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A high-efficiency differential expression method for cancer heterogeneity using large-scale single-cell

Xin Yuan1,2, Shuangge Ma2,3, Botao Fa4

  • 1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Frontiers in Genetics
|December 16, 2022
PubMed
Summary

We developed HEART, a new method for analyzing single-cell RNA sequencing data to identify differential gene expression in complex diseases like colorectal cancer. HEART accurately detects key genes, aiding in understanding tumor heterogeneity and metastasis.

Keywords:
DE genePBMC68Kcolorectal cancercombination testdifferential analysis

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Colorectal cancer is a complex disease characterized by significant tumor heterogeneity, which impedes effective treatment strategies.
  • Single-cell RNA sequencing (scRNA-seq) offers high-resolution insights into cancer heterogeneity but presents computational challenges due to data sparsity and scale.
  • Existing differential expression (DE) methods struggle with the unique characteristics of scRNA-seq data, impacting accuracy and efficiency.

Purpose of the Study:

  • To introduce HEART (high-efficiency and robust test), a novel statistical method designed to address the limitations of current DE analysis for scRNA-seq data.
  • To evaluate the performance of HEART against existing DE methods using diverse simulation datasets and real-world scRNA-seq data.
  • To identify potential blood-based biomarkers for colorectal cancer metastasis using HEART analysis.

Main Methods:

  • HEART, a statistical combination test, was developed to detect differential gene expression beyond simple mean changes, accommodating various sources of biological variation.
  • HEART's performance was rigorously assessed through comparisons with six other popular DE methods on simulation datasets generated by two distinct mechanisms.
  • Validation involved applying HEART to PBMC68K and human brain scRNA-seq datasets, as well as a colorectal cancer patient dataset, followed by spatial transcriptomic validation.

Main Results:

  • HEART demonstrated high accuracy (AUROC > 0.75) and exceptional computational efficiency (under 2 minutes) across multiple simulation settings.
  • The method performed robustly on real scRNA-seq data, achieving an AUROC of 0.79 for the PBMC68K dataset (UMI counts) and 0.65 for the human brain dataset (read counts).
  • Analysis of colorectal cancer patient data identified several potential blood-based biomarkers (CTTN, S100A4, S100A6, UBA52, FAU, VIM) linked to metastasis, which were subsequently validated.

Conclusions:

  • HEART is a highly accurate and efficient tool for differential gene expression analysis in single-cell RNA sequencing data, overcoming challenges posed by data characteristics.
  • The identified biomarkers (CTTN, S100A4, S100A6, UBA52, FAU, VIM) show promise for non-invasive detection and monitoring of colorectal cancer metastasis.
  • This study highlights the potential of advanced computational methods like HEART in unraveling cancer heterogeneity and discovering clinically relevant biomarkers.