Robustica: customizable robust independent component analysis.

ABSTRACT:

Background

Independent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are dependent on the choice of clustering method and on computing a potentially biased and large Pearson distance matrix.

Results

We present robustica, a Python-based package to compute robust independent components with a fully customizable clustering algorithm and distance metric. Here, we exploited its customizability to revisit and optimize robust ICA systematically. Of the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering

SUBMITTER: Anglada-Girotto M 

PROVIDER: S-EPMC9721028 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature