A Mixture Model Based Read Simulating Method for Single Cell RNA Sequencing

15152e1b-6bf2-4413-b96a-ca1721f333a020251121131230975wseas:wseasmdt@crossref.orgMDT DepositMOLECULAR SCIENCES AND APPLICATIONS2732-99922944-913810.37394/232023https://wseas.com/journals/msa/index.php42320254232025510.37394/232023.2025.5https://wseas.com/journals/msa/2025.phpA Mixture Model Based Read Simulating Method for Single Cell RNA SequencingWenshanLiSchool of Cyber Science and Engineering Sichuan University No. 24 South Section 1, Yihuan Road, Chengdu CHINAWenboFangSchool of Cyber Science and Engineering Sichuan University No. 24 South Section 1, Yihuan Road, Chengdu CHINAAoLiuSchool of Cyber Science and Engineering Sichuan University No. 24 South Section 1, Yihuan Road, Chengdu CHINABeibeiLiSchool of Cyber Science and Engineering Sichuan University No. 24 South Section 1, Yihuan Road, Chengdu CHINAJunjiangHeSchool of Cyber Science and Engineering Sichuan University No. 24 South Section 1, Yihuan Road, Chengdu CHINAHongxiaWangSchool of Cyber Science and Engineering Sichuan University No. 24 South Section 1, Yihuan Road, Chengdu CHINATechniques for single-cell RNA sequencing (scRNA-seq) has enabled unprecedented insights into gene expressions in cell level. Drop-seq is one of the prominent scRNA-seq protocols, and there has been a rapid growth in related analysis tools for Drop-seq data. These methods are tested either using spike-in experiments or on simulation datasets as the real word gene differential expressions are usually unknown. Since spike-in experiments are expensive and time consuming, simulated datasets have become a reasonable alternative method. However, current RNA-seq simulators mostly target at bulk RNA sequencing, which provokes the need of a scRNA-seq simulator for the Drop-seq technology. In this paper, we present a mixture model based read simulating method to simulate the sequencing reads of a Drop-seq experiment. The proposed method is able to simulate large amounts of Drop-seq reads according to the user's experimental setting. Data generated by the proposed model is a reasonable approximation to real Drop-seq data.112120251121202517918913https://wseas.com/journals/msa/2025/a26msa-012(2025).pdf10.37394/232023.2025.5.13https://wseas.com/journals/msa/2025/a26msa-012(2025).pdf10.1038/nmeth.2694Wu, A. R. , Neff, N. F. , Kalisky, T. , Dalerba, P. , Treutlein, B. , & Rothenberg, M. E. , et al. Quantitative assessment of single-cell rnasequencing methods. Nature Methods. 10.1016/j.molcel.2015.04.005Kolodziejczyk, A. , Kim, J. K. , Svensson, V. , Marioni, J. , & Teichmann, S. . (2015). The technology and biology of single-cell rna sequencing. Molecular Cell, 58(4), 610-620. 10.1038/s12276-018-0071-8Byungjin, H. , Hyun, L. J. , & Duhee, B. . (2018). Single-cell rna sequencing technologies and bioinformatics pipelines. Experimental & Molecular Medicine, 50(8), 96-. 10.1038/nbt.2282Ramsköld, D. , Luo, S. , Wang, Y. C. , Li, R. , Deng, Q. , & Faridani, O. R. , et al. (2012). Fulllength mrna-seq from single-cell levels of rna and individual circulating tumor cells. Nature Biotechnology, 30(8), 777-782. 10.1101/gr.110882.110Islam, S. , Kjallquist, U. , Moliner, A. , Zajac, P. , Fan, J. B. , & Lonnerberg, P. , et al. (2011). Characterization of the single-cell transcriptional landscape by highly multiplex rna-seq. Genome Research, 21(7), 1160. 10.1016/j.celrep.2012.08.003Hashimshony, T. , Wagner, F. , Sher, N. , & Yanai, I. . (2012). Cel-seq: single-cell rna-seq by multiplexed linear amplification. Cell Reports, 2(3), 666-673. 10.1016/j.cell.2015.04.044Klein, A. , Mazutis, L. , Akartuna, I. , Tallapragada, N. , Veres, A. , & Li, V. , et al. (2015). Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell, 161(5), 1187-1201. 