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GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution.

Iori Azuma¹, Tadahaya Mizuno¹, Hiroyuki Kusuhara¹

¹Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Bunkyo-ku 113-0033, Japan.

Briefings in Bioinformatics

|July 10, 2024

Summary

We developed guided LDA deconvolution (GLDADec), a new method to accurately estimate cell type proportions from bulk transcriptome data. This tool enhances cell type analysis in immunology and oncology research.

Keywords:

Latent Dirichlet Allocation The Cancer Genome Atlas deconvolution marker gene name perturbation semi-supervised learning

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate inference of cell type proportions from bulk transcriptome data is essential for understanding complex biological systems in immunology and oncology.
Existing bulk deconvolution methods face challenges in precision and interpretability.

Purpose of the Study:

To introduce guided LDA deconvolution (GLDADec), a novel computational method for precise cell type deconvolution from bulk RNA sequencing data.
To evaluate GLDADec's performance and biological interpretability compared to existing methods.

Main Methods:

GLDADec utilizes cell type-specific marker gene names to guide Latent Dirichlet Allocation (LDA) for topic modeling.
Benchmarking was performed on blood-derived datasets and heterogeneous tissue data.
Biological interpretability was assessed through enrichment analysis of biological processes.

Main Results:

GLDADec demonstrated high estimation performance and robustness in benchmarking studies.
The method outperformed existing deconvolution techniques in accuracy.
Application to The Cancer Genome Atlas (TCGA) data enabled subtype stratification and survival analysis.

Conclusions:

GLDADec provides a powerful and interpretable approach for cell type deconvolution from bulk transcriptome data.
The method has practical utility in clinical settings, aiding in cancer subtype stratification and survival prediction.
GLDADec is available as an open-source Python package, facilitating its widespread adoption.