CellChat

Computational methods that model how the gene expression of a cell is influenced by interacting cells are lacking. We present NicheNet, a method that predicts ligand–target links between interacting cells by combining their expression data with prior knowledge of signaling and gene regulatory networks. We applied NicheNet to the tumor and immune cell microenvironment data and demonstrated that NicheNet can infer active ligands and their gene regulatory effects on interacting cells.

Single-cell RNA-seq datasets in diverse biological and clinical conditions provide great opportunities for the full transcriptional characterization of cell types. However, the integration of these datasets is challeging as they remain biological and techinical differences. **Harmony** is an algorithm allowing fast, sensitive and accurate single-cell data integration.

Single-cell RNA data allows cell-cell communications (***CCC***) methods to infer CCC at either the individual cell or cell cluster/cell type level, but physical distances between cells are not preserved Almet, Axel A., et al., (2021). On the other hand, spatial data provides spatial distances between cells, but single-cell or gene resolution is potentially lost. Therefore, integrating two types of data in a proper manner can complement their strengths and limitations, from that improve CCC analysis. In this pipeline, we analyze CCC on Visium data with single-cell data as a reference. The pipeline includes 4 sub-notebooks as following 01-deconvolution: This step involves deconvolution and cell type annotation for Visium data, with cell type information obtained from a relevant single-cell dataset. The deconvolution method is SpatialDWLS which is integrated in Giotto package. 02-giotto: performs spatial based CCC and expression based CCC on Visium data using Giotto method. 03-nichenet: performs spatial based CCC and expression based CCC on Visium data using NicheNet method. 04-visualization: visualizes CCC results obtained from Giotto and NicheNet.

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. **STdeconvolve** provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve .