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Related Concept Videos

Proteomics01:33

Proteomics

7.2K
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...
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Updated: Jun 7, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation

Kyung Hyun Lee1, Shervin Assassi2, Chandra Mohan3

  • 1Institute for Clinical Research and Learning Health Care, Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA. Kyung.Hyun.Lee@uth.tmc.edu.

BMC Genomics
|November 15, 2024
PubMed
Summary

Comparing machine learning and dimensionality reduction methods for proteomics data analysis in small sample sizes reveals similar performance but heterogeneous biomarker selection. Larger sample sizes improve biomarker stability, crucial for disease prediction.

Keywords:
Machine learningPerformance metricsProteomics dataSmall sample sizes

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Area of Science:

  • Biomedical data science
  • Proteomics and clinical data integration
  • Biomarker discovery

Background:

  • Integrating proteomics and clinical data aids disease prediction and diagnosis.
  • High dimensionality and small sample sizes in clinical proteomics challenge machine learning.
  • Optimal analysis pipelines for reproducible and clinically meaningful proteomics results are unclear.

Purpose of the Study:

  • To compare the performance of 9 distinct analysis schemes using machine learning and dimensionality reduction.
  • To evaluate biomarker selection heterogeneity and biological pathway similarities across different analytical approaches.
  • To assess the impact of sample size on biomarker stability in proteomics data analysis.

Main Methods:

  • Analysis of simulated proteomics data (1317 proteins, 26 subjects) using 9 different schemes.
  • Inclusion of machine learning and dimensionality reduction techniques.
  • Sensitivity analysis with varying sample sizes to assess biomarker stability.

Main Results:

  • All schemes showed high performance metrics in small sample sizes (<30), suggesting potential overfitting.
  • Significant heterogeneity was observed in the specific proteins selected as discriminatory between groups.
  • Despite protein heterogeneity, selected biomarkers shared similar biological pathways and genetic disease associations; larger sample sizes enhanced biomarker stability.

Conclusions:

  • Widely used analysis pipelines perform similarly for distinguishing cohort groups and identifying common biological pathways in proteomics data.
  • Careful selection of statistical models is crucial when the goal is to pinpoint specific discriminatory proteins due to potential heterogeneity.
  • Biomarker stability and reliability are enhanced with increased sample sizes, which is vital for subsequent investigations.