Transcompp: understanding phenotypic plasticity by estimating Markov transition rates for cell state transitions
Abstract
Gradual population-level changes in tissues can be driven by stochastic plasticity, meaning rare stochastic transitions of single-cell phenotype. Quantifying the rates of these stochastic transitions requires time-intensive experiments, and analysis is generally confounded by simultaneous bidirectional transitions and asymmetric proliferation kinetics. To quantify cellular plasticity, we developed Transcompp (Transition Rate ANalysis of Single Cells to Observe and Measure Phenotypic Plasticity), a Markov modeling algorithm that uses optimization and resampling to compute best-fit rates and statistical intervals for stochastic cell-state transitions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 23, 2020
- Source ID
- 10.1093/bioinformatics/btaa021
Entities
People
- Lisa Tucker-Kellogg
- Marie-Veronique Clement
- Mario O Ihsan
- N Suhas Jagannathan
- Roy E. Welsch
- Xiao Xuan Kin
Organizations
- Duke–NUS Medical School
- Massachusetts Institute of Technology
- National University Health System
- Naval Medical Research Center
- St. Baldrick's Foundation