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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

Comparison of sparse biclustering algorithms for gene expression datasets.

Kath Nicholls¹, Chris Wallace^1,2

¹Cambridge Institute for Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, CB2 0AW, UK.

Briefings in Bioinformatics

|May 5, 2021

Summary

This study compares sparse biclustering algorithms for gene expression analysis. Bayesian methods excelled on simulated data, while modified non-negative matrix factorization performed well on real-world datasets, highlighting the need for careful multi-tissue study design.

Keywords:

biclustering clustering gene expression multi-tissue

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Traditional gene and sample clustering methods assume cluster consistency across heterogeneous samples.
Biclustering algorithms address this by performing simultaneous gene and sample clustering.
Previous reviews lack recent algorithms and robust real-world dataset evaluations.

Purpose of the Study:

To compare the performance of four classes of sparse biclustering algorithms.
To evaluate algorithms on both simulated and real-world gene expression datasets.
To assess the suitability of biclustering for multi-tissue and multi-cell type analyses.

Main Methods:

Comparison of Bayesian, non-negative matrix factorization (NMF), and other sparse biclustering algorithms.
Utilized simulated datasets with varying numbers of genes and bicluster sizes.
Evaluated algorithms on multi-tissue mouse RNA-seq and human blood cell subset datasets.

Main Results:

All algorithms struggled with large simulated datasets; Bayesian methods showed high accuracy but were slower.
Modified NMF algorithms, with a novel sparsity-inducing post-processing step, ranked highly on real datasets.
Biclustering rarely identified cross-tissue clusters in mouse data, but succeeded in human blood cell subsets, indicating tissue heterogeneity challenges.

Conclusions:

Bayesian biclustering offers accuracy but at a computational cost.
Sparsity-inducing post-processing can enhance NMF for biclustering real-world data.
Careful consideration of tissue heterogeneity is crucial for designing and interpreting multi-tissue gene expression studies.