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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Published on: August 17, 2015

Penalized likelihood optimization for censored missing value imputation in proteomics.

Lucas Etourneau^1,2, Laura Fancello¹, Samuel Wieczorek¹

¹Univ. Grenoble Alpes, CNRS, CEA, INSERM, BGE UA13, ProFI FR2048, EDyP, Bâtiment 42b, CEA de Grenoble, 17 avenue des Martyrs, 38054 Grenoble Cedex 9, France.

Biostatistics (Oxford, England)

|March 22, 2025

Summary

This summary is machine-generated.

Pirat, a novel algorithm, addresses missing data in label-free proteomics by using a unique likelihood maximization strategy. This method outperforms existing imputation techniques, improving proteome characterization accuracy.

Keywords:

covariance matrix estimation imputation of missing not at random values mass spectrometry based proteomics multi-omic imputation penalized likelihood maximization

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Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Label-free bottom-up proteomics is a popular high-throughput workflow for proteome characterization.
This method generates data with complex missing values, posing challenges for accurate analysis.
Existing imputation methods struggle to effectively handle these missing data patterns.

Purpose of the Study:

To introduce Pirat, a novel algorithm for imputing missing values in label-free proteomics data.
To develop an imputation strategy that models instrument limitations and integrates multi-omic data.
To demonstrate Pirat's superior performance compared to existing imputation methods.

Main Methods:

Pirat employs a likelihood maximization strategy to handle missing values.
It models the instrument limit using a global censoring mechanism learned from available data.
The algorithm estimates covariance between enzymatic cleavage products and integrates transcriptomic data when available.

Main Results:

Benchmarking on diverse datasets shows Pirat outperforms all pre-existing imputation methods.
Pirat demonstrates effectiveness across various experimental designs and missingness patterns.
The study highlights significant improvements in imputation accuracy and differential analysis.

Conclusions:

Pirat offers a robust solution for missing data imputation in proteomics.
The findings suggest a paradigm shift is needed in proteomics imputation strategies.
Incorporating models for instrument censorship and correlation structures can enhance existing methods.