Recent advances in single-cell sequencing technologies offer an opportunity to explore cell–
cell communication in tissues systematically and with reduced bias. A key challenge is …

Single‐cell RNA sequencing (scRNA‐seq) technology has become the state‐of‐the‐art
approach for unravelling the heterogeneity and complexity of RNA transcripts within …

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods,
together with associated computational tools and the growing availability of public data …

The growing availability of single-cell data, especially transcriptomics, has sparked an
increased interest in the inference of cell-cell communication. Many computational tools …

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing
datasets provide unprecedented opportunities to dissect cell–cell communication (CCC) …

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but
does not capture their spatial distribution nor reveal local networks of intercellular …

Cell–cell interactions orchestrate organismal development, homeostasis and single-cell
functions. When cells do not properly interact or improperly decode molecular messages …

Understanding global communications among cells requires accurate representation of cell-
cell signaling links and effective systems-level analyses of those links. We construct a …

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

With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies,
the systematic decoding of cell-cell communication has received great research interest. To …