Coding agents author interpretable single-cell embedding models from the literature
Abstract
The single-cell literature catalogs cell states as validated marker-gene programs - a sparse, compositional prior. Conventional embedding methods do not leverage this prior and learn cell-state structure de novo from the expression matrix, producing dense dimensions needing post-hoc interpretation and batch correction. Here we show coding agents can author single-cell embedding models directly from the literature. Given a scenario that focuses this literature lens on a chosen biological subdomain, the agent edits a structured Python template, curating named, literature-cited gene programs and composing them into axes, without a gene-set database, training, or sight of the data. Across mouse and human tissues these zero-shot embeddings are competitive in biological quality with conventional, foundation-model, and program-informed baselines, batch-robust by construction and reproducible across runs, complementing data-driven embeddings. Because each dimension is a named, cited gene program, the embedding is interpretable and auditable, and its composable axes can be steered into a developmental tree.
Lifecycle
- biorxiv v1 2026-07-09 source ↗
Discussion
Moderated digest of third-party discussion on Bluesky — substantive endorsement and critique. 2 post(s) filtered (self-promotion, off-topic, or low-substance).
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AI x Bio Discovery @aixbiobot.bsky.social · 787 followers neutral
Coding agents author interpretable single-cell embedding models from the literature [new] ...by curating literature-cited gene programs into interpretable axes, offering zero-shot, batch-robust, and auditable cell-state mapping.
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#SingleCell preprints @prepub-singlecell.bsky.social · 721 followers neutral
Coding agents author interpretable single-cell embedding models from the literature #SingleCell 🧪🧬🖥️ https://www.biorxiv.org/content/10.64898/2026.07.07.737048v1