Assessing the Performance of Methods for Cell Clustering from Single-cell DNA Sequencing Data

Overview

Journal PLoS Comput Biol

Specialty Biology

Date 2023 Oct 12

PMID 37824596

Authors

Rituparna Khan

Xian Mallory

Affiliations

Soon will be listed here.

Abstract

Background: Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings.

Results: In this paper, we evaluated six state-of-the-art cell clustering tools-SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone-on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate.

Conclusion: From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets.

References

Burrell R, McGranahan N, Bartek J, Swanton C . The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013; 501(7467):338-45. DOI: 10.1038/nature12625. View

de Bourcy C, De Vlaminck I, Kanbar J, Wang J, Gawad C, Quake S . A quantitative comparison of single-cell whole genome amplification methods. PLoS One. 2014; 9(8):e105585. PMC: 4138190. DOI: 10.1371/journal.pone.0105585. View

Yap T, Gerlinger M, Futreal P, Pusztai L, Swanton C . Intratumor heterogeneity: seeing the wood for the trees. Sci Transl Med. 2012; 4(127):127ps10. DOI: 10.1126/scitranslmed.3003854. View

Feuk L, Carson A, Scherer S . Structural variation in the human genome. Nat Rev Genet. 2006; 7(2):85-97. DOI: 10.1038/nrg1767. View

Turajlic S, Sottoriva A, Graham T, Swanton C . Resolving genetic heterogeneity in cancer. Nat Rev Genet. 2019; 20(7):404-416. DOI: 10.1038/s41576-019-0114-6. View

Sharp A, Cheng Z, Eichler E . Structural variation of the human genome. Annu Rev Genomics Hum Genet. 2006; 7:407-42. DOI: 10.1146/annurev.genom.7.080505.115618. View

Dillekas H, Rogers M, Straume O . Are 90% of deaths from cancer caused by metastases?. Cancer Med. 2019; 8(12):5574-5576. PMC: 6745820. DOI: 10.1002/cam4.2474. View

Jahn K, Kuipers J, Beerenwinkel N . Tree inference for single-cell data. Genome Biol. 2016; 17:86. PMC: 4858868. DOI: 10.1186/s13059-016-0936-x. View

Hou Y, Song L, Zhu P, Zhang B, Tao Y, Xu X . Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell. 2012; 148(5):873-85. DOI: 10.1016/j.cell.2012.02.028. View

10.

Wang Y, Navin N . Advances and applications of single-cell sequencing technologies. Mol Cell. 2015; 58(4):598-609. PMC: 4441954. DOI: 10.1016/j.molcel.2015.05.005. View

11.

Zafar H, Wang Y, Nakhleh L, Navin N, Chen K . Monovar: single-nucleotide variant detection in single cells. Nat Methods. 2016; 13(6):505-7. PMC: 4887298. DOI: 10.1038/nmeth.3835. View

12.

Roth A, McPherson A, Laks E, Biele J, Yap D, Wan A . Clonal genotype and population structure inference from single-cell tumor sequencing. Nat Methods. 2016; 13(7):573-6. DOI: 10.1038/nmeth.3867. View

13.

Ross E, Markowetz F . OncoNEM: inferring tumor evolution from single-cell sequencing data. Genome Biol. 2016; 17:69. PMC: 4832472. DOI: 10.1186/s13059-016-0929-9. View

14.

Chen Z, Gong F, Wan L, Ma L . RobustClone: a robust PCA method for tumor clone and evolution inference from single-cell sequencing data. Bioinformatics. 2020; 36(11):3299-3306. DOI: 10.1093/bioinformatics/btaa172. View

15.

Lawrence M, Stojanov P, Polak P, Kryukov G, Cibulskis K, Sivachenko A . Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013; 499(7457):214-218. PMC: 3919509. DOI: 10.1038/nature12213. View

16.

Farswan A, Gupta R, Gupta A . ARCANE-ROG: Algorithm for reconstruction of cancer evolution from single-cell data using robust graph learning. J Biomed Inform. 2022; 129:104055. DOI: 10.1016/j.jbi.2022.104055. View

17.

Zong C, Lu S, Chapman A, Xie X . Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science. 2012; 338(6114):1622-6. PMC: 3600412. DOI: 10.1126/science.1229164. View

18.

Yu Z, Liu H, Du F, Tang X . GRMT: Generative Reconstruction of Mutation Tree From Scratch Using Single-Cell Sequencing Data. Front Genet. 2021; 12:692964. PMC: 8212059. DOI: 10.3389/fgene.2021.692964. View

19.

Davis A, Navin N . Computing tumor trees from single cells. Genome Biol. 2016; 17(1):113. PMC: 4881295. DOI: 10.1186/s13059-016-0987-z. View

20.

McGranahan N, Swanton C . Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. Cell. 2017; 168(4):613-628. DOI: 10.1016/j.cell.2017.01.018. View