scTrilemma：单细胞表示学习中身份、不变性与保真度的平衡
scTrilemma: Balancing Identity, Invariance, and Fidelity in Single-Cell Representation Learning
浏览论文内容
中文总结 AI 辅助
scTrilemma提出一种潜在瓶颈VAE，通过路由变异至嵌入、解码器或先验，在无标签条件下同时满足单细胞表示的身份、不变性和保真度，并在零样本评估中领先。
中文摘要 AI 辅助
单细胞RNA测序表示学习本质上是无标签的：细胞身份、状态和上下文并非固定的训练目标，因此什么是信号或噪声取决于分析任务。因此，一个单一的表示必须保留生物学身份和状态，对噪声上下文保持鲁棒性，并保留表达分析所需的基因水平变异，这三个需求我们称之为表示三难困境。为了解决这个问题，我们引入了scTrilemma，一种潜在瓶颈变分自编码器，它将表达衍生的变异路由到嵌入、解码器或先验中，而不是强制所有变异都通过一个嵌入。它通过表达对基因令牌进行门控，通过解码器路由细胞表示，并在无标签的伪批量上下文上条件化先验，在单一重建目标下，无需目标注释或辅助表示损失。在连续CZ CELLxGENE Census版本上的基于发布的零样本评估中，scTrilemma同时满足所有三个需求，并在多种疾病设置中保留生物学状态、差异表达和通路结构。潜在干预进一步表明，上下文可以几乎不损失其他需求地被移除，留下身份与保真度之间的剩余张力。代码在此https URL公开可用。
英文摘要
Single-cell RNA-seq representation learning is fundamentally label-free: cell identities, states, and contexts are not fixed training targets, so what constitutes signal or nuisance is analysis-dependent. A single representation must therefore preserve biological identity and state, remain robust to nuisance context, and retain the gene-level variation needed for expression analysis, three demands we call the representation trilemma. To tackle this problem, we introduce scTrilemma, a latent-bottleneck VAE that routes expression-derived variation to the embedding, the decoder, or the prior rather than forcing all of it through one embedding. It gates gene tokens by expression, routes the cell representation through the decoder, and conditions the prior on unlabeled pseudo-bulk context, under a single reconstruction objective and without target annotations or auxiliary representation losses. In release-based zero-shot evaluation on successive CZ CELLxGENE Census releases, scTrilemma leads all three demands at once and preserves biological-state, differential-expression, and pathway structure across multiple disease settings. Latent interventions further show that context can be removed at almost no cost to the other demands, leaving identity against fidelity as the remaining tension. Code is publicly available at https://github.com/yunhak0/scTrilemma.
发表机构
- KAIST（韩国科学技术院）
- HITS（海德堡理论研究所）
机构由 AI 辅助整理，请以论文原文为准。
