  Single cell RNA sequencing (scRNA-seq) data makes studying the development of
cells possible at unparalleled resolution. Given that many cellular
differentiation processes are hierarchical, their scRNA-seq data is expected to
be approximately tree-shaped in gene expression space. Inference and
representation of this tree-structure in two dimensions is highly desirable for
biological interpretation and exploratory analysis. Our two contributions are
an approach for identifying a meaningful tree structure from high-dimensional
scRNA-seq data, and a visualization method respecting the tree-structure. We
extract the tree structure by means of a density based minimum spanning tree on
a vector quantization of the data and show that it captures biological
information well. We then introduce DTAE, a tree-biased autoencoder that
emphasizes the tree structure of the data in low dimensional space. We compare
to other dimension reduction methods and demonstrate the success of our method
experimentally. Our implementation relying on PyTorch and Higra is available at
github.com/hci-unihd/DTAE.
