SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data

Yu, Zhenhua; Du, Fang; Song, Lijuan

doi:10.3389/fgene.2022.823941

Cited by 7 publications

(8 citation statements)

References 39 publications

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“…We compare bmVAE to four state-of-the-art methods including RobustClone ( Chen et al , 2020 ), BnpC ( Borgsmüller et al , 2020 ), AMC ( Yu and Du, 2022 ) and SCClone ( Yu et al , 2022 ), and two baseline dimensionality reduction methods including PCA ( Joliffe and Morgan, 1992 ) and t-SNE ( van der Maaten and Hinton, 2008 ). We select these methods for evaluation as they are significantly different from each other in the way of clustering mutation data.…”

Section: Methodsmentioning

confidence: 99%

“…To date, several bioinformatics methods have been proposed to cluster single-cell mutation data ( Borgsmüller et al , 2020 ; Chen et al , 2020 ; Myers et al , 2020 ; Ross and Markowetz, 2016 ; Roth et al , 2016 ; Yu et al , 2022 ; Yu and Du, 2022 ; Zafar et al , 2019 ). These methods can be divided into two classes: detecting tumor clones with or without phylogeny inference.…”

Section: Introductionmentioning

confidence: 99%

“…One disadvantage of SBMClone is that it does not output genotypes of inferred clones. SCClone ( Yu et al , 2022 ) employs an expectation–maximization algorithm to jointly infer tumor clones and their mutational states, but suffers from high computational complexity on large datasets. Taken together, clustering high-dimensional single-cell mutation data is a hard challenge, and more efficient and accurate methods are still needed for scDNA-seq mutation data.…”

Section: Introductionmentioning

confidence: 99%

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bmVAE: a variational autoencoder method for clustering single-cell mutation data

Yan

2022

Bioinformatics

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Motivation Genetic intra-tumor heterogeneity (ITH) characterizes the differences in genomic variations between tumor clones, and accurately unmasking ITH is important for personalized cancer therapy. Single-cell DNA sequencing (scDNA-seq) now emerges as a powerful means for deciphering underlying ITH based on point mutations of single cells. However, detecting tumor clones from single-cell mutation data remains challenging due to the error-prone and discrete nature of the data. Results We introduce bmVAE, a bioinformatics tool for learning low-dimensional latent representation of single cell based on a variational autoencoder and then clustering cells into subpopulations in the latent space. bmVAE takes single-cell binary mutation data as inputs, and outputs inferred cell subpopulations as well as their genotypes. To achieve this, the bmVAE framework is designed to consist of three modules including dimensionality reduction, cell clustering and genotype estimation. We assess the method on various synthetic datasets where different factors including false negative rate, data size and data heterogeneity are considered in simulation, and further demonstrate its effectiveness on two real datasets. The results suggest bmVAE is highly effective in reasoning ITH, and performs competitive to existing methods. Availability bmVAE is freely available at https://github.com/zhyu-lab/bmvae. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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bmVAE: a variational autoencoder method for clustering single-cell mutation data

Yan

2022

Bioinformatics

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“…The vast majority of studies investigate intra-tumor heterogeneity based on bulk DNA sequencing (DNA-seq) or single-cell DNA-seq (scDNA-seq) data [4, 5]. Unfortunately, bulk DNA-seq measures a mixture of millions of cells from different tumors and healthy cells and thus provides only aggregated information of variant allele frequencies.…”

Section: Mainmentioning

confidence: 99%

Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues

Shafighi

Geras

Jurzysta

et al. 2022

Preprint

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Spatial and genomic heterogeneity of tumors is the key for cancer progression, treatment, and survival. However, a technology for direct mapping the clones in the tumor tissue based on point mutations is lacking. Here, we propose Tumoroscope, the first probabilistic model that accurately infers cancer clones and their high-resolution localization by integrating pathological images, whole exome sequencing, and spatial transcriptomics data. In contrast to previous methods, Tumoroscope explicitly addresses the problem of deconvoluting the proportions of clones in spatial transcriptomics spots. Applied to a reference prostate cancer dataset and a newly generated breast cancer dataset, Tumoroscope reveals spatial patterns of clone colocalization and mutual exclusion in sub-areas of the tumor tissue. We further infer clone-specific gene expression levels and the most highly expressed genes for each clone. In summary, Tumoroscope enables an integrated study of the spatial, genomic, and phenotypic organization of tumors.

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“…Specifically, the false negative, false positive and missing rates shall be incorporated in the design of the clustering algorithm. The few scDNAseq-based tools that aim at only the cell clustering, not the lineage tree inference, are SCG [44], BnpC [25], SCClone [45], Robust-Clone [46] and ARCANE-ROG [47]. Here we skip discussing RobustClone because it does not have a friendly user interface and is not as popular.…”

Section: Introductionmentioning

confidence: 99%

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

Khan

Romanovitch

2022

Preprint

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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 benchmarked three state-of-the-art cell clustering tools–SCG, BnpC and SCClone–on simulated datasets given a variety of parameter settings and a real dataset. We designed a simulator specifically for cell clustering, and compared the three methods' performances in terms of their clustering accuracy, genotyping accuracy and running time. Conclusion: From the benchmark study, we conclude that BnpC's clustering accuracy is the highest of all three methods. SCG's accuracy is very close to BnpC's, but it is much faster than the other two methods especially when the cell number is within 1000. When there are a large number of single cells (> 1500), BnpC is highly recommended due to its scalability while not sacrificing the clustering accuracy. Of the three methods, SCClone has the highest accuracy in estimating the number of clusters especially when the underlying number of cluster is high. When the variance of the cluster sizes is high, all three methods' clustering accuracy drops. To improve the clustering accuracy while cluster sizes' variance is high is potentially a future work in scDNAseq cell clustering.

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SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data

Cited by 7 publications

References 39 publications

bmVAE: a variational autoencoder method for clustering single-cell mutation data

bmVAE: a variational autoencoder method for clustering single-cell mutation data

Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues

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

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