FROM SINGLE CELLS TO SUBPOPULATIONS: EXPLORING CELL CLUSTERING IN TRANSCRIPTOMICS

Article Sidebar

FULL TEXT PDF
Published: Nov 14, 2022
DOI: https://doi.org/10.53555/eijbps.v8i1.44
Keywords:
Single-cell analysis, Gene expression, Cell clustering, Subpopulations, Single-cell RNA sequencing, Clustering algorithms, Cellular heterogeneity

Main Article Content

Barrett Hayes
University of Toronto, Canada

Abstract

of single-cell analysis. This progress enables researchers to delve into the intricate world of gene expression profiles atthe level of individual cells. One of the pivotal endeavors in transcriptomics is to explore cell clustering, where cells aregrouped based on their gene expression patterns, allowing for the identification and characterization of distinctsubpopulations. This process provides a profound understanding of cellular heterogeneity and its implications in variousfields, from developmental biology to disease research. In this abstract, we elucidate the significance of exploring cellclustering in transcriptomics, highlight the role of cutting-edge clustering algorithms, and emphasize the insights that canbe gained from this approach. By visualizing and comprehending cell subpopulations, we unlock the potential to uncovernovel cell types, dissect disease mechanisms, and advance personalized medicine. The journey from single cells tosubpopulations in transcriptomics is a transformative one, fostering new perspectives and driving innovative discoveriesin the life sciences.

Article Details

Issue
Vol. 8 No. 1 (2022): EPH - International Journal of Biological & Pharmaceutical Science (ISSN: 2208-2166)
Section
Articles

References

. G. R. R. Dewa, C. Park, and I. Sohn, "Distributed cell clustering based on multi-layer message passing for downlink joint processing coordinated multipoint transmission," Applied Sciences, vol. 10, no. 15, p. 5154, 2020.

. S. M. Khong et al., "Single-cell transcriptomics of human mesenchymal stem cells reveal age-related cellular subpopulation depletion and impaired regenerative function," Stem cells, vol. 37, no. 2, pp. 240-246, 2019.

. D. W.-H. Ho et al., "Single-cell transcriptomics reveals the landscape of intra-tumoral heterogeneity and stemness-related subpopulations in liver cancer," Cancer letters, vol. 459, pp. 176-185, 2019.

. A. Vandenbon and D. Diez, "A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data," Nature communications, vol. 11, no. 1, p. 4318, 2020.

. C. M. Pauken, S. R. Kenney, K. J. Brayer, Y. Guo, U. A. Brown-Glaberman, and D. Marchetti, "Heterogeneity of circulating tumor cell neoplastic subpopulations outlined by single-cell transcriptomics," Cancers, vol. 13, no. 19, p. 4885, 2021.

. S. K. Longo, M. G. Guo, A. L. Ji, and P. A. Khavari, "Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics," Nature Reviews Genetics, vol. 22, no. 10, pp. 627-644, 2021.

. J. Fan, K. Slowikowski, and F. Zhang, "Single-cell transcriptomics in cancer: computational challenges and opportunities," Experimental & Molecular Medicine, vol. 52, no. 9, pp. 1452-1465, 2020.

. W. Liu et al., "Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data," Nucleic acids research, vol. 50, no. 12, pp. e72-e72, 2022.

. J.-F. Poulin, B. Tasic, J. Hjerling-Leffler, J. M. Trimarchi, and R. Awatramani, "Disentangling neural cell diversity using single-cell transcriptomics," Nature neuroscience, vol. 19, no. 9, pp. 1131-1141, 2016.

. F. Nie, X. Wang, M. Jordan, and H. Huang, "The constrained laplacian rank algorithm for graph-based clustering," in Proceedings of the AAAI conference on artificial intelligence, 2016, vol. 30, no. 1.