Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However,
dynamic inference of an evolving cell population from time series scRNA-seq data is …

Motivation Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for
studying cellular decision-making processes. Robust inference of cell state transition paths …

Time-series single-cell RNA sequencing (scRNA-seq) datasets provide unprecedented
opportunities to learn dynamic processes of cellular systems. Due to the destructive nature …

Existing computational methods that use single-cell RNA-sequencing for cell fate prediction
either summarize observations of cell states and their couplings without modeling the …

Modeling cellular processes in the framework of dynamical systems theories is a focused
area in systems and mathematical biology, but a bottleneck to extend the efforts to genome …

Recent advances in single-cell sequencing technology have revolutionized our ability to
acquire whole transcriptome data. However, uncovering the underlying transcriptional …

Motivation: Learning cellular dynamics through reconstruction of the underlying cellular
potential energy landscape (aka Waddington landscape) from time-series single-cell RNA …

Cell fate decisions play a pivotal role in development, but technologies for dissecting them
are limited. We developed a multifunction new method, Topographer, to construct a …

Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is
challenging. Although a large number of methods and approaches exist, robustly identifying …

Developmental cell fate decisions are dynamic processes driven by the complex behaviour
of gene regulatory networks. A challenge in studying these processes using single-cell …