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Publicação
ROSIE
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 2.35 MB | Adobe PDF |
Orientador(es)
Resumo(s)
The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy. Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods. We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of (Formula presented.) genes and more than (Formula presented.) samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks.
Descrição
We thank Peter Segaert for providing his adapted code of the enetLTS method. The results presented here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga .
Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 390740016 (AJ and NR).
Publisher Copyright:
© The Author(s) 2022.
Palavras-chave
biomarker classification Ensemble feature selection outlier robust sparse triple-Negative Breast Cancer Epidemiology Statistics and Probability Health Information Management SDG 3 - Good Health and Well-being