Optimal dimensionality selection for independent component analysis of transcriptomic data.

ABSTRACT:

Background

Independent component analysis is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, independent component analysis effectively reveals both the source signals of the transcriptome as co-regulated gene sets, and the activity levels of the underlying regulators across diverse experimental conditions. Two major variables that affect the final gene sets are the diversity of the expression profiles contained in the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; how

SUBMITTER: McConn JL 

PROVIDER: S-EPMC8653613 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature