LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data - Lu, Min; Sha, Yifan; Silva, Tiago C.; Colaprico, Antonio; Sun, Xiaodian; Ban, Yuguang; Wang, Lily; Lehmann, Brian D.; Chen, X. Steven (Frontiers in Genetics, 2021)
Cell-cell interactions (CCIs) and cell-cell communication (CCC) are critical for maintaining complex biological systems. The availability of single cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised random forests. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse CITE-seq dataset and a triple-negative breast cancer scRNA-seq dataset.