Methods for profiling genes at the single-cell level have revolutionized our ability to study
several biological processes and systems including development, differentiation, response …

Studies with temporal or spatial resolution are crucial to understand the molecular dynamics
and spatial dependencies underlying a biological process or system. With advances in high …

We present a systematic evaluation of state-of-the-art algorithms for inferring gene
regulatory networks from single-cell transcriptional data. As the ground truth for assessing …

Over the past decade, single-cell genomics technologies have allowed scalable profiling of
cell-type-specific features, which has substantially increased our ability to study cellular …

Background Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our
understanding of cellular heterogeneity. Current analytical workflows are driven by …

Alzheimer's disease (AD) is the most common form of dementia, characterized by
progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic …

Single-cell techniques are advancing rapidly and are yielding unprecedented insight into
cellular heterogeneity. Map** the gene regulatory networks (GRNs) underlying cell states …

Here, we present Scribe (https://github. com/aristoteleo/Scribe-py), a toolkit for detecting and
visualizing causal regulatory interactions between genes and explore the potential for single …

The gene regulatory structure of cells involves not only the regulatory relationship between
two genes, but also the cooperative associations of multiple genes. However, most gene …

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of
millions of cells across species and diseases. These data have spurred the development of …