PT  - JOURNAL ARTICLE
AU  - Mayank Sharma
AU  - Huipeng Li
AU  - Debarka Sengupta
AU  - Shyam Prabhakar
AU  - Jayadeva
TI  - FORKS: Finding Orderings Robustly using k-means and Steiner trees
AID  - 10.1101/132811
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 132811
4099  - http://biorxiv.org/content/early/2017/06/20/132811.short
4100  - http://biorxiv.org/content/early/2017/06/20/132811.full
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.