Making external validation valid for molecular classifier development Journal Article


Authors: Wu, Y.; Huang, H. C.; Qin, L. X.
Article Title: Making external validation valid for molecular classifier development
Abstract: PURPOSE Accurate assessment of a molecular classifier that guides patient care is of paramount importance in precision oncology. Recent years have seen an increasing use of external validation for such assessment. However, little is known about how it is affected by ubiquitous unwanted variations in test data because of disparate experimental handling and by the use of data normalization for alleviating such variations. METHODS In this paper, we studied these issues using two microarray data sets for the same set of tumor samples and additional data simulated by resampling under various levels of signal-to-noise ratio and different designs for array-to-sample allocation. RESULTS We showed that (1) unwanted variations can lead to biased classifier assessment and (2) data normalization mitigates the bias to varying extents depending on the specific method used. In particular, frozen normalization methods for test data outperform their conventional forms in terms of both reducing the bias in accuracy estimation and increasing robustness to handling effects. We make available our benchmarking tool as an R package on GitHub for performing such evaluation on additional methods for normalization and classification. CONCLUSION Our findings thus highlight the importance of proper test-data normalization for valid assessment by external validation and call for caution on the choice of normalization method for molecular classifier development. © 2021 by American Society of Clinical Oncology.
Journal Title: JCO Precision Oncology
Volume: 5
ISSN: 2473-4284
Publisher: American Society of Clinical Oncology  
Date Published: 2021-08-05
Start Page: 1250
End Page: 1258
Language: English
DOI: 10.1200/po.21.00103
PROVIDER: scopus
PMCID: PMC8345919
PUBMED: 34377885
DOI/URL:
Notes: Article -- Export Date: 1 September 2021 -- Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. Li-Xuan Qin
    191 Qin
  2. Huei Chung Huang
    7 Huang