ArticleBriefings in bioinformatics2023
Quantum annealing-based clustering of single cell RNA-seq data.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Quantum medicine: A quantum-mechanical framework for redox biology, disease and precision medicine.Clinical and translational medicine · 2026Review
- Quantum computing and the implementation of precision medicine.NPJ genomic medicine · 2025Review
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Authors and funding
2 authors.
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Abstract
Cluster analysis is a crucial stage in the analysis and interpretation of single-cell gene expression (scRNA-seq) data. It is an inherently ill-posed problem whose solutions depend heavily on hyper-parameter and algorithmic choice. The popular approach of K-means clustering, for example, depends heavily on the choice of K and the convergence of the expectation-maximization algorithm to local minima of the objective. Exhaustive search of the space for multiple good quality solutions is known to be a complex problem. Here, we show that quantum computing offers a solution to exploring the cost function of clustering by quantum annealing, implemented on a quantum computing facility offered by D-Wave [1]. Out formulation extracts minimum vertex cover of an affinity graph to sub-sample the cell population and quantum annealing to optimise the cost function. A distribution of low-energy solutions can thus be extracted, offering alternate hypotheses about how genes group together in their space of expressions.
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