دورية أكاديمية
How missing value imputation is confounded with batch effects and what you can do about it.
| العنوان: | How missing value imputation is confounded with batch effects and what you can do about it. |
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| المؤلفون: | Goh WWB; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore; Center for Biomedical Informatics, Nanyang Technological University, Singapore. Electronic address: wilsongoh@ntu.edu.sg., Hui HWH; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore., Wong L; Department of Computer Science, National University of Singapore, Singapore; Department of Pathology, National University of Singapore, Singapore. Electronic address: wongls@comp.nus.edu.sg. |
| المصدر: | Drug discovery today [Drug Discov Today] 2023 Sep; Vol. 28 (9), pp. 103661. Date of Electronic Publication: 2023 Jun 09. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Science Ltd. Country of Publication: England NLM ID: 9604391 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-5832 (Electronic) Linking ISSN: 13596446 NLM ISO Abbreviation: Drug Discov Today Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Kidlington, Oxford : Irvington, NJ : Elsevier Science Ltd. ; Distributed by Virgin Mailing and Distribution, c1996- |
| مواضيع طبية MeSH: | Electronic Data Processing* |
| مستخلص: | In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning. (Copyright © 2023 Elsevier Ltd. All rights reserved.) |
| فهرسة مساهمة: | Keywords: batch effects; class-batch proportion imbalance; computational biology; confounding; data science; missing value imputation; statistics |
| تواريخ الأحداث: | Date Created: 20230610 Date Completed: 20230912 Latest Revision: 20230912 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1016/j.drudis.2023.103661 |
| PMID: | 37301250 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1878-5832 |
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| DOI: | 10.1016/j.drudis.2023.103661 |