# Quantum annealing for enhanced feature selection in single-cell RNA sequencing data analysis

## TL;DR

This paper explores using quantum computing to improve gene selection in single-cell RNA sequencing data, helping uncover important biological patterns.

## Contribution

The paper introduces quantum annealing-based QUBO for feature selection in scRNA-seq data, offering a novel computational approach.

## Key findings

- QUBO identifies genes with expression patterns linked to cell state transitions and drug resistance.
- Quantum annealing reveals complex gene patterns missed by traditional methods.
- The approach enhances interpretation of scRNA-seq data in differentiation and drug resistance studies.

## Abstract

Feature selection is a machine learning technique for identifying relevant variables in classification and regression models. In single-cell RNA sequencing (scRNA-seq) data analysis, feature selection is used to identify relevant genes that are crucial for understanding cellular processes. Traditional feature selection methods often struggle with the complexity of scRNA-seq data and suffer from interpretation difficulties. Quantum annealing presents a promising alternative approach. In this study, we implement quantum annealing-empowered quadratic unconstrained binary optimization (QUBO) for feature selection in scRNA-seq data. Using data from a human cell differentiation system and an anticancer drug resistance study, we demonstrate that QUBO feature selection effectively identifies genes whose expression patterns reflect critical cell state transitions associated with differentiation and drug resistance development. Our findings indicate that quantum annealing-powered QUBO reveals complex gene expression patterns potentially missed by traditional methods, thereby enhancing scRNA-seq data analysis and interpretation.