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Assessing the performance of methods for cell clustering from single-cell DNA sequencing data.
Rituparna Khan1, Xian Mallory1
1Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America.
Plos Computational Biology
|October 12, 2023
Summary
This study compares six computational tools for analyzing intra-tumor heterogeneity using single-cell DNA sequencing. BnpC and SCG offer the highest accuracy, with BnpC excelling in speed for large datasets.
Area of Science:
- Genomics
- Computational Biology
- Cancer Research
Background:
- Intra-tumor heterogeneity (ITH) arises from multiple subclones within a single tumor, impacting cancer progression and treatment.
- Single-cell DNA sequencing (scDNAseq) is crucial for characterizing ITH by identifying unique mutations in each subclone.
- Existing computational tools for ITH analysis often focus on evolutionary tree reconstruction, which can be computationally intensive.
Purpose of the Study:
- To provide a comprehensive and objective comparison of state-of-the-art cell clustering tools for scDNAseq data.
- To evaluate the performance of these tools under various parameter settings and data conditions, including ultra-low coverage.
- To guide researchers in selecting appropriate tools for subclonality characterization without complex tree reconstruction.
Main Methods:
- Evaluated six tools: SCG, BnpC, SCClone, RobustClone, SCITE, and SBMClone.
- Utilized a custom-designed simulator for cell clustering to generate diverse datasets.
- Assessed performance based on clustering accuracy, specificity, sensitivity, and running time, including an ultra-low coverage dataset for SBMClone.
Main Results:
- BnpC and SCG demonstrated the highest clustering accuracy, with BnpC showing superior performance in speed for large cell numbers and accuracy for numerous clusters.
- SCClone exhibited the highest accuracy in determining the number of clusters.
- RobustClone and SCITE had the lowest accuracy; SCITE overestimated clusters, while RobustClone underestimated them with low sensitivity. SBMClone performed well on ultra-low coverage data.
Conclusions:
- BnpC and SCG are recommended for accurate and efficient subclonality characterization in scDNAseq data.
- SCClone is best for accurately estimating the number of subclones.
- SBMClone is highly suitable for large-scale, ultra-low coverage scDNAseq datasets, offering robust clustering performance.
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