HiLDA: a statistical approach to investigate differences in mutational signatures

We propose a hierarchical latent Dirichlet allocation model (HiLDA) for characterizing somatic mutation data in cancer. The method allows us to infer mutational patterns and their relative frequencies in a set of tumor mutational catalogs and to compare the estimated frequencies between tumor sets....

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Detalles Bibliográficos
Autores principales: Yang, Zhi, Pandey, Priyatama, Shibata, Darryl, Conti, David V., Marjoram, Paul, Siegmund, Kimberly D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Bioinformatics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6717498/
https://www.ncbi.nlm.nih.gov/pubmed/31523512
http://dx.doi.org/10.7717/peerj.7557

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6717498/
https://www.ncbi.nlm.nih.gov/pubmed/31523512
http://dx.doi.org/10.7717/peerj.7557

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