A fast, scalable and versatile tool for analysis of single-cell omics data

Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python…

• LI
  Ludwig Institute for Cancer Research
• WU
  Westlake University
• NI
  National Institutes of Health

  Awards: UM1HG011585, U19MH114831, R01 EY031663, UM1MH130994, U54AG079758, U01MH114828, U01MH121282, U01HG012059, RF1MH128838, R24AG073198
• UO
  University of California, San Diego