Factor Models for Cancer Signatures
Physica A 462 (2016) 527-559
70 Pages Posted: 1 May 2016 Last revised: 10 Apr 2017
Date Written: April 28, 2016
We present a novel method for extracting cancer signatures by applying statistical risk models (See our paper at: http://ssrn.com/abstract=2732453) from quantitative finance to cancer genome data. Using 1389 whole genome sequenced samples from 14 cancers, we identify an "overall" mode of somatic mutational noise. We give a prescription for factoring out this noise and source code for fixing the number of signatures. We apply nonnegative matrix factorization (NMF) to genome data aggregated by cancer subtype and filtered using our method. The resultant signatures have substantially lower variability than those from unfiltered data. Also, the computational cost of signature extraction is cut by about a factor of 10. We find 3 novel cancer signatures, including a liver cancer dominant signature (96% contribution) and a renal cell carcinoma signature (70% contribution). Our method accelerates finding new cancer signatures and improves their overall stability. Reciprocally, the methods for extracting cancer signatures could have interesting applications in quantitative finance.
Keywords: factor models, principal components, statistical risk models, nonnegative matrix factorization, somatic mutations, cancer signatures, genome, exome, DNA, eRank, correlation, covariance, serial, cross-sectional, sample, matrix
JEL Classification: G00
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