北京雁栖湖应用数学研究院

丘成栋

孙楠

2024年03月11日 15:00 至 15:30

理科楼A-304

Emerging single-cell technologies provide high-resolution measurements of distinct cellular modalities opening new avenues for generating detailed cellular atlases of many and diverse tissues. The high dimensionality, sparsity, and inaccuracy of single cell sequencing measurements, however, can obscure discriminatory information, mask cellular subtype variations and complicate downstream analyses which can limit our understanding of cell function and tissue heterogeneity. Here, we present a novel pre-processing method (scPSD) inspired by power spectral density analysis that enhances the accuracy for cell subtype separation from large-scale single-cell omics data. We comprehensively benchmarked our method on a wide range of single-cell RNA-sequencing datasets and showed that scPSD pre-processing, while being fast and scalable, significantly reduces data complexity, enhances cell-type separation, and enables rare cell identification. Additionally, we applied scPSD to transcriptomics and chromatin accessibility cell atlases and demonstrated its capacity to discriminate over 100 cell types across the whole organism and across different modalities of single-cell omics data.

孙楠目前是北京雁栖湖应用数学研究院的博士后。她的研究方向包括生物信息学、机器学习和应用数学，在The Innovation, Computational and Structural Biotechnology Journal, BMC Bioinformatics, Frontiers in Cellular and Infection Microbiology, Journal of Computational Biology, Genes等期刊发表多篇论文，参与多项国家自然科学基金及北京市自然科学基金项目，主持中国博士后科学基金第78批面上资助。