LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data

Min Lu, Yifan Sha, Tiago C. Silva, Antonio Colaprico, Xiaodian Sun, Yuguang Ban, Lily Wang, Brian D. Lehmann, X. Steven Chen

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number708835
JournalFrontiers in Genetics
Volume12
DOIs
StatePublished - Aug 20 2021
Externally publishedYes

Keywords

  • cell–cell communications
  • cell–cell interaction
  • ligand-receptor interaction
  • random forests
  • single-cell RNA-seq

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Genetics(clinical)

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