RT Journal Article
SR Electronic
T1 HiLDA: a statistical approach to investigate differences in mutational signatures
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 577452
DO 10.1101/577452
A1 Zhi Yang
A1 Priyatama Pandey
A1 Darryl Shibata
A1 David V. Conti
A1 Paul Marjoram
A1 Kimberly D. Siegmund
YR 2019
UL http://biorxiv.org/content/early/2019/03/15/577452.abstract
AB 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. We apply our method to somatic mutations in colon cancer with mutations classified by the time of occurrence, before or after tumor initiation. Applying the methods to 16 colon cancers, we found significant associations between the relative frequencies of mutational patterns and the time of occurrence of mutations. Our novel method provides higher statistical power for detecting differences in mutational signatures.