CCIDeconv: Hierarchical model for deconvolution of subcellular cell-cell interactions in single-cell data

MotivationCell-cell interaction (CCI) underlies several fundamental mechanisms including development, homeostasis and disease progression. CCI are known to be localised to specific subcellular regions, for example, within the cytoplasms of cells. With the emergence of subcellular spatial transcriptomics technologies (sST), there is an opportunity to attribute CCI to subcellular regions. We aimed to deconvolute CCI to subcellular CCI (sCCI) in non-spatial single cell transcriptomics data (i.e. scRNA-seq) datasets using a modified CCI score from CellChat. ResultsBy calculating the sCCI score specific to cytoplasm and nucleus in nine publicly available sST datasets, we identified unique nucleus-nucleus and cytoplasm-cytoplasm sCCI. Then, we deconvolved the communication score to subcellular regions by using a hierarchical classification and regression model which we name as CCIDeconv. We performed leave-one-dataset-out cross-validation across nine datasets over a range of different tissue types from human samples. We observed that training across many different tissue types resulted in robust deconvolution performance in an unseen dataset. As the number of training datasets increased, models trained without spatial features achieved similar performance as models including spatial features. This implied the potential for accurate prediction of sCCI events from even scRNA-seq with large numbers of training datasets. Overall, we offer a method towards attributing CCI events to subcellular regions. This method can allow researchers in dissecting sCCI patterns to gain insights in underlying biology in a range of tissues covering health and disease.