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Related Experiment Videos

LISA2: Learning Complex Single-Cell Trajectory and Expression Trends.

Yang Chen1, Yuping Zhang2,3, James Y H Li3,4

  • 1Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States.

Frontiers in Genetics
|September 13, 2021
PubMed
Summary

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LISA2 is a new computational method for analyzing single-cell data. It efficiently maps cell development trajectories using transcriptomics and epigenomics, improving the study of cell differentiation and rare cell populations.

Area of Science:

  • Computational Biology
  • Single-Cell Genomics
  • Developmental Biology

Background:

  • Single-cell transcriptional and epigenomics profiling are crucial for understanding cell types, differentiation, and gene regulation.
  • Existing trajectory inference methods like Monocle 2/3, URD, and STREAM focus on tree-based structures.
  • There is a need for flexible and efficient methods applicable to diverse single-cell data types.

Purpose of the Study:

  • To introduce LISA2, a novel, fast, and flexible method for single-cell trajectory learning.
  • To address limitations in current methods by incorporating specified root and leaf nodes for complex trajectories.
  • To enable trajectory inference for both transcriptomics and epigenomics single-cell data.

Main Methods:

  • Developed LISA2, a trajectory learning method incorporating specified root and leaf nodes.
Keywords:
developmentexpression trendspseudo timescATAC-seqscRNA-seqtrajectory

Related Experiment Videos

  • Utilized 3D Landmark ISOmetric feature MAPping (L-ISOMAP) for visualizing complex developmental trajectories.
  • Applied LISA2 to simulated and real single-cell transcriptomics and single-cell assay for transposase-accessible chromatin (scATAC-seq) datasets.

    • Main Results:

    • LISA2 efficiently estimates single-cell developmental trajectories, including for rare cell populations and adjacent terminal cell states.
    • The method demonstrates applicability across different molecular data types (transcriptomics and epigenomics).
    • Successful application to cerebellum, diencephalon, and hematopoietic stem cell datasets.

    Conclusions:

    • LISA2 offers an efficient and flexible approach for single-cell trajectory inference.
    • The method enhances the analysis of complex developmental processes and diverse cell types.
    • LISA2 is a valuable tool for single-cell data analysis in transcriptomics and epigenomics.