Yuzhe Li, Ph.D.

Yuzhe Li, Ph.D.

AI Biology

Talks and presentations

Poster on Single-Cell data Analysis via Perturbation Estimation

October 27, 2023

Poster, The 12th National Conference on Bioinformatics and Systems Biology of China, Qingdao, China

RNA velocity provides the directional information of cell-state dynamics and transitions from single-cell RNA-sequencing data. To reveal cellular transitions and perturb this process in silico, models capable of unveiling the causal effects of complex gene regulatory interactions are required. Here, we present SCAPE, a deep learning-based model that predicts the transcription dynamics under in silico genetic perturbations. SCAPE is uniquely able to predict in silico post-perturbation RNA velocities, which can be directly used to predict cell-fate diversions induced by the genetic perturbation of certain genes. We applied SCAPE to predict cell-fate diversions after in silico genetic perturbations in neurogenesis, gastrulation, and pancreatic endocrinogenesis. We validated the efficacy and accuracy of SCAPE in recapitulating well-characterized cell-state transitions induced by gene perturbations. Overall, SCAPE will facilitate the biomedical study of cell-fate decisions and cell-state transitions.

Poster on Tissue module discovery in single-cell resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding.

February 25, 2023

Poster, The 11th National Conference on Bioinformatics and Systems Biology of China, Guangzhou, China

Computational methods are desired for single-cell-resolution spatial transcriptomics (ST) data analysis to uncover spatial organization principles for how individual cells exert tissue-specific functions. Here, we present ST data analysis via interaction-aware cell embedding (SPACE), a deep-learning method for cell-type identification and tissue module discovery from single-cell-resolution ST data by learning a cell representation that captures its gene expression profile and interactions with its spatial neighbors. SPACE identified spatially informed cell subtypes defined by their special spatial distribution patterns and distinct proximal-interacting cell types. SPACE also automatically discovered “cell communities”—tissue modules with discernible boundaries and a uniform spatial distribution of constituent cell types. For each cell community, SPACE outputs a characteristic proximal cell-cell interaction network associated with physiological processes, which can be used to refine ligand-receptor-based intercellular signaling analyses. We envision that SPACE can be used in large-scale ST projects to understand how proximal cell-cell interactions contribute to emergent biological functions within cell communities.