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Updated: Dec 4, 2025

Quantitative Analysis of Chromatin Proteomes in Disease

Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data.

Meng Wang¹, Lihua Jiang¹, Ruiqi Jian¹

¹Department of Genetics, Stanford University, Stanford, CA 94305, USA.

Bioinformatics (Oxford, England)

|October 24, 2020

Summary

A new data normalization method, RobNorm, effectively reduces systematic bias in proteomics data from diverse samples. This approach maintains biological variation, improving analysis of heterogeneous datasets.

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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

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Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Data normalization is crucial for mass spectrometry-based proteomics to reduce sample variation.
Existing methods often assume similar protein expression distributions, failing with heterogeneous samples like various tissue types.

Purpose of the Study:

To develop a novel, data-driven normalization method for proteomics data.
To correct systematic bias while preserving biological heterogeneity in complex samples.

Main Methods:

Developed RobNorm, a normalization algorithm using density-power-weighting and robust fitting.
Incorporated a robustness criterion for improved performance on structured data.

Main Results:

RobNorm demonstrated superior reduction of systematic bias compared to other methods.
The algorithm effectively maintained biological variation across different tissues in heterogeneous datasets.
Evaluated through simulations and analysis of Genotype-Tissue Expression (GTEx) data.

Conclusions:

RobNorm offers improved normalization for heterogeneous proteomics data.
The method enhances the reliability of cross-sample comparisons in complex biological studies.