.knn(X_pca, 15) # IVF kNN, self first
G = aof.fuzzy_simplicial_set(idx, dist, 15) # = umap-learn
emb = aof.umap(G) # UMAP layout, all threads, deterministic
labels = aof.leiden(G, resolution=1.0, random_state=0) # = leidenalg
labels = aof.leiden_multi(G, [0.25, 0.5, 1.0], random_state=0)
labels = aof.leiden_brisc(G, resolution=1.0, random_state=0) # = brisc SingleCell.cluster
emb = aof.localmap(X_pca) # = brisc SingleCell.localmap (also aof.pacmap)
from anndataoom_fast import memory # in-memory CSR kernels
qc = memory.qc_metrics(adata.X, mt=adata.var_names.str.startswith("MT-"))
aof.install() routes anndataoom's chunked_* preprocessing functions
through the C++ engine in place.
Exactness
| step | vs |
|---|---|
| QC, normalize_total, HVG (pearson), scale | omicverse, identical / ≤1e-13 relative |
| PCA (in memory, covariance_eigh) | sklearn, same components up to sign |
| fuzzy simplicial set | umap-learn, bit-identical |
UMAP layout (sgd='parallel', default) |
same objective, edge schedule and negative sampling as umap-learn; same embedding for any thread count; sgd='sequential' = umap-learn's algorithm |
Leiden (leiden, RBConfiguration) |
leidenalg, identical partitions |
Leiden, brisc flavour (leiden_brisc) |
brisc, identical labels |
PaCMAP / LocalMAP (pacmap, localmap) |
brisc's algorithms; PaCMAP bit-identical to brisc given the same kNN; LocalMAP identical on every machine (brisc's own LocalMAP depends on the CPU's RSQRTSS) |
| COSG markers | omicverse, identical top-n lists |
| kNN | approximate (IVF), recall > 0.99; exact=True for brute force |
License
GPL-3.0-or-later. Leiden is ported from leidenalg/libleidenalg (GPL-3.0) and
igraph (GPL-2.0-or-later); the UMAP graph/layout follow umap-learn (BSD-3);
third_party/libdeflate is MIT. leiden_brisc is ported from
brisc (MIT, Copyright (c) 2026 Michael
Wainberg and Keon Arbabi), as are pacmap / localmap.
Parallel UMAP
umap-learn optimises the layout sequentially (or with Hogwild!, which is not
reproducible). sgd='parallel' keeps umap's update rule — each sampled edge
pulls its head and its tail together, followed by negative samples — and
parallelises it deterministically: vertices are visited in fixed blocks in
umap's own (head-sorted) edge order, each head is updated by one thread from
live coordinates, the tail moves are recorded and applied after the block in
edge order, and negative samples come from a hash of (seed, edge, epoch). On
SEA-AD (1.38M cells) it matches the sequential layout's kNN preservation
(0.0233 vs 0.0230) and gives identical embeddings for any number of threads.
1 maintainer
Metadata
1 maintainer
Release files for anndataoom-fast 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| anndataoom_fast-0.1.1.tar.gz | 687.7 kB | Without provenance | Details for anndataoom_fast-0.1.1.tar.gz |
Built distributions (wheels)
Total release size: 30.1 MB
Release files / anndataoom_fast-0.1.1.tar.gz
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| Download URL | anndataoom_fast-0.1.1-cp314-cp314-win_amd64.whl |
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| Size | 970.4 kB |
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| Size | 637.3 kB |
| Tags | CPython 3.12 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 |
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| Size | 788.0 kB |
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Release files / anndataoom_fast-0.1.1-cp311-cp311-macosx_11_0_arm64.whl
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Release files / anndataoom_fast-0.1.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
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Release files / anndataoom_fast-0.1.1-cp310-cp310-macosx_11_0_x86_64.whl
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Release files / anndataoom_fast-0.1.1-cp39-cp39-win_amd64.whl
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Release files / anndataoom_fast-0.1.1-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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Release files / anndataoom_fast-0.1.1-cp39-cp39-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
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Release files / anndataoom_fast-0.1.1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
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Release files / anndataoom_fast-0.1.1-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
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Release files / anndataoom_fast-0.1.1-cp39-cp39-macosx_11_0_x86_64.whl
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Release files / anndataoom_fast-0.1.1-cp39-cp39-macosx_11_0_arm64.whl
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