Recent foundation models for single-cell transcriptomic data generate informative, context-aware gene and cell representations. Spatial transcriptomic (ST) data offer extra positional insights but were not considered by these single-cell models. We introduce stFormer, a transformer model tailored for ST data, which emp...
SPgen integrates protein sequences, functional annotations, transcriptomic profiles, and spatial information to learn transferable representations that enable inference beyond experimentally profiled proteins, and accurately reconstructs measured spatial patterns, reduces measurement noise, and enables proteome-wide sp...
Jia-Chen Li, Kai-Yuan Yang, Qiaoling Che et al.· bioRxiv· 0 citations
This work proposes a multimodal framework for learning subcellularly resolved cell embeddings by jointly leveraging RNA expression profiles, protein sequence representations, and protein structural information, and is the first framework that produces subcellularly resolved cell embeddings by jointly incorporating tran...
Zhen Zhou, Jia-Chen Li, Yuan Liu et al.· 0 citations
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