kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2024-21-4-45-57kaz29-1538Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEОБЗОР ИНСТРУМЕНТОВ, МЕТОДОЛОГИЙ И МЕТОДОВ ОБРАБОТКИ, ПРЕДВАРИТЕЛЬНОЙ ОБРАБОТКИ И КЛАСТЕРНОГО АНАЛИЗА ГЕНЕТИЧЕСКИХ ДАННЫХA REVIEW OF TOOLS, METHODOLOGIES, AND TECHNIQUES FOR PROCESSING, PRE-PROCESSING, AND CLUSTERING ANALYSIS OF GENETIC DATAhttps://orcid.org/0000-0003-3716-0895КуникеевА.KunikeyevA.

магистр технических наук

г. Алматы

Master of Engineering Sciences

Almaty

 

a.kunikeyev@satbayev.universityhttps://orcid.org/0000-0002-2013-1513ЕримбетоваА.YerimbetovaA.

доктор Ph.D., канд. техн. наук, ассоц. профессор

г. Алматы

PhD, Candidate of Technical Sciences, Associate Professor

Almaty

aigerian8888@gmail.comhttps://orcid.org/0000-0002-0678-7583СатыбалдиеваР.SatybaldiyevaR.

канд. техн. наук, профессор

г. Алматы

Candidate of Technical Sciences, Professor

Almaty

r.satybaldiyeva@satbayev.universityСатбаев УниверситетКазахстанSatbayev UniversityKazakhstanСатбаев Университет; Институт информационных и вычислительных технологий Комитета науки Министерства науки и высшего образования Республики КазахстанКазахстанSatbayev University; Institute of Information and Computational Technologies of the Committee of Science of the Ministry of Science and Higher Education of the Republic of KazakhstanKazakhstan2024221220242144557Copyright © Куникеев А., Еримбетова А., Сатыбалдиева Р., 20242024Куникеев А., Еримбетова А., Сатыбалдиева Р.Kunikeyev A., Yerimbetova A., Satybaldiyeva R.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/1538

Анализ экспрессии генов стал ключевым компонентом в понимании поведения клеток, механизмов заболеваний и реакции на лекарства. Появление высокопроизводительного секвенирования, в частности секвенирования РНК отдельных клеток (scRNA-seq), расширило наши возможности изучения клеточной гетерогенности до беспрецедентного уровня. Алгоритмы кластеризации, необходимые для группировки генов или клеток со схожими профилями экспрессии, стали бесценными для анализа огромных наборов данных, генерируемых этими технологиями. В этой статье рассматриваются различные методы кластеризации, применяемые к данным об экспрессии генов, в частности секвенирования РНК отдельных клеток. Анализ охватывает традиционные методы, такие как иерархическая кластеризация и k-means, а также более продвинутые подходы, такие как кластеризация на основе моделей, методы на основе машинного обучения и глубокого обучения. Основные проблемы включают обработку многомерных данных, снижение шума и достижение масштабируемости для больших наборов данных. Более того, новые достижения, такие как интеграция данных мультиомики, кластеризация на основе глубокого обучения и федеративное обучение, предлагают потенциальные улучшения точности и биологической значимости для приложений кластеризации в исследовании экспрессии генов. Обзор завершается обсуждением будущих направлений развития алгоритмов кластеризации для обработки все более сложных данных об экспрессии генов для получения более точных биологических пониманий.

Gene expression analysis has become a key component in understanding cellular behavior, disease mechanisms, and drug response. The advent of high-throughput sequencing, particularly single-cell RNA sequencing (scRNAseq), has expanded our ability to study cellular heterogeneity to an unprecedented level. Clustering algorithms needed to group genes or cells with similar expression profiles have become invaluable for analyzing the massive data sets generated by these technologies. This article reviews various clustering methods applied to gene expression data, particularly single-cell RNA sequencing. The analysis covers traditional methods such as hierarchical clustering and k-means, as well as more advanced approaches such as model-based clustering, machine learning-based methods, and deep learning methods. The primary challenges encompass handling high-dimensional data, mitigating noise, and achieving scalability for large datasets. Moreover, new advancements such as multi-omics data integration, deep learning-based clustering, and federated learning offer potential enhancements in accuracy and biological relevance for clustering applications in gene expression research. The review concludes with a discussion of clustering algorithms in handling increasingly complex gene expression data for more accurate biological insights.

