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Waters MR, Inkman M, Jayachandran K, Kowalchuk RM, Robinson C, Schwarz JK, Swamidass SJ, Griffith OL, Szymanski JJ, Zhang J. GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis. PATTERNS (NEW YORK, N.Y.) 2024; 5:100910. [PMID: 38370125 PMCID: PMC10873154 DOI: 10.1016/j.patter.2023.100910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/23/2023] [Accepted: 12/07/2023] [Indexed: 02/20/2024]
Abstract
Big genomic data and artificial intelligence (AI) are ushering in an era of precision medicine, providing opportunities to study previously under-represented subtypes and rare diseases rather than categorize them as variances. However, clinical researchers face challenges in accessing such novel technologies as well as reliable methods to study small datasets or subcohorts with unique phenotypes. To address this need, we developed an integrative approach, GAiN, to capture patterns of gene expression from small datasets on the basis of an ensemble of generative adversarial networks (GANs) while leveraging big population data. Where conventional biostatistical methods fail, GAiN reliably discovers differentially expressed genes (DEGs) and enriched pathways between two cohorts with limited numbers of samples (n = 10) when benchmarked against a gold standard. GAiN is freely available at GitHub. Thus, GAiN may serve as a crucial tool for gene expression analysis in scenarios with limited samples, as in the context of rare diseases, under-represented populations, or limited investigator resources.
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Affiliation(s)
- Michael R. Waters
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Matthew Inkman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Kay Jayachandran
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | | | - Clifford Robinson
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Julie K. Schwarz
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - S. Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63105, USA
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63105, USA
| | - Obi L. Griffith
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jeffrey J. Szymanski
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jin Zhang
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Institute for Informatics (I), Washington University School of Medicine, St. Louis, MO 63110, USA
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