平特五不中

Aur茅lie Labbe, PhD

Assistant Professor, Departments of Epidemiology, Biostatistics & Occupational Health and Psychiatry, 平特五不中

Principal component of explained variance

ALL ARE WELCOME

Abstract:

The genomics era has led to an increase in the dimensionality of the data collected to investigate biological questions. In this context, dimension-reduction techniques can be used to summarize high-dimensional signals into low-dimensional ones, to further test for association with one or more covariates of interest. We revisit one such approach, previously known as Principal Component of Heritability and renamed here as Principal Component of Explained Variance (PCEV). As its name suggests, the PCEV seeks a linear combination of outcomes in an optimal manner, by maximising the proportion of variance explained by one or several covariates of interest. By construction, this

method optimises power but limited by its computational complexity, it has unfortunately received little attention in the past. Here, we propose a general analytical PCEV framework that builds on the assets of the original method, i.e. conceptually simple and free of tuning parameters. Moreover, our framework extends the range of applications of the original procedure by providing a computationally simple strategy for high-dimensional outcomes, along with exact and asymptotic testing procedures that drastically reduce its computational cost. We investigate the merits of the

PCEV using an extensive set of simulations. Furthermore, the use of the PCEV approach will be illustrated using three examples taken from the epigenetics and brain imaging areas.

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