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<p>Data from high-throughput technologies assessing global patterns of biomolecules (<i>omic</i> 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 <i>omic</i> 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 <i>omic</i> 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 <i>omic</i> data, HarmonizR, our method (1)&#xa0;retains up to five orders of magnitude more numeric values, (2) leverages multi-core and distributed-memory systems for up to 11&#xa0;× runtime improvement (3) considers covariates and reference measurements to account for severely imbalanced or sparsely distributed conditions (up to 2&#xa0;× improvement of average-silhouette-width).</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月2日

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  • Yannis Schumann,
  • Simon Schlumbohm,
  • Julia E. Neumann,
  • Philipp Neumann

摘要

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).