ABSTRACT
Motivation 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. From the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering independent components across ICA iterations. After confirming the bias introduced with Pearson distances, we created a subroutine that infers and corrects the components’ signs across ICA iterations to enable using Euclidean distance. Our subroutine effectively corrected the bias while simultaneously increasing the precision, robustness, and memory efficiency of the algorithm. Finally, we show the applicability of robustica by dissecting over 500 tumor samples from low-grade glioma (LGG) patients, where we define a new gene expression module with the key modulators of tumor aggressiveness downregulated upon IDH1 mutation.
Availability and implementation robustica is written in Python under the open-source BSD 3-Clause license. The source code and documentation are freely available at https://github.com/CRG-CNAG/robustica. Additionally, all scripts to reproduce the work presented are available at https://github.com/MiqG/publication_robustica.
Contact miquel.anglada{at}crg.eu
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We included supplementary figures in the main text to provide a more complete story at first read.
