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Proteomics

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...

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Misclassification Errors in Unsupervised Classification Methods. Comparison Based on the Simulation of Targeted

Victor P Andreev¹, Brenda W Gillespie², Brian T Helfand³

¹Arbor Research Collaborative for Health, 340 E. Huron St., Suite 300, Ann Arbor, MI 48104, USA.

Journal of Proteomics & Bioinformatics

|August 16, 2016

Summary

This summary is machine-generated.

Unsupervised classification aids omics studies by identifying disease subtypes. A new simulation approach estimates sample size for targeted proteomics, finding k-means effective for lower urinary tract dysfunction.

Keywords:

Biomarker signature Clustering Common complex disease Misclassification error Power analysis Sample size Targeted proteomics

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Area of Science:

Biomedical data science
Computational biology
Proteomics

Background:

Omics studies increasingly use unsupervised classification for complex diseases with potential subtypes.
Molecular signatures can reveal disease mechanisms and enable targeted interventions.
Established methods for sample size estimation in unsupervised classification are lacking.

Purpose of the Study:

To develop a simulation approach for comparing misclassification errors in unsupervised classification.
To estimate the required sample size for targeted proteomics studies.
To evaluate clustering methods for classifying disease subtypes based on molecular signatures.

Main Methods:

In silico simulations using data mimicking plasma proteomics from lower urinary tract dysfunction patients.
Comparison of hierarchical, k-means, and k-medoids clustering algorithms.
Assessment of classification accuracy based on differentially abundant proteins from a 1129-protein panel.

Main Results:

K-means clustering demonstrated superior performance compared to hierarchical and k-medoids methods.
Classification achieved a misclassification error below 5% with 100 patients.
The model utilized molecular signatures from 40 differentially abundant proteins with an effect size of 1.5.

Conclusions:

The developed simulation approach is valuable for sample size estimation in unsupervised classification for omics studies.
K-means clustering is a robust method for identifying disease subtypes in targeted proteomics data.
Accurate subtype classification is achievable with a moderate number of patients and proteins.