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2018 ; 34
(13
): i178-i186
Nephropedia Template TP
Bioinformatics
2018[Jul]; 34
(13
): i178-i186
PMID29949997
show ga
MOTIVATION: In many applications, inter-sample heterogeneity is crucial to
understanding the complex biological processes under study. For example, in
genomic analysis of cancers, each patient in a cohort may have a different driver
mutation, making it difficult or impossible to identify causal mutations from an
averaged view of the entire cohort. Unfortunately, many traditional methods for
genomic analysis seek to estimate a single model which is shared by all samples
in a population, ignoring this inter-sample heterogeneity entirely. In order to
better understand patient heterogeneity, it is necessary to develop practical,
personalized statistical models. RESULTS: To uncover this inter-sample
heterogeneity, we propose a novel regularizer for achieving patient-specific
personalized estimation. This regularizer operates by learning two latent
distance metrics-one between personalized parameters and one between clinical
covariates-and attempting to match the induced distances as closely as possible.
Crucially, we do not assume these distance metrics are already known. Instead, we
allow the data to dictate the structure of these latent distance metrics.
Finally, we apply our method to learn patient-specific, interpretable models for
a pan-cancer gene expression dataset containing samples from more than 30
distinct cancer types and find strong evidence of personalization effects between
cancer types as well as between individuals. Our analysis uncovers
sample-specific aberrations that are overlooked by population-level methods,
suggesting a promising new path for precision analysis of complex diseases such
as cancer. AVAILABILITY AND IMPLEMENTATION: Software for personalized linear and
personalized logistic regression, along with code to reproduce experimental
results, is freely available at github.com/blengerich/personalized_regression.
free
free
free
