A semiparametric kernel independence test with application to mutational signatures.

ABSTRACT: Cancers arise owing to somatic mutations, and the characteristic combinations of somatic mutations form mutational signatures. Despite many mutational signatures being identified, mutational processes underlying a number of mutational signatures remain unknown, which hinders the identification of interventions that may reduce somatic mutation burdens and prevent the development of cancer. We demonstrate that the unknown cause of a mutational signature can be inferred by the associated signatures with known etiology. However, existing association tests are not statistically powerful due to excess zeros in mutational signatures data. To address this limitation, we propose a semiparametric kernel independence test (SKIT). The SKIT statistic is defined as the integrated squared distance betwee

SUBMITTER: Lee D 

PROVIDER: S-EPMC9070557 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature