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Journal Articles Frontiers in Genetics Year : 2023

Performance comparisons between clustering models for reconstructing NGS results from technical replicates

Yue Zhai
  • Function : Author
Maxime Vallée
  • Function : Author
Jean Iwaz
  • Function : Author
Pascal Roy
  • Function : Author

Abstract

To improve the performance of individual DNA sequencing results, researchers often use replicates from the same individual and various statistical clustering models to reconstruct a high-performance callset. Here, three technical replicates of genome NA12878 were considered and five model types were compared (consensus, latent class, Gaussian mixture, Kamila–adapted k-means, and random forest) regarding four performance indicators: sensitivity, precision, accuracy, and F1-score. In comparison with no use of a combination model, i) the consensus model improved precision by 0.1%; ii) the latent class model brought 1% precision improvement (97%–98%) without compromising sensitivity (= 98.9%); iii) the Gaussian mixture model and random forest provided callsets with higher precisions (both >99%) but lower sensitivities; iv) Kamila increased precision (>99%) and kept a high sensitivity (98.8%); it showed the best overall performance. According to precision and F1-score indicators, the compared non-supervised clustering models that combine multiple callsets are able to improve sequencing performance vs. previously used supervised models. Among the models compared, the Gaussian mixture model and Kamila offered non-negligible precision and F1-score improvements. These models may be thus recommended for callset reconstruction (from either biological or technical replicates) for diagnostic or precision medicine purposes.

Dates and versions

hal-04382909 , version 1 (09-01-2024)

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Yue Zhai, Claire Bardel, Maxime Vallée, Jean Iwaz, Pascal Roy. Performance comparisons between clustering models for reconstructing NGS results from technical replicates. Frontiers in Genetics, 2023, 14, ⟨10.3389/fgene.2023.1148147⟩. ⟨hal-04382909⟩
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