Obtaining insights from high-dimensional data: sparse principal covariates regression.

ABSTRACT:

Background

Data analysis methods are usually subdivided in two distinct classes: There are methods for prediction and there are methods for exploration. In practice, however, there often is a need to learn from the data in both ways. For example, when predicting the antibody titers a few weeks after vaccination on the basis of genomewide mRNA transcription rates, also mechanistic insights about the effect of vaccinations on the immune system are sought. Principal covariates regression (PCovR) is a method that combines both purposes. Yet, it misses insightful representations of the data as these include all the variables.

Results

Here, we propose a sparse extension of principal covariates regression such that the resulting solutions are based on an automatically selected subs

SUBMITTER: Van Deun K 

PROVIDER: S-EPMC5870402 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature