A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices

Mathematics

Nicoletta Del Buono¹  Laura Selicato¹  Grazia Gargano¹  Flavia Esposito¹  Giuseppina Opinto²  Attilio Guarini²  GianMaria Zaccaria²  MariaCarmela Vegliante²  Sabino Ciavarella² 

[1] Department of Mathematics, University of Bari Aldo Moro, 70125 Bari, Italy;Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori ‘Giovanni Paolo II’, 70124 Bari, Italy;

关键词: anomaly;    low rank decomposition;    gene expression;    clustering;    outliers;   

DOI  :  10.3390/math9080882

来源: DOAJ

【 摘 要 】

One of the main problems in the analysis of real data is often related to the presence of anomalies. Namely, anomalous cases can both spoil the resulting analysis and contain valuable information at the same time. In both cases, the ability to detect these occurrences is very important. In the biomedical field, a correct identification of outliers could allow the development of new biological hypotheses that are not considered when looking at experimental biological data. In this work, we address the problem of detecting outliers in gene expression data, focusing on microarray analysis. We propose an ensemble approach for detecting anomalies in gene expression matrices based on the use of Hierarchical Clustering and Robust Principal Component Analysis, which allows us to derive a novel pseudo-mathematical classification of anomalies.

【 授权许可】

Unknown