AI Article Synopsis
- Single-cell sequencing has changed how we analyze the diversity in tumor cells, allowing for more detailed insights.
- The new method, LoRA-TV, improves the clustering of tumor cells by using read depth profiles and optimizing data aggregation instead of analyzing each cell separately.
- Results show that LoRA-TV outperforms existing methods in both accuracy and speed, confirming its utility through various data analyses.
Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11179121 | PMC |
| http://dx.doi.org/10.1093/bib/bbae277 | DOI Listing |
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