REVIEW: Complex Analysis of Single-Cell RNA Sequencing Data

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Anna A. Khozyainova^1,a*, Anna A. Valyaeva^2,3, Mikhail S. Arbatsky^4,5,6, Sergey V. Isaev⁷, Pavel S. Iamshchikov^1,8, Egor V. Volchkov⁹, Marat S. Sabirov¹⁰, Viktoria R. Zainullina¹, Vadim I. Chechekhin⁶, Rostislav S. Vorobev¹, Maxim E. Menyailo¹, Pyotr A. Tyurin-Kuzmin⁶, and Evgeny V. Denisov¹

¹Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, 634050 Tomsk, Russia

²Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119991 Moscow, Russia

³Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119991 Moscow, Russia

⁴Laboratory of Artificial Intelligence and Bioinformatics, The Russian Clinical Research Center for Gerontology, Pirogov Russian National Medical University, 129226 Moscow, Russia

⁵School of Public Administration, Lomonosov Moscow State University, 119991 Moscow, Russia

⁶Faculty of Fundamental Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia

⁷Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, 117036 Moscow, Russia

⁸Laboratory of Complex Analysis of Big Bioimage Data, National Research Tomsk State University, 634050 Tomsk, Russia

⁹Department of Oncohematology, Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, 117198 Moscow, Russia

¹⁰Laboratory of Bioinformatics and Molecular Genetics, Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, 119334 Moscow, Russia

^* To whom correspondence should be addressed.

Received September 23, 2022; Revised December 13, 2022; Accepted December 13, 2022

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Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell–cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.

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KEY WORDS: single-cell RNA sequencing, cell cycle, clustering, differential expression, cell type, trajectory inference, cell–cell interaction, gene regulatory network, copy number variation, single nucleotide variant, phylogenetics, epigenomics, spatial transcriptomics

DOI: 10.1134/S0006297923020074

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