About AutoZyme
AutoZyme is a framework for producing drop-in performance upgrades to widely used scientific toolkits. It combines two ingredients:
- An autonomous-research loop that proposes, implements, and benchmarks candidate optimizations.
- A concordance gate that rejects any change whose outputs diverge from the upstream baseline beyond a method-appropriate tolerance.
Current releases
SeuratTurbo— R package, drop-in patches for Seurat v5.x · github.com/ElliotXie/seurat-turboScanpyTurbo— Python package, drop-in patches for Scanpy v1.11.x · github.com/ElliotXie/scanpy-turbo
Datasets
Every benchmark is run on at least one of the following publicly available single-cell datasets, spanning four orders of magnitude in cell count:
| Dataset | Cells | Source | Used for |
|---|---|---|---|
ifnb |
14k | Kang et al., Nature Biotechnology 2018 · GSE96583 | Integration (CCA / RPCA), SCTransform |
pbmc68k |
68k | Zheng et al., Nature Communications 2017 · 10x Genomics | Small-scale benchmark (all core methods) |
pbmc200k_glaucoma |
208k | CZ CELLxGENE · Human PBMC Glaucoma Atlas | Medium-scale benchmark, batch HVG |
heart_adult |
486k | Litviňuková et al., Nature 2020 | Large-scale benchmark (>36 GB RAM) |
Authors
Contributor list is updated with each release — see individual repository
CONTRIBUTING.md files and commit history. If you'd like to be listed,
open a PR.
Code
AutoZyme is fully open source. The three canonical repositories are:
- ElliotXie/autozyme — umbrella project: framework, benchmark harness, manuscript, this website.
- ElliotXie/seurat-turbo — the shipped R package.
- ElliotXie/scanpy-turbo — the shipped Python package.
Contributions are welcome. If you want to propose a toolkit to
accelerate, use the Suggest & Vote page. If you want
to submit an optimization for a method that's already in-scope,
open a pull request on the relevant package repo — see its CONTRIBUTING.md
for the benchmark contract (pinned environment, dataset, concordance metric, and
tolerance) a submission must satisfy.
How we choose what to work on next
On the Suggest & Vote page, anyone can nominate a toolkit or method. Each nomination can be upvoted, downvoted, and commented on. We periodically review the ranking and pick top-voted entries to optimize next. No fixed cadence — progress depends on the complexity of the method.
How benchmarks are reported
Every benchmark row is one method × dataset × thread count combination, run on a fixed hardware profile. The optimized run must pass a concordance check to be reported at all. The Benchmarks page shows the raw numbers for every run; the homepage aggregates them into per-method best speedups.
Status
AutoZyme is under active development. Details of the search loop and the results shown here are the subject of an in-progress manuscript.
How to cite
@misc{autozyme2026,
title = {AutoZyme: Autonomous-Research-Driven Speedups for Scientific Toolkits},
author = {The AutoZyme Team},
year = {2026},
note = {Manuscript in preparation}
}