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  3. Emd-hvg: A Normalization-independent Method For Highly Variable Gene Selection Based On Earth Mover's Distance.
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  3. Emd-hvg: A Normalization-independent Method For Highly Variable Gene Selection Based On Earth Mover's Distance.

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EMD-HVG: a normalization-independent method for highly variable gene selection based on Earth mover's distance.

Chunfang Peng1,2, Guobin Li1,2, Jiamiao Wu1,2

  • 1Department of Statistical Science, School of Mathematics, Sun Yat-sen University, No.135, Xingang Xi Road, Guangzhou, 510275, Guangdong, China.

BMC Bioinformatics
|June 20, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces Earth Mover's Distance based Highly Variable Gene identification (EMD-HVG), a novel method for identifying highly variable genes in single-cell RNA sequencing and spatial transcriptomics. EMD-HVG offers improved accuracy and robustness without normalization, enhancing downstream analyses.

Keywords:
ClusteringHighly variable genesSingle-cell RNA sequencingSpatial transcriptomics

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Identifying highly variable genes (HVGs) is crucial for single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST).
  • Existing methods often rely on normalization, which can obscure biological variability and struggle with sparse, noisy scRNA-seq/ST data.
  • Current distributional assumptions in HVG identification may not accurately reflect the nature of this data.

Purpose of the Study:

  • To develop a normalization-independent and nonparametric method for robust HVG identification.
  • To overcome the limitations of conventional normalization-based approaches in scRNA-seq and ST data analysis.

Main Methods:

  • Proposed the Earth Mover's Distance based Highly Variable Gene identification method (EMD-HVG).
  • EMD-HVG utilizes a mixture distribution model for gene expression patterns, preserving biological heterogeneity.
  • Employs Earth Mover's Distance (EMD), a nonparametric metric, to assess gene expression variability without distributional assumptions.

    • Main Results:

    • EMD-HVG demonstrated superior performance compared to existing HVG detection methods across numerous evaluation scenarios.
    • Achieved top performance in 26 out of 30 tested scenarios based on various accuracy metrics.
    • The identified HVGs significantly improved the quality of downstream clustering and cell-type delineation.

    Conclusions:

    • EMD-HVG is a robust and broadly applicable method for HVG identification in both scRNA-seq and ST.
    • The method enhances downstream analysis precision, facilitating more accurate cell-type identification.
    • EMD-HVG offers a significant advancement for transcriptomic data analysis by avoiding normalization pitfalls.