High performance data integration for large-scale analyses of incomplete Omic profiles using Batch-Effect Reduction Trees (BERT)

Data from high-throughput technologies assessing global patterns of biomolecules ( omic data), is often afflicted with missing values and with measurement-specific biases (batch-effects), that hinder the quantitative comparison of independently acquired datasets. This work introduces batch-effect re...

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Bibliographic Details
Published in:Nature communications Vol. 16; no. 1; pp. 7104 - 13
Main Authors: Schumann, Yannis, Schlumbohm, Simon, Neumann, Julia E., Neumann, Philipp
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 02.08.2025
Nature Publishing Group
Nature Portfolio
Subjects:
631/114/1305
631/114/2401
631/553/117
Algorithms
Biomolecules
Computational Biology - methods
Data integration
Datasets
Distributed memory
Estimates
Experimental data
Humanities and Social Sciences
Humans
Integration
Measurement techniques
Metabolomics
Metabolomics - methods
multidisciplinary
Phenotypes
Proteomics
Proteomics - methods
Quality control
Science
Science (multidisciplinary)
Transcriptomics
Trees
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Summary:Data from high-throughput technologies assessing global patterns of biomolecules ( omic data), is often afflicted with missing values and with measurement-specific biases (batch-effects), that hinder the quantitative comparison of independently acquired datasets. This work introduces batch-effect reduction trees (BERT), a high-performance method for data integration of incomplete omic profiles. We characterize BERT on large-scale data integration tasks with up to 5000 datasets from simulated and experimental data of different quantification techniques and omic types (proteomics, transcriptomics, metabolomics) as well as other datatypes e.g., clinical data, emphasizing the broad scope of the algorithm. Compared to the only available method for integration of incomplete omic data, HarmonizR, our method (1) retains up to five orders of magnitude more numeric values, (2) leverages multi-core and distributed-memory systems for up to 11 × runtime improvement (3) considers covariates and reference measurements to account for severely imbalanced or sparsely distributed conditions (up to 2 × improvement of average-silhouette-width). This study presents BERT, an algorithm for high-performance integration of incomplete omics data with robustness to unequal phenotype distribution. It validates the method on simulated and experimental data from proteomics, metabolomics and transcriptomics.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-025-62237-4