F. Alexander Wolf

TL;DR: This work presents Scanpy, a scalable toolkit for analyzing single-cell gene expression data that includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks, and AnnData, a generic class for handling annotated data matrices.

TL;DR: ScVelo reconstructs transient cell states and differentiation pathways from single-cell RNA-sequencing data, and infer gene-specific rates of transcription, splicing and degradation, recover each cell’s position in the underlying differentiation processes and detect putative driver genes.

TL;DR: This work describes an efficient way to robustly estimate the temporal order of differentiating cells according to diffusion pseudotime (DPT), which measures transitions between cells using diffusion-like random walks.

TL;DR: Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions, which preserves the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow.

TL;DR: ScVelo enables disentangling heterogeneous subpopulation kinetics with unprecedented resolution in hippocampal dentate gyrus neurogenesis and pancreatic endocrinogenesis and is anticipate that scVelo will greatly facilitate the study of lineage decisions, gene regulation, and pathway activity identification.