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Le TT, Fu W, Moore JH. Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Bioinformatics, in press (2019). [PubMed] [Bioinformatics]

MOTIVATION: Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programming to recommend an optimized analysis pipeline for the data scientist's prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data.

RESULTS: We introduce two new features implemented in TPOT that helps increase the system's scalability: Feature Set Selector and Template. Feature Set Selector (FSS) provides the option to specify subsets of the features as separate datasets, assuming the signals come from one or more of these specific data subsets. FSS increases TPOT's efficiency in application on big data by slicing the entire dataset into smaller sets of features and allowing genetic programming to select the best subset in the final pipeline. Template enforces type constraints with strongly typed genetic programming and enables the incorporation of FSS at the beginning of each pipeline. Consequently, FSS and Template help reduce TPOT computation time and may provide more interpretable results. Our simulations show TPOT-FSS significantly outperforms a tuned XGBoost model and standard TPOT implementation. We apply TPOT-FSS to real RNA-Seq data from a study of major depressive disorder. Independent of the previous study that identified significant association with depression severity of two modules, TPOT-FSS corroborates that one of the modules is largely predictive of the clinical diagnosis of each individual.

AVAILABILITY: Detailed simulation and analysis code needed to reproduce the results in this study is available at https://github.com/lelaboratoire/tpot-fss. Implementation of the new TPOT operators is available at https://github.com/EpistasisLab/tpot.

Manduchi E, Hemerich D, van Setten J, Tragante V, Harakalova M, Pei J, Williams SM, van der Harst P, Asselbergs FW, Moore JH. A comparison of two workflows for regulome and transcriptome-based prioritization of genetic variants associated with myocardial mass. Genet Epidemiol. 2019 Sep;43(6):717-726. [PubMed] [Genetic Epi]

A typical task arising from main effect analyses in a Genome Wide Association Study (GWAS) is to identify single nucleotide polymorphisms (SNPs), in linkage disequilibrium with the observed signals, that are likely causal variants and the affected genes. The affected genes may not be those closest to associating SNPs. Functional genomics data from relevant tissues are believed to be helpful in selecting likely causal SNPs and interpreting implicated biological mechanisms, ultimately facilitating prevention and treatment in the case of a disease trait. These data are typically used post GWAS analyses to fine‐map the statistically significant signals identified agnostically by testing all SNPs and applying a multiple testing correction. The number of tested SNPs is typically in the millions, so the multiple testing burden is high. Motivated by this, in this study we investigated an alternative workflow, which consists in utilizing the available functional genomics data as a first step to reduce the number of SNPs tested for association. We analyzed GWAS on electrocardiographic QRS duration using these two workflows. The alternative workflow identified more SNPs, including some residing in loci not discovered with the typical workflow. Moreover, the latter are corroborated by other reports on QRS duration. This indicates the potential value of incorporating functional genomics information at the onset in GWAS analyses.