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DIMA: Data-Driven Selection of an Imputation Algorithm.

Janine Egert^1,2, Eva Brombacher^1,2,3,4, Bettina Warscheid^5,6

¹Institute of Medical Biometry and Statistics (IMBI), Institute of Medicine and Medical Center Freiburg, 79104 Freiburg im Breisgau, Germany.

Journal of Proteome Research

|June 1, 2021

Summary

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This summary is machine-generated.

Data-driven selection of imputation algorithm (DIMA) improves missing value imputation in proteomics. DIMA reliably identifies high-performing methods, enhancing quantitative proteomics data analysis.

Area of Science:

Proteomics
Bioinformatics
Data Science

Background:

Missing values (MVs) are common in quantitative proteomics data analysis.
Assessing the performance of different MV imputation methods is challenging and data-dependent.

Purpose of the Study:

To introduce a data-driven selection of imputation algorithm (DIMA) for robust MV handling in proteomics.
To evaluate DIMA's performance and applicability across diverse quantitative proteomics datasets.

Main Methods:

DIMA was developed and tested on 142 quantitative proteomics datasets from the PRoteomics IDEntifications (PRIDE) database.
Simulated datasets with varying percentages (5-50%) of MVs, including missing not at random (MNAR) and missing completely at random (MCAR) values, were used for evaluation.
Performance was assessed by comparing DIMA-suggested algorithms against other methods using root mean square error difference (ΔRMSE).

Keywords:

accuracy imputation mass spectrometry missing values proteomics

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Main Results:

DIMA consistently suggests a high-performing imputation algorithm for quantitative proteomics data.
The recommended algorithm by DIMA is always among the top three performing methods.
DIMA achieves a ΔRMSE ≤ 10% in 80% of the evaluated cases, demonstrating reliable performance.
Broad applicability was confirmed across a large number of diverse datasets.

Conclusions:

DIMA offers a reliable and data-driven approach for selecting optimal imputation algorithms in quantitative proteomics.
This method enhances the accuracy and robustness of proteomics data analysis pipelines by improving missing value handling.
DIMA implementations are available in MATLAB and R for wider adoption in the research community.