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Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform
Published on: January 13, 2016
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Representing high throughput expression profiles via perturbation barcodes reveals compound targets.
Tracey M Filzen1, Peter S Kutchukian2, Jeffrey D Hermes3
1Medical Writing, Merck Research Laboratories, Upper Gwynedd, Pennsylvania, United States of America.
Plos Computational Biology
|February 10, 2017
Summary
This study introduces a deep learning method to create a "perturbation barcode" from gene expression data. This barcode better reveals biological insights and compound characteristics from noisy, large-scale chemical genetics experiments.
Area of Science:
- Computational Biology
- Genomics
- Pharmacology
Background:
- High-throughput mRNA expression profiling generates vast datasets for studying cellular responses to perturbations.
- Analyzing this data requires methods to handle noise (batch effects, stochastic variation) and extract meaningful biological signals.
- The L1000 platform profiles thousands of genes across numerous compound treatments.
Purpose of the Study:
- To develop a deep learning approach for processing large-scale gene expression data from chemical genetics experiments.
- To create a 'perturbation barcode' that summarizes and enhances the biological insights derived from landmark gene expression.
- To demonstrate the utility of this barcode for compound characterization and functional prediction.
Main Methods:
- Utilized deep learning techniques, specifically deep metric learning, to transform landmark gene expression data into a perturbation barcode.
- Applied the method to large-scale L1000 profiling data from thousands of compound treatments.
- Developed visualization techniques based on the perturbation barcode for compound function assignment.
Main Results:
- The perturbation barcode effectively captured compound structure and target information, outperforming raw expression data.
- The barcode predicted compound high-throughput screening promiscuity more accurately than original measurements.
- Visualizations from the barcode enabled sensitive function assignment to unknown compounds via guilt-by-association.
- Predicted and experimentally validated compound activity on the MAPK pathway.
Conclusions:
- Deep learning-derived perturbation barcodes offer a powerful method for extracting biological insights from noisy, large-scale chemical genetics data.
- This approach enhances the characterization of compounds and facilitates the discovery of their biological functions.
- The methodology holds significant promise for hypothesis generation and testing in big data-driven biological research.
