Microbiome data integration by robust similarity network fusion

Microbiome datasets are often comprised of different representations or views which provide complementary information, such as metabolic pathways, taxonomic assignments and gene families. Computational methods for integration of multi-view information combine these data to create a comprehensive view of a given microbiome study. Similarity network fusion (SNF) provides a candidate to solve this problem by efficiently fusing similarity networks built from each data view into one network that represents the full spectrum of the underlying data. Based on this method, we propose a Robust Similarity Network Fusion (RSNF) approach which combines the strength of random forest to construct robust affine graph and the advantage of SNF at data aggregation. The experimental results indicate that the proposed strategy not only substantially outperforms single data type analysis but improve the clustering performance significantly comparing to several state-of-the-art methods in various datasets. The application on human microbiome data suggests that we can cluster microbiome samples in high accuracy.