Trajectory inference approaches analyze genome-wide omics data from thousands of single
cells and computationally infer the order of these cells along developmental trajectories …

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data
science. However, accurate prediction of cell fates, and thereby biologically meaningful …

Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during
dynamic biological processes such as tissue development and regeneration. Many …

Recent developments in single‐cell transcriptomics have opened new opportunities for
studying dynamic processes in immunology in a high throughput and unbiased manner …

Computational trajectory inference enables the reconstruction of cell state dynamics from
single-cell RNA sequencing experiments. However, trajectory inference requires that the …

Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage
differentiation trajectories, along with the characterization of cellular heterogeneity and state …

Background Single-cell transcriptomics allows researchers to investigate complex
communities of heterogeneous cells. It can be applied to stem cells and their descendants in …

The need to understand cell developmental processes spawned a plethora of computational
methods for discovering hierarchies from scRNAseq data. However, existing techniques are …

Single cell genomics has become a popular approach to uncover the cellular heterogeneity
of progenitor and terminally differentiated cell types with great precision. This approach can …

Tracking the progeny of single cells is necessary for building lineage trees that recapitulate
processes such as embryonic development and stem cell differentiation. In classical lineage …