The paper introduces scTrilemma, a latent-bottleneck variational autoencoder designed to address the representation trilemma in single‑cell RNA‑seq data: preserving biological identity and state, remaining robust to nuisance context, and retaining gene‑level variation for expression analysis. scTrilemma routes expression‑derived variation to the embedding, decoder, or prior, gating gene tokens by expression and conditioning the prior on unlabeled pseudo‑bulk context, all under a single reconstruction objective without target annotations. In zero‑shot evaluations on successive CZ CELLxGENE Census releases, scTrilemma simultaneously satisfies all three demands, maintaining biological state, differential‑expression, and pathway structure across multiple disease settings, and latent interventions show context can be removed with minimal impact on other demands.
By Yunhak Oh, Yoonho Lee, Junseok Lee, Namkyeong Lee, Sang-Yeon Hwang, Yinhua Piao, Hyomin Kim, Seonghwan Kim, Jaechang Lim, Woo Youn Kim, Sungsoo Ahn, Chanyoung Park
arXiv:2605.07938v2 Announce Type: replace
Abstract: Single-cell representation learning (SCRL) from gene expression data offers a way to uncover the complex regulatory logic underlying cellular funct...
By Sachini Weerasekara, Natasha Darras, Sagar Kamarthi, Colles Price, Jacqueline Isaacs
CellMSA introduces a novel single‑cell representation learning framework that leverages a multiple‑sequence‑alignment‑inspired context model. For each target cell, it retrieves relevant cells across batches and related cell types, summarizing cross‑cell patterns into a context‑dependent gene‑pair representation that is fed into a pair‑aware encoder. Pretraining on a massive human single‑cell corpus (≈109 million cells) and subsequent benchmarks demonstrate consistent performance gains over existing methods.
By Suyuan Zhao, Minghao Liu, Yizhen Luo, Zaiqing Nie
arXiv:2606. 07760v1 Announce Type: new Abstract: Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design.
By Alma Andersson, Aya Abdelsalam Ismail, Edward De Brouwer, Doron Haviv, Tommaso Biancalani, Kyunghyun Cho, Gabriele Scalia, A\"icha BenTaieb, Hector Corrada Bravo
arXiv:2608. 00985v1 Announce Type: new Abstract: The rapid growth of single-cell transcriptomic data has enabled the development of foundation models pretrained primarily by reconstructing masked expression values.
By Jiaqi Xiong, Yuntao hu, Yu Zheng, Yifei Shi, Xinyue Guo, Jiaxin Qi
arXiv:2603. 15263v2 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) has revolutionized representation learning, with Joint-Embedding Architectures (JEAs) emerging as an effective approach for capturing semantic features.
By Konstantinos Almpanakis, Anna Kreshuk