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Cell-cell communication events (CEs) mediated by multiple ligand-receptor pairs construct a complex intercellular signaling network. Usually only a subset of CEs directly works for a specific downstream response in certain microenvironments. We call them functional communication events (FCEs). Spatial transcriptomic methods can profile the spatial distribution of gene expression levels of ligands, receptors, and their downstream genes. This provides a new possibility for revealing the holographic network of cell-cell communication. We developed HoloNet, a computational method for decoding FCEs using spatial transcriptomic data. We modeled CEs as a multi-view network, developed an attention-based graph learning model on the network to predict the target gene expression, and decoded the FCEs for specific downstream genes by interpreting the trained model. We applied HoloNet on two breast cancer Visium datasets to reveal the communication landscapes in breast cancer microenvironments. It detected ligand-receptor signals triggering the expression changes of invasion-related genes in stromal cells surrounding tumors. The experiments showed that HoloNet is a powerful tool on spatial transcriptomic data to help understand the shaping of cellular phenotypes through cell-cell communication in a microenvironment.

eQTL studies are essential for understanding genomic regulation. Effects of genetic variations on gene regulation are cell-type-specific and cellular-context-related, so studying eQTLs at a single-cell level is crucial. The ideal solution is to use both mutation and expression data from the same cells. However, current technology of such paired data in single cells is still immature. We present a new method, eQTLsingle, to discover eQTLs only with single cell RNA-seq (scRNA-seq) data, without genomic data. It detects mutations from scRNA-seq data and models gene expression of different genotypes with the zero-inflated negative binomial (ZINB) model to find associations between genotypes and phenotypes at single-cell level. On a glioblastoma and gliomasphere scRNA-seq dataset, eQTLsingle discovered hundreds of cell-type-specific tumor-related eQTLs, most of which cannot be found in bulk eQTL studies. Detailed analyses on examples of the discovered eQTLs revealed important underlying regulatory mechanisms. eQTLsingle is a unique powerful tool for utilizing the huge scRNA-seq resources for single-cell eQTL studies, and it is available for free academic use at https://github.com/horsedayday/eQTLsingle.

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