Expanding the coverage of spatial proteomics: a machine learning approach

Abstract Motivation Multiplexed protein imaging methods use a chosen set of markers and provide valuable information about complex tissue structure and cellular heterogeneity. However, the number of markers that can be measured in the same tissue sample is inherently limited. Results In this paper,...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2024-02, Vol.40 (2)
Main Authors: Sun, Huangqingbo, Li, Jiayi, Murphy, Robert F
Format: Article
Language:English
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Cellular structure
Heterogeneity
Machine learning
Predictions
Protein composition
Protein structure
Proteins
Proteomics
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Summary:Abstract Motivation Multiplexed protein imaging methods use a chosen set of markers and provide valuable information about complex tissue structure and cellular heterogeneity. However, the number of markers that can be measured in the same tissue sample is inherently limited. Results In this paper, we present an efficient method to choose a minimal predictive subset of markers that for the first time allows the prediction of full images for a much larger set of markers. We demonstrate that our approach also outperforms previous methods for predicting cell-level protein composition. Most importantly, we demonstrate that our approach can be used to select a marker set that enables prediction of a much larger set than could be measured concurrently. Availability and implementation All code and intermediate results are available in a Reproducible Research Archive at https://github.com/murphygroup/CODEXPanelOptimization.
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/btae062