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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: May 24, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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Published on: April 8, 2016

AUTOMATED ANALYSIS OF QUANTITATIVE IMAGE DATA USING ISOMORPHIC FUNCTIONAL MIXED MODELS, WITH APPLICATION TO

Jeffrey S Morris1, Veerabhadran Baladandayuthapani, Richard C Herrick

  • 1The University of Texas M.D. Anderson Cancer Center.

The Annals of Applied Statistics
|March 13, 2012
PubMed
Summary

A new Bayesian functional mixed model framework analyzes quantitative image data, improving resolution for image-based proteomics. This approach reveals subtle protein expression differences missed by conventional methods.

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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

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Published on: April 8, 2016

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Published on: June 15, 2018

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Area of Science:

  • Quantitative image analysis
  • Statistical modeling
  • Bioinformatics

Background:

  • Quantitative image data are crucial in science, often requiring analysis of multiple images.
  • Existing methods struggle with complex images and correlating information across datasets.
  • Image-based proteomics presents unique challenges for data analysis.

Purpose of the Study:

  • To present a unified Bayesian functional mixed model framework for quantitative image data analysis.
  • To develop a flexible approach capable of handling complex images and multiple factors.
  • To apply this framework to image-based proteomic data for enhanced biological insights.

Main Methods:

  • Bayesian functional mixed model approach for quantitative image data.
  • General isomorphic modeling for fitting functional mixed models, including wavelet-based methods.
  • Application to image-based proteomic data from an animal study on opiate addiction.

Main Results:

  • The framework successfully analyzes complex, irregular images and models multiple factors.
  • It automatically generates inferential plots, considers statistical and practical significance, and controls false discovery rate.
  • The image-based approach identified significant protein expression regions missed by conventional spot-based analyses, potentially revealing post-translational modifications or co-migrating proteins.

Conclusions:

  • The proposed Bayesian functional mixed model offers a powerful and flexible framework for quantitative image data.
  • This image-based approach enhances the resolution of gel images, detecting differentially expressed proteins previously missed.
  • The method has broad applicability across scientific disciplines utilizing quantitative imaging.