Files in this item
Statistical challenges in mutational signature analyses of cancer sequencing data
Item metadata
dc.contributor.author | Velasco Pardo, Victor | |
dc.contributor.author | Papathomas, Michail | |
dc.contributor.author | Lynch, Andy | |
dc.contributor.editor | Bispo, Regina | |
dc.contributor.editor | Henriques-Rodrigues, Lígia | |
dc.contributor.editor | Alpizar-Jara, Russell | |
dc.contributor.editor | de Carvalho, Miguel | |
dc.date.accessioned | 2023-11-29T00:37:31Z | |
dc.date.available | 2023-11-29T00:37:31Z | |
dc.date.issued | 2022-11-29 | |
dc.identifier | 277737263 | |
dc.identifier | b9bda3b8-72bc-4b31-b4a6-7706749802c7 | |
dc.identifier | 85144376920 | |
dc.identifier.citation | Velasco Pardo , V , Papathomas , M & Lynch , A 2022 , Statistical challenges in mutational signature analyses of cancer sequencing data . in R Bispo , L Henriques-Rodrigues , R Alpizar-Jara & M de Carvalho (eds) , Recent developments in statistics and data science : SPE2021, Évora, Portugal, October 13–16 . Springer Proceedings in Mathematics & Statistics , vol. 398 , Springer , Cham , pp. 241-258 , XXV Congress of the Portuguese Statistical Society , Évora , Portugal , 13/10/21 . https://doi.org/10.1007/978-3-031-12766-3_17 | en |
dc.identifier.citation | conference | en |
dc.identifier.isbn | 9783031127656 | |
dc.identifier.isbn | 9783031127663 | |
dc.identifier.issn | 2194-1009 | |
dc.identifier.other | ORCID: /0000-0002-7876-7338/work/124490039 | |
dc.identifier.other | ORCID: /0000-0002-5897-695X/work/124490329 | |
dc.identifier.uri | https://hdl.handle.net/10023/28787 | |
dc.description | Funding: We thank The Melville Trust for the Care and Cure of Cancer for providing financial support. | en |
dc.description.abstract | Cancer is a disease driven and characterised by mutations in the DNA. Different categorisations of DNA mutations have allowed the identification of patterns that can act as signatures for the processes that have governed the life of the cancer. Over the last decade, research groups have identified more than 100 such signatures. Mutational signature analyses are improving our understanding of cancer aetiology and have the potential to play a role in diagnosis, prognosis and treatment choice. Consisting of the estimation of probability mass functions or weights determining non-negative weighted combinations, they are perhaps unique amongst comparable analyses in the medical literature, in that no confidence intervals or other representations of uncertainty are demanded when reporting the results. Here, we review the key statistical challenges for the field, assess the potential of existing approaches to adapt to those challenges, and comment on what we think are promising directions. As we deal with data that are noisy and heterogeneous, we evaluate how to present them so that models use all the information available. Often posed as a matrix factorisation problem, we argue that a fully probabilistic approach is required to quantify uncertainty around model parameters and to underpin principled study design. Lastly, we argue that novel methodology is required to evaluate uncertainties in analyses where prior information is available. | |
dc.format.extent | 298744 | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.relation.ispartof | Recent developments in statistics and data science | en |
dc.relation.ispartofseries | Springer Proceedings in Mathematics & Statistics | en |
dc.subject | Biostatistics | en |
dc.subject | Bioinformatics | en |
dc.subject | Cancer | en |
dc.subject | Genomics | en |
dc.subject | Next generation sequencing | en |
dc.subject | Whole genome sequencing | en |
dc.subject | QA Mathematics | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | QH426 Genetics | en |
dc.subject | RC0254 Neoplasms. Tumors. Oncology (including Cancer) | en |
dc.subject | NS | en |
dc.subject | SDG 3 - Good Health and Well-being | en |
dc.subject | MCC | en |
dc.subject.lcc | QA | en |
dc.subject.lcc | QA75 | en |
dc.subject.lcc | QH426 | en |
dc.subject.lcc | RC0254 | en |
dc.title | Statistical challenges in mutational signature analyses of cancer sequencing data | en |
dc.type | Conference item | en |
dc.contributor.institution | University of St Andrews. Statistics | en |
dc.contributor.institution | University of St Andrews. Centre for Research into Ecological & Environmental Modelling | en |
dc.contributor.institution | University of St Andrews. St Andrews Bioinformatics Unit | en |
dc.contributor.institution | University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis | en |
dc.contributor.institution | University of St Andrews. Cellular Medicine Division | en |
dc.contributor.institution | University of St Andrews. School of Medicine | en |
dc.identifier.doi | https://doi.org/10.1007/978-3-031-12766-3_17 | |
dc.date.embargoedUntil | 2023-11-29 | |
dc.identifier.url | https://ocs.springer.com/prom/home/SPE2021 | en |
This item appears in the following Collection(s)
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.