LoRA-TV: read depth profile-based clustering of tumor cells in single-cell sequencing
Abstract Single-cell sequencing has revolutionized our ability to dissect the heterogeneity within tumor populations. In this study, we present LoRA-TV (Low Rank Approximation with Total Variation), a novel method for clustering tumor cells based on the read depth profiles derived from single-cell s...
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Veröffentlicht in: | Briefings in bioinformatics 2024-06, Vol.25 (4) |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Abstract
Single-cell sequencing has revolutionized our ability to dissect the heterogeneity within tumor populations. In this study, we present LoRA-TV (Low Rank Approximation with Total Variation), a novel method for clustering tumor cells based on the read depth profiles derived from single-cell sequencing data. Traditional analysis pipelines process read depth profiles of each cell individually. By aggregating shared genomic signatures distributed among individual cells using low-rank optimization and robust smoothing, the proposed method enhances clustering performance. Results from analyses of both simulated and real data demonstrate its effectiveness compared with state-of-the-art alternatives, as supported by improvements in the adjusted Rand index and computational efficiency. |
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ISSN: | 1467-5463 1477-4054 1477-4054 |
DOI: | 10.1093/bib/bbae277 |