Unsupervised Inference of Developmental Directions for Single Cells Using VECTOR
Feng Zhang, Xiaoying Li, Weidong Tian
Abstract
A key step in trajectory inference is the determination of starting cells, which is typically done by using manually selected marker genes. In this study, we find that the quantile polarization of a cell's principal-component values is strongly associated with their respective states in development hierarchy, and therefore provides an unsupervised solution for determining the starting cells. Based on this finding, we developed a tool named VECTOR that infers vectors of developmental directions for cells in Uniform Manifold Approximation and Projection (UMAP). In seven datasets of different developmental scenarios, VECTOR correctly identifies the starting cells and successfully infers the vectors of developmental directions. VECTOR is freely available for academic use at https://github.com/jumphone/Vector.