计算机科学
参数统计
推论
Python(编程语言)
外周血单个核细胞
人工智能
计算生物学
生物
数学
生物化学
统计
操作系统
体外
作者
Huanjing Hu,Zhen Feng,Haizhen Lin,Junjie Zhao,Yaru Zhang,Fei Xu,Lingling Chen,Feng Chen,Yunlong Ma,Xinyu Wang,Qi Zhao,Jianwei Shuai
摘要
Abstract The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.
科研通智能强力驱动
Strongly Powered by AbleSci AI