Empirical and computational solutions for multi-omics single-cell assays

新兴的单细胞测序技术正在改变分子细胞生物学，但识别新的细胞类型及其功能需要整合高度异质性的数据。由于缺乏高质量的数据集，能够提取生物相关结果的计算方法的发展受到阻碍。该项目旨在开发新的测序方法，并生成数据，以推动我们的降维多变量方法的数据集成开发。通过将计算机模拟和体内方法相结合，该项目有望通过提供有用的协议和工具来产生细胞生物学的新见解，使愿意从事尖端单细胞研究的科学家受益。

Emerging single-cell sequencing technologies are transforming molecular cell biology, but identifying novel cell types and their functions requires the integration of highly heterogeneous data. The development of computational methods able to extract biologically relevant results is hindered by the lack of high-quality datasets. This project aims to develop novel sequencing methodologies and generate data to drive our dimension reduction multivariate method developments for data integration. By combining in silico and in vivo approaches, the project is anticipated to benefit scientists willing to work in cutting-edge single-cell research by providing useful protocols and tools to generate novel insights in cell biology.