методы кластеризациибиоинформатикамашинное обучениелубокое обучениесеквенирование РНК отдельных клетокэкспрессия генов: Clustering methodsBioinformaticsMachine LearningDeep learningsingle-cell RNA sequencingGene expressionsThis research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant № AP22686112 Study of somatic mutations from single-cell RNA data using machine learning methods in patients with peripheral artery disease). Datasets available in NCBI (PRJNA736095; GEO: GSE176415): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176415 Sample code on R, available on github: https://github.com/dauletulyaidyn/scrna_clusterin_samples/uploadReferencesCasadei R. et al. Identification of housekeeping genes suitable for gene expression analysis in the zebrafish. Gene Expression Patterns, 2011, vol. 11, no. 3–4, pp. 271–276.Casadei R. et al. Identification of housekeeping genes suitable for gene expression analysis in the zebrafish. Gene Expression Patterns, 2011, vol. 11, no. 3–4, pp. 271–276.Seo D., Ginsburg G.S., Goldschmidt-Clermont P.J. Gene Expression Analysis of Cardiovascular Diseases. J Am Coll Cardiol, 2006, vol. 48, no. 2, pp. 227–235.Seo D., Ginsburg G.S., Goldschmidt-Clermont P.J. Gene Expression Analysis of Cardiovascular Diseases. J Am Coll Cardiol, 2006, vol. 48, no. 2, pp. 227–235.Predicting drug response based on gene expression. Crit Rev Oncol Hematol, 2004, vol. 51, no. 3, pp. 205–227.Predicting drug response based on gene expression. Crit Rev Oncol Hematol, 2004, vol. 51, no. 3, pp. 205–227.Huang X. et al. High Throughput Single Cell RNA Sequencing, Bioinformatics Analysis and Applications, 2018, pp. 33–43.Huang X. et al. High Throughput Single Cell RNA Sequencing, Bioinformatics Analysis and Applications, 2018, pp. 33–43.Perera M.A.I., Wijesinghe C.R., Weerasinghe A.R. Analysis of Expression Data Using Unsupervised Techniques. 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2020, pp. 119–124.Perera M.A.I., Wijesinghe C.R., Weerasinghe A.R. Analysis of Expression Data Using Unsupervised Techniques. 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2020, pp. 119–124.Li X., Wang C.-Y. From bulk, single-cell to spatial RNA sequencing. Int J Oral Sci, 2021, vol. 13, no. 1, p. 36.Li X., Wang C.-Y. From bulk, single-cell to spatial RNA sequencing. Int J Oral Sci, 2021, vol. 13, no. 1, p. 36.Nathans J.F. et al. Genetic Tools for Cell Lineage Tracing and Profiling Developmental Trajectories in the Skin. Journal of Investigative Dermatology, 2024, vol. 144, no. 5, pp. 936–949.Nathans J.F. et al. Genetic Tools for Cell Lineage Tracing and Profiling Developmental Trajectories in the Skin. Journal of Investigative Dermatology, 2024, vol. 144, no. 5, pp. 936–949.Yao D.W. et al. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat Genet, 2020, vol. 52, no. 6, pp. 626–633.Yao D.W. et al. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat Genet, 2020, vol. 52, no. 6, pp. 626–633.Huang C.-T. et al. Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery. iScience, 2019, vol. 15, pp. 291–306.Huang C.-T. et al. Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery. iScience, 2019, vol. 15, pp. 291–306.Qi R. et al. Clustering and classification methods for single-cell RNA-sequencing data. Brief Bioinform, 2020, vol. 21, no. 4, pp. 1196–1208.Qi R. et al. Clustering and classification methods for single-cell RNA-sequencing data. Brief Bioinform, 2020, vol. 21, no. 4, pp. 1196–1208.Badsha Md.B. et al. Robust complementary hierarchical clustering for gene expression data analysis by β-divergence. J Biosci Bioeng, 2013, vol. 116, no. 3, pp. 397–407.Badsha Md.B. et al. Robust complementary hierarchical clustering for gene expression data analysis by β-divergence. J Biosci Bioeng, 2013, vol. 116, no. 3, pp. 397–407.Chen L. et al. Deep soft K-means clustering with self-training for single-cell RNA sequence data. NAR Genom Bioinform, 2020, vol. 2, no. 2.Chen L. et al. Deep soft K-means clustering with self-training for single-cell RNA sequence data. NAR Genom Bioinform, 2020, vol. 2, no. 2.Li L. et al. Selecting Representative Samples From Complex Biological Datasets Using K-Medoids Clustering. Front Genet., 2022, vol. 13.Li L. et al. Selecting Representative Samples From Complex Biological Datasets Using K-Medoids Clustering. Front Genet., 2022, vol. 13.Gormley