Abstract
Dimensionality reduction is standard practice for filtering noise and identifying relevant dimensions in large-scale data analyses. In biology, single-cell expression studies almost always begin with reduction to two or three 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 analysis of cell relationships. However, there is little theoretical support for this practice. We examine the theoretical and practical implications of low-dimensional embedding of single-cell data, and find extensive distortions incurred on the global and local properties of biological patterns relative to the highdimensional, ambient space. In lieu of this, we propose semi-supervised dimensionality reduction to higher dimension, and show that such targeted reduction guided by the metadata associated with single-cell experiments provides useful latent space representations for hypothesis-driven biological discovery.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Updated analysis and explanation of two-dimensional embeddings on a greater variety of metrics. Supplemental files updated accordingly.
