A new ensemble method for detecting anomalies in gene expression matrices.

Laura Selicato, Flavia Esposito, Grazia Gargano, Maria Carmela Vegliante, Giuseppina Opinto, Gian Maria Zaccaria, Sabino Ciavarella, Attilio Guarini, Nicoletta Del Buono

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number882
Issue number8
Publication statusPublished - Apr 2 2021


  • Anomaly
  • Clustering
  • Gene expression
  • Low rank decomposition
  • Outliers

ASJC Scopus subject areas

  • Mathematics(all)


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