Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

Abstract Accurate and comprehensive extraction of information from high-dimensional single cell datasets necessitates faithful visualizations to assess biological populations. A state-of-the-art algorithm for non-linear dimension reduction, t-SNE, requires multiple heuristics and fails to produce clear representations of datasets when millions of cells are projected. We develop opt-SNE, an automated toolkit for t-SNE parameter selection that utilizes Kullback-Leibler divergence evaluation in real time to tailor the early exaggeration and overall number of gradient descent iterations in a dataset-specific manner. The precise calibration of early exaggeration together with opt-SNE adjustment of gradient descent…

• IS
  International Society for Advancement of Cytometry
• UL
  Universiteit Leiden
• LU
  Leids Universitair Medisch Centrum