Representing high throughput expression profiles via perturbation barcodes reveals compound targets
Open accessAbstract
High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound's high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.
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Advanced transcriptional similarity metrics derived through AI/ML approaches can help ameliorate the cell-line confounding bias.
“Advanced transcriptional similarity metrics derived through AI/ML approaches can help ameliorate this bias.210”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [210]
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- Authors
- Filzen, T. M., Kutchukian, P. S., Hermes, J. D., Li, J., & Tudor, M.
- Affiliations
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Corresponding author
- Tudor, Matthew
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-02-09
- Volume
- 13
- Issue
- 2
- Pages
- e1005335
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5300121
- OpenAlex
- W2341986069
- Cited by
- 31
- References
- 61
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 7 · 2019: 4 · 2018: 4 · 2017: 1
- FWCI
- 3.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- high-throughput gene expression profiling, L1000, deep metric learning, guilt by association, chemical genetics, MAPK signaling, batch effects
- MeSH terms
- Animals, Cell Physiological Phenomena, Drug Evaluation, Preclinical, Pharmaceutical Preparations, Humans, Gene Expression, Gene Expression Profiling, Molecular Targeted Therapy, High-Throughput Nucleotide Sequencing
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