RT Journal Article
SR Electronic
T1 FORKS: Finding Orderings Robustly using k-means and Steiner trees
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 132811
DO 10.1101/132811
A1 Mayank Sharma
A1 Huipeng Li
A1 Debarka Sengupta
A1 Shyam Prabhakar
A1 Jayadeva
YR 2017
UL http://biorxiv.org/content/early/2017/06/20/132811.abstract
AB Recent advances in single cell RNA-seq technologies have provided researchers with unprecedented details of transcriptomic variation across individual cells. However, it has not been straightforward to infer differentiation trajectories from such data, due to the parameter-sensitivity of existing methods. Here, we present Finding Orderings Robustly using k-means and Steiner trees (FORKS), an algorithm that pseudo-temporally orders cells and thereby infers bifurcating state trajectories. FORKS, which is a generic method, can be applied to both single-cell and bulk differentiation data. It is a semi-supervised approach, in that it requires the user to specify the starting point of the time course. We systematically benchmarked FORKS and eight other pseudo-time estimation algorithms on six benchmark datasets, and found it to be more accurate, more reproducible, and more memory-efficient than existing methods for pseudo-temporal ordering. Another major advantage of our approach is its robustness – FORKS can be used with default parameter settings on a wide range of datasets.