[Lecture] Jingsi Ming: FIRM: Fast integration of single-cell RNA-sequencing data across Multiple platforms
Time: 15:00-16:00pm, Oct. 14th, 2020, Wednesday
Venue: RoomA1716, Science Building, North Zhongshan Road Campus
Spreker: Jingsi Ming, Assistant Professor, Academy of Statistics and Interdisciplinary Science, East China Normal University
Abstract:Single-cell RNA-sequencing (scRNA-seq) has now been used extensively to discover novel cell types and reconstruct evelopmental trajectories by measuring mRNA expression patterns of individual cells. However, datasets collected using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms, are difficult to compare because of their heterogeneity. Each platform has unique advantages, and integration of these datasets would provide deeper insights into cell biology and gene regulation. Through comprehensive data exploration, we found that accurate integration is often hampered by differences in cell-type compositions. Herein we describe FIRM, an algorithm that addresses this problem and achieves efficient and accurate integration of heterogeneous scRNA-seq datasets across multiple platforms.We applied FIRM to numerous scRNA-seq datasets generated using SS2 and 10X from mouse, mouse lemur, and human, comparing its performance in dataset integration with other state-of-the art methods. The integrated datasets generated using FIRM show accurate mixing of shared cell type identities and superior preservation of original structure for each dataset. FIRM not only generates robust integrated datasets for downstream analysis, but is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a versatile and indispensable tool for scRNA-seq analysis.
