ArticleFrontiers in genetics2021
LR Hunting: A Random Forest Based Cell-Cell Interaction Discovery Method for Single-Cell Gene Expression Data.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Global trends in machine learning applications for single-cell transcriptomics research.Hereditas · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Artificial Intelligence-Powered Insights into Polyclonality and Tumor Evolution.Research (Washington, D.C.) · 2025Review
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- Ensemble methods of rank-based trees for single sample classification with gene expression profiles.Journal of translational medicine · 2024Article
- The biological significance of cuproptosis-key gene MTF1 in pan-cancer and its inhibitory effects on ROS-mediated cell death of liver hepatocellular carcinoma.Discover oncology · 2023Article
- Telehealth utilization in U.S. medicare beneficiaries aged 65 years and older during the COVID-19 pandemic.BMC public health · 2023Article
- Exploring Factors That Affected Student Well-Being during the COVID-19 Pandemic: A Comparison of Data-Mining Approaches.International journal of environmental research and public health · 2022Article
- Access to care through telehealth among U.S. Medicare beneficiaries in the wake of the COVID-19 pandemic.Frontiers in public health · 2022Article
- Principles of Spatial Transcriptomics Analysis: A Practical Walk-Through in Kidney Tissue.Frontiers in physiology · 2021Review
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Authors and funding
9 authors.
Funding
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.
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