10.1126/science.1247651Jaitin, D. A. , Kenigsberg, E. , Keren-Shaul, H. , Elefant, N. , Paul, F. , & Zaretsky, I. , et al. (2014). Massively parallel single-cell rna-seq for marker-free decomposition of tissues into cell types. Science, 343(6172), 776-779. 10.1101/003236Soumillon, M. , Cacchiarelli, D. , Semrau, S. , Oudenaarden, A. V. , & Mikkelsen, T. . (2014). Characterization of directed differentiation by high-throughput single-cell rna-seq. bioRxiv. 10.1016/j.cell.2015.05.002Macosko, E. Z. , Basu, A. , Satija, R. , Nemesh, J. , & Mccarroll, S. A. . (2015). Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell, 161(5), 1202-1214. 10.1101/035758Christoph, Ziegenhain, Beate, Vieth, Swati, & Parekh, et al. (2017). Comparative analysis of single-cell rna sequencing methods. Molecular Cell. 10.1038/nmeth.2772Islam, S. , Zeisel, A. , Joost, S. , La Manno, G. , Zajac, P. , & Kasper, M. , et al. (2014). Quantitative single-cell rna-seq with unique molecular identifiers. Nature Methods, 11(2), 163-166. 10.1007/s00018-014-1637-9De Klerk, E. , Den Dunnen, J. T. , & ‘t Hoen, Peter A. C. (2014). Rna sequencing: from tagbased profiling to resolving complete transcript structure. Cellular & Molecular Life Sciences, 71(18), 3537-3551. 10.1093/bioinformatics/btr708Huang, W. , Li, L. , Myers, J. R. , & Marth, G. T. . (2011). Art: a next-generation sequencing read simulator. Bioinformatics. 10.1093/bioinformatics/btq365S., Balzer, K., Malde, A., & Lanzen, et al. (2010). Characteristics of 454 pyrosequencing data--enabling realistic simulation with flowsim. Bioinformatics, 26(18), i420-i425. 10.1093/nar/gks251Angly, F. E. , Dana, W. , Forest, R. , Philip, H. , & Tyson, G. W. . (2012). Grinder: a versatile amplicon and shotgun sequence simulator. Nucleic Acids Research, 40(12), e94. 10.1186/1756-0500-7-533Shcherbina, & Anna. (2014). Fastqsim: platform-independent data characterization and in silico read generation for ngs datasets. BMC Research Notes, 7(1), 1-12. 10.1093/bioinformatics/btv272Frazee, A. C. , Jaffe, A. E. , Ben, L. , & Leek, J. T. . (2015). Polyester: simulating rna-seq datasets with differential transcript expression. Bioinformatics(17), 2778-84. 10.1038/nrg.2016.57Escalona, M. , Rocha, S. , & Posada, D. . A comparison of tools for the simulation of genomic next-generation sequencing data. Nature Reviews Genetics. 10.1186/s13059-017-1305-0Zappia, L. , Phipson, B. , & Oshlack, A. . (2017). Splatter: simulation of single-cell rna sequencing data. Genome Biology, 18(1), 174. 10.1186/s13059-016-0947-7Lun, A. T. L. , Bach, K. , & Marioni, J. C. . (2016). Pooling across cells to normalize singlecell rna sequencing data with many zero counts. Genome biology, 17(1), 75. 10.1093/biostatistics/kxw055Lun, A. T. L. , & Marioni, J. C. . (2017). Overcoming confounding plate effects in differential expression analyses of single-cell rna-seq data. Biostatistics, 18(3), 451-464. 10.1371/journal.pcbi.1004333Vallejos, C. A. , Marioni, J. C. , Sylvia, R. , & Quaid, M. . (2015). Basics: bayesian analysis of single-cell sequencing data. PLoS Computational Biology, 11(6), e1004333. 10.1038/s41598-017-12989-xRizzetto, S. , Eltahla, A. A. , Lin, P. , Bull, R. , Lloyd, A. R. , & Ho, J. W. K. , et al. (2017). Impact of sequencing depth and read length on single cell rna sequencing data of t cells. Scientific Reports. 10.1093/bioinformatics/bts635Dobin, A. , Davis, C. A. , Schlesinger, F. , Drenkow, J. , & Gingeras, T. R. . (2012). Star: ultrafast universal rna-seq aligner. Bioinformatics, 29(1). 10.1093/bioinformatics/btp692Dewey, C. N. . (2010). Rna-seq gene expression estimation with read mapping uncertainty. Bioinformatics, 26(4), 493-500. 10.1186/s13059-018-1496-zángeles, Arzalluz-Luque, Ana, & Conesa. (2018). Single-cell rnaseq for the study of isoforms-how is that possible?. Genome biology.