The specious art of single-cell genomics

Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to 2 or 3 dimensions to produce "all-in-one" visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to 2, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data and…

• CI
  California Institute of Technology
• RS
  Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology
• NI
  National Institutes of Health

  Award: U19MH114830
• NI
  National Institute of Mental Health

  Award: U19MH114830