LISA2: Learning Complex Single-Cell Trajectory and Expression Trends - Chen, Yang; Zhang, Yuping; Li, James Y. H.; Ouyang, Zhengqing (Frontiers in Genetics, 2021)
Single cell transcriptional and epigenomics profiles have been applied in a variety of tissues and diseases for discovering new cell types, differentiation trajectories, and gene regulatory networks. Many methods such as Monocle 2/3, URD, STREAM have been developed for tree-based trajectory building. Here, we propose a fast and flexible trajectory learning method LISA2 for single cell data. This new method has two distinctive features: (1) apply specified leaves and root to relieve the complexity to build development trajectory especially for some special cases like rare cell populations and adjacent terminal cell states; and (2) make it applicable for both transcriptomics and epigenomics data. Here, we propose a fast and flexible trajectory learning method LISA2 for single cell data. LISA2 can build tree trajectories with specified leaves and root for complex trajectories and visualize it with 3D Landmark ISOmetric feature MAPping (L-ISOMAP). We apply LISA2 in simulation and real datasets with thousands of cells including both single cell transcriptomic data and single cell ATAC-seq data. LISA2 is efficient in estimating single cell trajectory and expression trends for different kinds of molecular state of cells.