scJET: Full-gene Space Single-cell Expression Generation with Patch-based Transformer Modeling
Abstract
Most single-cell generative models rely on highly variable genes (HVGs) or low-dimensional latent representations, limiting their capacity to capture the complexity of full-gene features. We present scJET, a patch-based Transformer denoising framework that operates in full-gene space. scJET preserves global manifold structure, local neighborhood statistics, and gene-level expression programs. By combining scalable patch tokenization with full-gene denoising, scJET provides an efficient framework for transcriptome-wide single-cell matrix generation.
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
scJET: Full-gene Space Single-cell Expression Generation with Patch-based Transformer Modeling [new] leverages patch tokenization for denoising, generating transcriptome-wide matrices preserve global/local structures & gene programs.
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#SingleCell preprints @prepub-singlecell.bsky.social · 721 followers neutral
scJET: Full-gene Space Single-cell Expression Generation with Patch-based Transformer Modeling #SingleCell 🧪🧬🖥️ https://www.biorxiv.org/content/10.64898/2026.07.06.736701v1