期刊
BRIEFINGS IN BIOINFORMATICS
卷 20, 期 4, 页码 1269-1279出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbx167
关键词
molecular-level interaction; biological systems; unsupervised classification; data preprocessing
资金
- PAT (Provincia Autonoma di Trento) [S503/2015/664730]
With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.
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