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Accuracy of mutational signature software on correlated signatures.

Publication ,  Journal Article
Wu, Y; Chua, EHZ; Ng, AWT; Boot, A; Rozen, SG
Published in: Sci Rep
January 10, 2022

Mutational signatures are characteristic patterns of mutations generated by exogenous mutagens or by endogenous mutational processes. Mutational signatures are important for research into DNA damage and repair, aging, cancer biology, genetic toxicology, and epidemiology. Unsupervised learning can infer mutational signatures from the somatic mutations in large numbers of tumors, and separating correlated signatures is a notable challenge for this task. To investigate which methods can best meet this challenge, we assessed 18 computational methods for inferring mutational signatures on 20 synthetic data sets that incorporated varying degrees of correlated activity of two common mutational signatures. Performance varied widely, and four methods noticeably outperformed the others: hdp (based on hierarchical Dirichlet processes), SigProExtractor (based on multiple non-negative matrix factorizations over resampled data), TCSM (based on an approach used in document topic analysis), and mutSpec.NMF (also based on non-negative matrix factorization). The results underscored the complexities of mutational signature extraction, including the importance and difficulty of determining the correct number of signatures and the importance of hyperparameters. Our findings indicate directions for improvement of the software and show a need for care when interpreting results from any of these methods, including the need for assessing sensitivity of the results to input parameters.

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Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

January 10, 2022

Volume

12

Issue

1

Start / End Page

390

Location

England

Related Subject Headings

  • Software
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Neoplasms
  • Mutation
  • Humans
  • High-Throughput Nucleotide Sequencing
  • DNA Mutational Analysis
  • Biomarkers, Tumor
  • Algorithms
 

Citation

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Wu, Y., Chua, E. H. Z., Ng, A. W. T., Boot, A., & Rozen, S. G. (2022). Accuracy of mutational signature software on correlated signatures. Sci Rep, 12(1), 390. https://doi.org/10.1038/s41598-021-04207-6
Wu, Yang, Ellora Hui Zhen Chua, Alvin Wei Tian Ng, Arnoud Boot, and Steven G. Rozen. “Accuracy of mutational signature software on correlated signatures.Sci Rep 12, no. 1 (January 10, 2022): 390. https://doi.org/10.1038/s41598-021-04207-6.
Wu Y, Chua EHZ, Ng AWT, Boot A, Rozen SG. Accuracy of mutational signature software on correlated signatures. Sci Rep. 2022 Jan 10;12(1):390.
Wu, Yang, et al. “Accuracy of mutational signature software on correlated signatures.Sci Rep, vol. 12, no. 1, Jan. 2022, p. 390. Pubmed, doi:10.1038/s41598-021-04207-6.
Wu Y, Chua EHZ, Ng AWT, Boot A, Rozen SG. Accuracy of mutational signature software on correlated signatures. Sci Rep. 2022 Jan 10;12(1):390.

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

January 10, 2022

Volume

12

Issue

1

Start / End Page

390

Location

England

Related Subject Headings

  • Software
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Neoplasms
  • Mutation
  • Humans
  • High-Throughput Nucleotide Sequencing
  • DNA Mutational Analysis
  • Biomarkers, Tumor
  • Algorithms