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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Quantum Annealing for Enhanced Feature Selection in Single-Cell RNA Sequencing Data Analysis.

Selim Romero^1,2,3, Shreyan Gupta^1,3, Victoria Gatlin^1,3

¹Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.

|August 20, 2025

Summary

Quantum annealing-powered QUBO enhances feature selection for single-cell RNA sequencing (scRNA-seq) data. This method identifies crucial genes in cell differentiation and drug resistance, improving data interpretation.

Keywords:

Quantum annealing feature selection quadratic unconstrained binary optimization (QUBO)quantum computing scRNA-seq

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Area of Science:

Computational Biology
Quantum Computing
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) generates complex gene expression data.
Traditional feature selection methods face challenges in interpreting scRNA-seq data.
Quantum annealing offers a novel approach for complex computational problems.

Purpose of the Study:

To implement quantum annealing-powered QUBO for feature selection in scRNA-seq data.
To identify genes critical for understanding cellular processes like differentiation and drug resistance.
To evaluate the effectiveness of quantum annealing in enhancing scRNA-seq data analysis and interpretation.

Main Methods:

Utilized quantum annealing with quadratic unconstrained binary optimization (QUBO).
Applied the QUBO feature selection method to scRNA-seq datasets.
Analyzed data from human cell differentiation and anticancer drug resistance studies.

Main Results:

QUBO feature selection successfully identified key genes related to cell state transitions.
Expression patterns of selected genes reflected critical differentiation and drug resistance processes.
Quantum annealing-powered QUBO revealed complex gene expression patterns potentially missed by conventional methods.

Conclusions:

Quantum annealing-QUBO is an effective technique for scRNA-seq feature selection.
This approach enhances the identification of biologically relevant genes.
The method improves the analysis and interpretation of complex single-cell gene expression data.