Sufficient principal component regression for pattern discovery in transcriptomic data.

ABSTRACT:

Motivation

Methods for the global measurement of transcript abundance such as microarrays and RNA-Seq generate datasets in which the number of measured features far exceeds the number of observations. Extracting biologically meaningful and experimentally tractable insights from such data therefore requires high-dimensional prediction. Existing sparse linear approaches to this challenge have been stunningly successful, but some important issues remain. These methods can fail to select the correct features, predict poorly relative to non-sparse alternatives or ignore any unknown grouping structures for the features.

Results

We propose a method called SuffPCR that yields improved predictions in high-dimensional tasks including regression and classification, especially in the ty

SUBMITTER: Ding L 

PROVIDER: S-EPMC9194947 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature