arXiv:2506. 22228v2 Announce Type: replace-cross Abstract: Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
arXiv:2606. 07760v1 Announce Type: new Abstract: Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design.
LapDDPM is a conditional Graph Diffusion Probabilistic Model that generates high‑fidelity, biologically plausible single‑cell RNA sequencing data. It incorporates graph‑based inductive biases and a spectral adversarial perturbation mechanism to enforce robustness against structural noise, effectively acting as a Distributionally Robust Optimization framework. The model extends to spatial transcriptomics and multi‑modal data, and experimental results on datasets such as PBMC3K, Dentate Gyrus, HLCA, Visium, and 10x Multiome show it outperforms state‑of‑the‑art baselines in distribution matching, manifold preservation, and downstream utility.
arXiv:2606. 27752v1 Announce Type: new Abstract: Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions.
PopPert is a framework that models population-level joint gene expression distributions to predict transcriptional responses to perturbations in single-cell RNA sequencing data. By using a low‑rank Gaussian Copula, it captures gene co‑expression patterns and eliminates the need for cell‑to‑cell correspondence, thereby reducing sensitivity to single‑cell noise. Across multiple benchmarks, PopPert outperforms existing methods in differential expression recovery, perturbation effect estimation, and distribution matching, demonstrating the effectiveness of population‑level joint distribution learning for unpaired single‑cell data.
arXiv:2606. 11286v1 Announce Type: cross Abstract: High-content imaging assays quantify cellular responses to chemical and genetic perturbations, yet continuous trajectories of individual cells are unobservable because cells are chemically fixed at acquisition.