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An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data.

Hongwei Sun1,2, Jiu Wang1, Zhongwen Zhang1

  • 1Department of Health Statistics, School of Public Health and Management, Binzhou Medical University, Yantai City, Shandong 264003, China.

Computational and Mathematical Methods in Medicine
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Summary

A new AR-Cstep algorithm improves biomarker detection in high-dimensional omics data by addressing slow convergence issues with the traditional C-step method. This enhanced approach offers faster computation and more accurate variable selection and outlier identification.

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Computational Biology

Background:

  • High dimensionality and noise in omics data complicate biomarker discovery.
  • Penalized maximum trimmed likelihood estimation effectively identifies mislabeled samples in high-dimensional datasets.
  • The conventional concentration step (C-step) algorithm for robust penalized regression exhibits slow convergence and optimization issues due to changing regularized parameters during iteration, particularly with high-dimensional omics data.

Purpose of the Study:

  • To introduce and evaluate the novel AR-Cstep algorithm as an improvement over the traditional C-step method.
  • To enhance the speed and accuracy of biomarker detection and outlier identification in high-dimensional omics data.
  • To address the convergence and optimization limitations of the C-step algorithm in robust penalized regression.

Main Methods:

  • Development of the AR-Cstep (C-step combined with an acceptance-rejection scheme) algorithm.
  • Comparative analysis of AR-Cstep and C-step algorithms using simulation experiments.
  • Application and comparison of both algorithms on triple-negative breast cancer (TNBC) RNA-seq data.

Main Results:

  • The AR-Cstep algorithm demonstrated significantly faster convergence, with average computation time reduced to 2% of the C-step algorithm.
  • AR-Cstep exhibited superior accuracy in variable selection and outlier identification compared to the C-step algorithm in simulations.
  • AR-Cstep successfully addressed the convergence problems of the C-step algorithm and ensured iterative improvement of the criterion function, as validated on TNBC RNA-seq data.

Conclusions:

  • The AR-Cstep algorithm offers a substantial improvement in computational efficiency and accuracy for robust penalized regression in high-dimensional omics data analysis.
  • AR-Cstep effectively overcomes the limitations of the C-step algorithm, ensuring reliable iterative optimization.
  • The AR-Cstep algorithm's framework is extensible to other robust models incorporating regularized parameters.