Abstract
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, and STREAM have been developed for tree-based trajectory building. Here, we propose a fast and flexible trajectory learning method, LISA2, for single-cell data analysis. This new method has two distinctive features: (1) LISA2 utilizes specified leaves and root to reduce the complexity for building the developmental trajectory, especially for some special cases such as rare cell populations and adjacent terminal cell states; and (2) LISA2 is applicable for both transcriptomics and epigenomics data. LISA2 visualizes complex trajectories using 3D Landmark ISOmetric feature MAPping (L-ISOMAP). We apply LISA2 to simulation and real datasets in cerebellum, diencephalon, and hematopoietic stem cells including both single-cell transcriptomics data and single-cell assay for transposase-accessible chromatin data. LISA2 is efficient in estimating single-cell trajectory and expression trends for different kinds of molecular state of cells.
Highlights
We apply LISA2 to a single-cell assay for transposase-accessible chromatin dataset from the human hematopoietic [hematopoietic stem cell (HSC)] system to show its potential applications on single-cell epigenome data (Chen H. et al, 2019) we further show the capability of LISA2 in identifying rare cell types
LISA2 builds the k-nearest neighbors (kNN) graph and utilizes community detection methods for clustering based on principal component analysis (PCA)
We developed LISA2 for single-cell trajectory analysis with user-defined root and leaf clusters
Summary
The fast development of single-cell sequencing technologies has impacted the studies of transcriptomics (Briggs et al, 2018; Farrell et al, 2018; Hochgerner et al, 2018), epigenomics (Rotem et al, 2015; Clark et al, 2018; Gaiti et al, 2019; Sinnamon et al, 2019), proteomics (Palii et al, 2019; Specht et al, 2019), and multiple-omics (Pott, 2017; Bian et al, 2018; Ren et al, 2018; Gu et al, 2019; Liu et al, 2019). Many algorithms for estimating cell trajectory have been developed (Ji and Ji, 2016; Liu et al, 2017; Perraudeau et al, 2017; Qiu et al, 2017; Chen et al, 2018; Farrell et al, 2018; Lummertz da Rocha et al, 2018; Street et al, 2018; Cao et al, 2019; Chen H. et al, 2019; Saelens et al, 2019; Setty et al, 2019; Wolf et al, 2019) based on single-cell gene expression data. To find an improved way to solve non-divergent trajectories, we have developed a fast and flexible trajectory learning method, LISA2, which provides an efficient solution to construct a spanning tree structure by specifying the root and tips. We compare LISA2 with URD, Monocle 2, and STREAM on the simulation dataset to show the advantages of LISA2 (Papadopoulos et al, 2019)
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