Enabling Precision Medicine with Big Data: A Cross-Platform Framework to Characterize Gene Presence and Function

Overview

Objectives/Goals The current design of gene expression studies makes data sets susceptible to bias and hinders cross-platform comparison. To address these issues, an unprecedented global-scale meta-analysis of gene expression distributions was conducted. Methods/Materials All microarray data (1,350,000 samples from 14,000 platforms) were downloaded from Gene Expression Omnibus onto a high-performance computing cluster and normalized. My project has two phases: (1) development of a robust gene detection call algorithm and (2) extrapolating gene function from statistical features. Detection calls (indicating gene presence) are necessary to gain a concrete understanding of a gene's behavior. However, existing software has limited platform support. In Phase 1, I proposed and developed a detection call algorithm that is extensible across all platforms and species. Unsupervised machine learning with Gaussian Mixture Models (GMMs) was leveraged to dynamically determine gene-specific thresholds for on-expression. In Phase 2, essential and immune genes were predicted based on GMM characteristics. Gene functions were verified using Gene Ontology enrichment analysis (enrichment score > 1.0), pathway over-representation analysis (p = 0.01), and existing databases such as Database of Essential Genes and NIH ImmPort. Results Of the 70686 probes (from 15 tumor samples) marked Present in published calls, the proposed detection call algorithm successfully identified 68449, achieving a remarkable 97% accuracy. In Phase 2, GMM feature extraction proved to be a successful predictive model for essential and immune genes. This workflow identified 83 potential essential genes and 6449 immunology-related genes across 5 platforms. Conclusions/Discussion My work represents the first comprehensive framework for characterizing gene presence and function from several gene expression platforms. Detection calls can now be used to filter RNAi assays, assign clinical phenotypes to unknown samples, and define patient subgroups for personalized treatment. Furthermore, essential and immune gene prediction enables systematic drug target and biomarker identification. Ultimately, this project revolutionizes the framework for analyzing gene expression big data, and has implications in both research and clinical medicine.

Awards (1)

• Third Place

Competition history

• CSEF 2015 Biochemistry/ Molecular Biology · Entry S0522

Resources

• CSEF project summary ↗

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