Stabilized COre Gene and Pathway Election Uncovers Pan-Cancer Shared Pathways and a Cancer-Specific Driver

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (i) coexpression network analyses focusing on correlations between genes and (ii) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: A slight change of parameterization or dataset could lead to marked alterations of outcomes. Here, we propose Stabilized COre gene and Pathway Election (SCOPE), a tool integrating bootstrapped least absolute shrinkage and selection operator and coexpression analysis, leading to robust outcomes insensitive to variations in data. By applying SCOPE to six cancer expression datasets (BRCA, COAD, KIRC, LUAD, PRAD, and THCA) in The Cancer Genome Atlas, we identified core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response. Moreover, we highlighted the pivotal role of CD63 as an oncogenic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations toward complex interactions using transcriptome data

Novel stabilized models to characterize gene-gene interactions by utilizing transcriptome data

Machine learning models employed in genetics often grapple with issues related to the "curse of dimensionality". Furthermore, due to the inherent noisy nature of most -omics data, most methods suffer from the problem of "stability": i.e., even slight perturbations of the original data may result in wholly different outcomes. This becomes particularly true when dealing with interactions as the number of potential interactions are usually astronomical. In this thesis, we present two novel methods: 1) Stabilized COre gene and Pathway Election (SCOPE) and 2) Interaction Bridged Association Study (IBAS) that uses two differing approaches in analyzing biological interactions. SCOPE employs a stabilized form of the LASSO that is better able to handle highly correlated expression data and a co-expression network analysis that identifies "core" genes that may be of interest as well as the underlying biological pathways or mechanisms by which they interact. Stabilizing these results across six cancers of The Cancer Genome Atlas uncovered hallmark cancer pathways as well as a novel potential therapeutic target of kidney cancer, CD63. IBAS utilizes a "data-bridge" composed of dimensionality reduced pathway level interactions of the transcriptome to identify genes associated with a phenotype of interest using the Sequence Kernel Association Test (SKAT), in a disentangled form of the Transcriptome Wide Association Study. Application to the Wellcome Trust Case Control Consortium reveals novel gene candidates with literature reviews highlighting their potential for further study. In conclusion, we have developed two novel methodologies in analyzing complex interaction patterns in -omics data using stabilized machine learning methods, paving the way to further understand the biological interactions underlying complex disease

XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.

Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the "latent variables" in an autoencoder, are difficult to interpret, not to mention prioritizing essential genes for functional follow-up. In contrast, in traditional analyses, one may identify important genes such as Differentially Expressed (DiffEx), Differentially Co-Expressed (DiffCoEx), and Hub genes. Intuitively, the complex gene-gene interactions may be beyond the capture of marginal effects (DiffEx) or correlations (DiffCoEx and Hub), indicating the need of powerful RL models. However, the lack of interpretability and individual target genes is an obstacle for RL's broad use in practice. To facilitate interpretable analysis and gene-identification using RL, we propose "Critical genes", defined as genes that contribute highly to learned representations (e.g., latent variables in an autoencoder). As a proof-of-concept, supported by eXplainable Artificial Intelligence (XAI), we implemented eXplainable Autoencoder for Critical genes (XA4C) that quantifies each gene's contribution to latent variables, based on which Critical genes are prioritized. Applying XA4C to gene expression data in six cancers showed that Critical genes capture essential pathways underlying cancers. Remarkably, Critical genes has little overlap with Hub or DiffEx genes, however, has a higher enrichment in a comprehensive disease gene database (DisGeNET) and a cancer-specific database (COSMIC), evidencing its potential to disclose massive unknown biology. As an example, we discovered five Critical genes sitting in the center of Lysine degradation (hsa00310) pathway, displaying distinct interaction patterns in tumor and normal tissues. In conclusion, XA4C facilitates explainable analysis using RL and Critical genes discovered by explainable RL empowers the study of complex interactions

cLD: Rare-variant linkage disequilibrium between genomic regions identifies novel genomic interactions.

Linkage disequilibrium (LD) is a fundamental concept in genetics; critical for studying genetic associations and molecular evolution. However, LD measurements are only reliable for common genetic variants, leaving low-frequency variants unanalyzed. In this work, we introduce cumulative LD (cLD), a stable statistic that captures the rare-variant LD between genetic regions, which reflects more biological interactions between variants, in addition to lack of recombination. We derived the theoretical variance of cLD using delta methods to demonstrate its higher stability than LD for rare variants. This property is also verified by bootstrapped simulations using real data. In application, we find cLD reveals an increased genetic association between genes in 3D chromatin interactions, a phenomenon recently reported negatively by calculating standard LD between common variants. Additionally, we show that cLD is higher between gene pairs reported in interaction databases, identifies unreported protein-protein interactions, and reveals interacting genes distinguishing case/control samples in association studies