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Updated: Jun 27, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.

Dongyuan Wu1, Susmita Datta1

  • 1Department of Biostatistics, University of Florida, Gainesville, FL 32611, USA.

Genes
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

We developed BPHurdle, a new statistical model for single-cell RNA sequencing (scRNA-seq) data, to accurately identify cell-type-specific expression quantitative trait loci (eQTLs) and understand gene regulation at a cellular level.

Keywords:
bootstrapeQTL analysishurdle modelpenalized regressionsingle-cell

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Area of Science:

  • Genomics
  • Computational Biology
  • Statistical Genetics

Background:

  • Expression quantitative trait loci (eQTL) analysis connects genetic variants to gene expression, aiding the study of gene regulation.
  • Single-cell RNA sequencing (scRNA-seq) enables cell-type-specific eQTL detection, but data sparsity and heterogeneity pose challenges.
  • Existing methods struggle with the complexities of scRNA-seq data for eQTL mapping.

Purpose of the Study:

  • To introduce a novel statistical framework, BPHurdle, tailored for scRNA-seq data.
  • To address the challenges of sparsity and heterogeneity in single-cell eQTL analysis.
  • To improve the accuracy and robustness of identifying cell-type-specific regulatory variants.

Main Methods:

  • Developed the Bootstrap Penalized Hurdle regression model (BPHurdle).
  • Utilized a hurdle framework with logistic and Poisson components to model excess zeros and positive expression levels.
  • Applied the model to both simulated and real scRNA-seq datasets.

Main Results:

  • BPHurdle demonstrated high accuracy and robustness in simulations for identifying regulatory variants.
  • Successfully identified reliable cell-type-specific eQTLs in a real scRNA-seq dataset case study.
  • Validated the model's effectiveness on differentially expressed genes.

Conclusions:

  • BPHurdle provides an advanced and flexible approach for single-cell eQTL mapping.
  • Offers deeper insights into genetic regulation of gene expression at cellular resolution.
  • Facilitates more precise understanding of genetic influences on cellular function.