CellMSA: Context Modeling for Single-Cell Representation Learning
Single-cell transcriptomics enables profiling of cellular states at unprecedented resolution, but its high dimensionality, sparsity, and technical batch effects pose significant challenges for representation learning. Existing single-cell foundation models typically encode each cell independently or only model cells from the same batch for denoising, thereby underutilizing the rich relational information across batches and cell types to model gene expression patterns. We argue that single-cell models can benefit from more informative cell-context modeling. By comparing consistency and variation across cells, models can capture fine-grained gene-gene dependencies associated with cell state...
| Authors | Suyuan Zhao, Minghao Liu, Yizhen Luo et al. |
|---|---|
| Primary category | q-bio.GN |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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