Determining the optimal number of independent components for reproducible transcriptomic data analysis.

ABSTRACT:

Background

Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data.

Results

Here we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ran

SUBMITTER: Kairov U 

PROVIDER: S-EPMC5594474 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature