Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization
Abstract Background The spectrum of mutations in a collection of cancer genomes can be described by a mixture of a few mutational signatures. The mutational signatures can be found using non-negative matrix factorization (NMF). To extract the mutational signatures we have to assume a distribution fo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-05-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05304-1 |
| _version_ | 1797827325470965760 |
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| author | Marta Pelizzola Ragnhild Laursen Asger Hobolth |
| author_facet | Marta Pelizzola Ragnhild Laursen Asger Hobolth |
| author_sort | Marta Pelizzola |
| collection | DOAJ |
| description | Abstract Background The spectrum of mutations in a collection of cancer genomes can be described by a mixture of a few mutational signatures. The mutational signatures can be found using non-negative matrix factorization (NMF). To extract the mutational signatures we have to assume a distribution for the observed mutational counts and a number of mutational signatures. In most applications, the mutational counts are assumed to be Poisson distributed, and the rank is chosen by comparing the fit of several models with the same underlying distribution and different values for the rank using classical model selection procedures. However, the counts are often overdispersed, and thus the Negative Binomial distribution is more appropriate. Results We propose a Negative Binomial NMF with a patient specific dispersion parameter to capture the variation across patients and derive the corresponding update rules for parameter estimation. We also introduce a novel model selection procedure inspired by cross-validation to determine the number of signatures. Using simulations, we study the influence of the distributional assumption on our method together with other classical model selection procedures. We also present a simulation study with a method comparison where we show that state-of-the-art methods are highly overestimating the number of signatures when overdispersion is present. We apply our proposed analysis on a wide range of simulated data and on two real data sets from breast and prostate cancer patients. On the real data we describe a residual analysis to investigate and validate the model choice. Conclusions With our results on simulated and real data we show that our model selection procedure is more robust at determining the correct number of signatures under model misspecification. We also show that our model selection procedure is more accurate than the available methods in the literature for finding the true number of signatures. Lastly, the residual analysis clearly emphasizes the overdispersion in the mutational count data. The code for our model selection procedure and Negative Binomial NMF is available in the R package SigMoS and can be found at https://github.com/MartaPelizzola/SigMoS . |
| first_indexed | 2024-04-09T12:46:09Z |
| format | Article |
| id | doaj.art-5ac27b7730f44738a081069ae12fc69a |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-09T12:46:09Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-5ac27b7730f44738a081069ae12fc69a2023-05-14T11:29:46ZengBMCBMC Bioinformatics1471-21052023-05-0124112410.1186/s12859-023-05304-1Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorizationMarta Pelizzola0Ragnhild Laursen1Asger Hobolth2Department of Mathematics, Aarhus UniversityDepartment of Mathematics, Aarhus UniversityDepartment of Mathematics, Aarhus UniversityAbstract Background The spectrum of mutations in a collection of cancer genomes can be described by a mixture of a few mutational signatures. The mutational signatures can be found using non-negative matrix factorization (NMF). To extract the mutational signatures we have to assume a distribution for the observed mutational counts and a number of mutational signatures. In most applications, the mutational counts are assumed to be Poisson distributed, and the rank is chosen by comparing the fit of several models with the same underlying distribution and different values for the rank using classical model selection procedures. However, the counts are often overdispersed, and thus the Negative Binomial distribution is more appropriate. Results We propose a Negative Binomial NMF with a patient specific dispersion parameter to capture the variation across patients and derive the corresponding update rules for parameter estimation. We also introduce a novel model selection procedure inspired by cross-validation to determine the number of signatures. Using simulations, we study the influence of the distributional assumption on our method together with other classical model selection procedures. We also present a simulation study with a method comparison where we show that state-of-the-art methods are highly overestimating the number of signatures when overdispersion is present. We apply our proposed analysis on a wide range of simulated data and on two real data sets from breast and prostate cancer patients. On the real data we describe a residual analysis to investigate and validate the model choice. Conclusions With our results on simulated and real data we show that our model selection procedure is more robust at determining the correct number of signatures under model misspecification. We also show that our model selection procedure is more accurate than the available methods in the literature for finding the true number of signatures. Lastly, the residual analysis clearly emphasizes the overdispersion in the mutational count data. The code for our model selection procedure and Negative Binomial NMF is available in the R package SigMoS and can be found at https://github.com/MartaPelizzola/SigMoS .https://doi.org/10.1186/s12859-023-05304-1Cancer genomicsCross-validationModel checkingModel selectionMutational signaturesNegative Binomial |
| spellingShingle | Marta Pelizzola Ragnhild Laursen Asger Hobolth Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization BMC Bioinformatics Cancer genomics Cross-validation Model checking Model selection Mutational signatures Negative Binomial |
| title | Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization |
| title_full | Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization |
| title_fullStr | Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization |
| title_full_unstemmed | Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization |
| title_short | Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization |
| title_sort | model selection and robust inference of mutational signatures using negative binomial non negative matrix factorization |
| topic | Cancer genomics Cross-validation Model checking Model selection Mutational signatures Negative Binomial |
| url | https://doi.org/10.1186/s12859-023-05304-1 |
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