Abstract
The variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. In this paper, we introduce SC1CC (SC1 Cell Cycle analysis tool), a computational approach for clustering and ordering single cell transcriptional profiles according to their progression along cell cycle phases. We also introduce a new robust metric, Gene Smoothness Score (GSS) for assessing the cell cycle based order of the cells. SC1CC is available as part of the SC1 web-based scRNA-Seq analysis pipeline, publicly accessible at https://sc1.engr.uconn.edu/.
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References
Bar-Joseph, Z., Gifford, D.K., Jaakkola, T.S.: Fast optimal leaf ordering for hierarchical clustering. Bioinformatics 17(suppl\(\_\)1), S22–S29 (2001)
Barron, M., Li, J.: Identifying and removing the cell-cycle effect from single-cell RNA-sequencing data. Sci. Rep. 6 (2016)
Buettner, F., et al.: Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33(2), 155–160 (2015)
Cooper, G.M., Hausman, R.E., Hausman, R.E.: The Cell: A Molecular Approach, vol. 10. ASM Press, Washington DC (2000)
Dominguez, D., Tsai, Y.H., Gomez, N., Jha, D.K., Davis, I., Wang, Z.: A high-resolution transcriptome map of cell cycle reveals novel connections between periodic genes and cancer. Cell Res. 26(8), 946 (2016)
Gene Ontology Consortium: the gene ontology (GO) database and informatics resource. Nucleic Acids Res. 32(suppl\(\_\)1), D258–D261 (2004)
Gubin, M.M., Alspach, E., et al.: High-dimensional analysis delineates myeloid and lymphoid compartment remodeling during successful immune-checkpoint cancer therapy. Cell 175(4), 1014–1030 (2018)
Hahsler, M., Hornik, K., Buchta, C.: Getting things in order: an introduction to the R package seriation. J. Stat. Softw. 25(3), 1–34 (2008)
Kowalczyk, M.S.: Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25(12), 1860–1872 (2015)
Lee, C., Măndoiu, I.I., Nelson, C.E.: Inferring ethnicity from mitochondrial DNA sequence. In: BMC Proceedings. vol. 5, p. S11. BioMed Central (2011)
Leng, N., et al.: Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments. Nat. Methods 12(10), 947 (2015)
Liu, Z., et al.: Reconstructing cell cycle pseudo time-series via single-cell transcriptome data. Nat. Commun. 8(1), 22 (2017)
Moussa, M., Măndoiu, I.I.: SC1: a tool for interactive web-based single cell RNA-seq data analysis. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds.) ISBRA 2020. LNCS, vol. 12304, pp. 389–397. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57821-3_39
Moussa, M., Mandoiu, I.: Single cell RNA-seq data clustering using TF-IDF based methods. BMC-Genomics 19(Suppl 6), 569 (2018)
Santos, A., Wernersson, R., Jensen, L.J.: Cyclebase 3.0: a multi-organism database on cell-cycle regulation and phenotypes. Nucleic Acids Res., gku1092 (2014)
Scialdone, A., et al.: Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54–61 (2015)
Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. Royal Stat. Society Ser. B (Stat. Methodol.) 63(2), 411–423 (2001)
Van Asch, V.: Macro-and micro-averaged evaluation measures. Technical report (2013)
Zheng, G.X.Y., et al.: Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017)
Acknowledgments
This work was partially supported by NSF Award 1564936, NIH grants 1R01MH112739-01 and R01NS073425, and a UConn Academic Vision Program Grant.
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Moussa, M., Măndoiu, I.I. (2021). Computational Cell Cycle Analysis of Single Cell RNA-Seq Data. In: Jha, S.K., Măndoiu, I., Rajasekaran, S., Skums, P., Zelikovsky, A. (eds) Computational Advances in Bio and Medical Sciences. ICCABS 2020. Lecture Notes in Computer Science(), vol 12686. Springer, Cham. https://doi.org/10.1007/978-3-030-79290-9_7
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DOI: https://doi.org/10.1007/978-3-030-79290-9_7
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