Related Experiment Video
Updated: Jun 23, 2026
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
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.
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.
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.
