TY - GEN
T1 - A mixture model for signature discovery from sparse mutation data
AU - Sason, Itay
AU - Chen, Yuexi
AU - Leiserson, Mark D.M.
AU - Sharan, Roded
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Mutational signatures and their exposures are key to understanding the processes that shape cancer genomes with applications to diagnosis and treatment. Yet current signature discovery or refitting approaches are limited to relatively rich mutation data that comes from whole-genome or whole-exome sequencing. Recently, orders of magnitude sparser data sets from gene panel sequencing have become increasingly available in the clinical setting. Such data have typically less than 10 mutations per sample, making them challenging to deal with using current approaches. Here we suggest a novel mixture model for sparse mutation data. In application to synthetic sparse datasets and real gene panel sequences it is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature.
AB - Mutational signatures and their exposures are key to understanding the processes that shape cancer genomes with applications to diagnosis and treatment. Yet current signature discovery or refitting approaches are limited to relatively rich mutation data that comes from whole-genome or whole-exome sequencing. Recently, orders of magnitude sparser data sets from gene panel sequencing have become increasingly available in the clinical setting. Such data have typically less than 10 mutations per sample, making them challenging to deal with using current approaches. Here we suggest a novel mixture model for sparse mutation data. In application to synthetic sparse datasets and real gene panel sequences it is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature.
UR - http://www.scopus.com/inward/record.url?scp=85084259165&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-45257-5_34
DO - 10.1007/978-3-030-45257-5_34
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AN - SCOPUS:85084259165
SN - 9783030452568
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 271
EP - 272
BT - Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings
A2 - Schwartz, Russell
PB - Springer
Y2 - 10 May 2020 through 13 May 2020
ER -