I.C., Murphy T.B., Raftery A.E. Model-Based Clustering. Annu Rev Stat Appl., 2023, vol. 10, no. 1, pp. 573–595.Gormley I.C., Murphy T.B., Raftery A.E. Model-Based Clustering. Annu Rev Stat Appl., 2023, vol. 10, no. 1, pp. 573–595.Yu B. et al. scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder. Brief Bioinform, 2021, vol. 22, no. 4.Yu B. et al. scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder. Brief Bioinform, 2021, vol. 22, no. 4.Wu X., Wu H., Wu Z. Penalized Latent Dirichlet Allocation Model in Single-Cell RNA Sequencing. Stat Biosci., 2021, vol. 13, no. 3, pp. 543–562.Wu X., Wu H., Wu Z. Penalized Latent Dirichlet Allocation Model in Single-Cell RNA Sequencing. Stat Biosci., 2021, vol. 13, no. 3, pp. 543–562.Arora S. et al. Variability in estimated gene expression among commonly used RNA-seq pipelines. Sci Rep., 2020, vol. 10, no.1, p. 2734.Arora S. et al. Variability in estimated gene expression among commonly used RNA-seq pipelines. Sci Rep., 2020, vol. 10, no.1, p. 2734.Lataretu M., Hölzer M. RNAflow: An Effective and Simple RNA-Seq Differential Gene Expression Pipeline Using Nextflow. Genes (Basel), 2020, vol. 11, no. 12, p. 1487.Lataretu M., Hölzer M. RNAflow: An Effective and Simple RNA-Seq Differential Gene Expression Pipeline Using Nextflow. Genes (Basel), 2020, vol. 11, no. 12, p. 1487.Rosati D. et al. Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review. Comput Struct Biotechnol J., 2024, vol. 23, pp. 1154–1168.Rosati D. et al. Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review. Comput Struct Biotechnol J., 2024, vol. 23, pp. 1154–1168.Lo C.-C., Chain P.S.G. Rapid evaluation and quality control of next generation sequencing data with FaQCs. BMC Bioinformatics, 2014, vol. 15, no. 1, p. 366.Lo C.-C., Chain P.S.G. Rapid evaluation and quality control of next generation sequencing data with FaQCs. BMC Bioinformatics, 2014, vol. 15, no. 1, p. 366.Bolger A.M., Lohse M., Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 2014, vol. 30, no. 15, pp. 2114–2120.Bolger A.M., Lohse M., Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 2014, vol. 30, no. 15, pp. 2114–2120.Sun K. Ktrim: an extra-fast and accurate adapter- and quality-trimmer for sequencing data. Bioinformatics, 2020, vol. 36, no. 11, pp. 3561–3562.Sun K. Ktrim: an extra-fast and accurate adapter- and quality-trimmer for sequencing data. Bioinformatics, 2020, vol. 36, no. 11, pp. 3561–3562.Dobin A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 2013, vol. 29, no. 1, pp. 15–21.Dobin A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 2013, vol. 29, no. 1, pp. 15–21.Kim D. et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol., 2019, vol. 37, no. 8, pp. 907–915.Kim D. et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol., 2019, vol. 37, no. 8, pp. 907–915.Kim D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol., 2013, vol. 14, no.4, p. R36.Kim D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol., 2013, vol. 14, no.4, p. R36.Anders S., Pyl P.T., Huber W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics, 2015, vol. 31, no. 2, pp. 166–169.Anders S., Pyl P.T., Huber W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics, 2015, vol. 31, no. 2, pp. 166–169.Kim T. et al. Impact of similarity metrics on single-cell RNA-seq data clustering, Brief Bioinform., 2019, vol. 20, no. 6, pp. 2316–2326.Kim T. et al. Impact of similarity metrics on single-cell RNA-seq data clustering, Brief Bioinform., 2019, vol. 20, no. 6, pp. 2316–2326.Liu S. et al. Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2. Journal of Visualized Experiments, 2021, no. 175.Liu S. et al. Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2. Journal of Visualized Experiments, 2021, no. 175.Abu-Jamous B., Kelly S. Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data. Genome Biol., 2018, vol. 19, no. 1, p. 172.Abu-Jamous B., Kelly S. Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data. Genome Biol., 2018, vol. 19, no. 1, p. 172.Abueg L.A.L. et al. The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update. Nucleic Acids Res., 2024, vol. 52, no. W1, pp. W83–W94.Abueg L.A.L. et al. The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update. Nucleic Acids Res., 2024, vol. 52, no. W1, pp. W83–W94.

The authors declare that there are no conflicts of interest present.