Seminar| Institute of Mathematical Sciences
Abstract: Single-cell RNA-sequencing (scRNA-seq) is being used extensively to measure the mRNA expression of individual cells from deconstructed tissues, organs and even entire organisms to generate cell atlas references, leading to discoveries of novel cell types and deeper insight into biological trajectories. Inherent heterogeneities between platforms, tissues and other batch effects make scRNA-seq data difficult to compare and integrate, especially in large-scale cell atlas efforts; yet,accurate integration is essential for gaining deeper insights into cell biology. We present two integration methods, FIRM and DeepMap. FIRM is a re-scaling algorithm which accounts for the effects of cell type compositions, and achieve accurate integration of scRNA-seq datasets across multiple tissue types, platforms and experimental batches. DeepMap is a deep learning based method using iterative cell matching and structure preservation, and is able to handle multiple scenarios including different platforms, species and modalities.