Lu Min, Sha Yifan, Silva Tiago C, Colaprico Antonio, Sun Xiaodian, Ban Yuguang, Wang Lily, Lehmann Brian D, Chen X Steven
Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States.
Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States.
Front Genet. 2021 Aug 20;12:708835. doi: 10.3389/fgene.2021.708835. eCollection 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 (RFs)-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 RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.
细胞间相互作用（CCIs）和细胞间通讯（CCCs）对于维持复杂的生物系统至关重要。单细胞RNA测序（scRNA-seq）数据的可用性为通过识别细胞间的配体-受体（LR）基因相互作用来破译CCIs和CCCs开辟了新途径。然而，大多数方法是为了检测单个基因对的LR相互作用而开发的。在这里，我们提出了一种名为LR hunting的新方法，该方法首先使用基于随机森林（RFs）的数据插补技术来连接不同细胞类型之间的数据。为了保证数据插补过程的稳健性，我们多次重复计算过程以生成聚合插补最小深度指数（IMDI）。接下来，我们使用无监督RFs同时识别LR对所有组合之间的显著LR相互作用。我们通过测序（CITE-seq）数据集和三阴性乳腺癌scRNA-seq数据集证明了LR hunting可以恢复具有生物学意义的CCIs。
