Defining and accounting for subphenotypic structure has the potential to increase statistical power and provide a deeper understanding of the heterogeneity in the molecular basis of complex disease. Existing phenotype subtyping methods primarily rely on clinically observed heterogeneity or metadata clustering. However, they generally tend to capture the dominant sources of variation in the data, which often originate from variation that is not descriptive of the mechanistic heterogeneity of the phenotype of interest; in fact, such dominant sources of variation, such as population structure or technical variation, are, in general, expected to be independent of subphenotypic structure. We instead aim to find a subspace with signal that is unique to a group of samples for which we believe that subphenotypic variation exists (e.g., cases of a disease). To that end, we introduce Phenotype Aware Components Analysis (PACA), a contrastive learning approach leveraging canonical correlation analysis to robustly capture weak sources of subphenotypic variation. In the context of disease, PACA learns a gradient of variation unique to cases in a given dataset, while leveraging control samples for accounting for variation and imbalances of biological and technical confounders between cases and controls. We evaluated PACA using an extensive simulation study, as well as on various subtyping tasks using genotypes, transcriptomics, and DNA methylation data. Our results provide multiple strong evidence that PACA allows us to robustly capture weak unknown variation of interest while being calibrated and well-powered, far superseding the performance of alternative methods. This renders PACA as a state-of-the-art tool for defining de novo subtypes that are more likely to reflect molecular heterogeneity, especially in challenging cases where the phenotypic heterogeneity may be masked by a myriad of strong unrelated effects in the data.

Motivation Since the first human genome was sequenced in 2001, there has been a rapid growth in the number of bioinformatic methods to process and analyze next generation sequencing (NGS) data for research and clinical studies that aim to identify genetic variants influencing diseases and traits. To achieve this goal, one first needs to call genetic variants from NGS data which requires multiple computationally intensive analysis steps. Unfortunately, there is a lack of an open source pipeline that can perform all these steps on NGS data in a manner which is fully automated, efficient, rapid, scalable, modular, user-friendly and fault tolerant. To address this, we introduce xGAP, an extensible Genome Analysis Pipeline, which implements modified GATK best practice to analyze DNA-seq data with aforementioned functionalities. Results xGAP implements massive parallelization of the modified GATK best practice pipeline by splitting a genome into many smaller regions with efficient load-balancing to achieve high scalability. It can process 30x coverage whole-genome sequencing (WGS) data in approximately 90 minutes. In terms of accuracy of discovered variants, xGAP achieves average F1 scores of 99.37% for SNVs and 99.20% for Indels across seven benchmark WGS datasets. We achieve highly consistent results across multiple on-premises (SGE & SLURM) high performance clusters. Compared to the Churchill pipeline, with similar parallelization, xGAP is 20% faster when analyzing 50X coverage WGS in AWS. Finally, xGAP is user-friendly and fault tolerant where it can automatically re-initiate failed processes to minimize required user intervention. Availability xGAP is available at https://github.com/Adigorla/xgap. Supplementary information Supplementary data are available at Bioinformatics online.