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SCNIC: Sparse Correlation Network Investigation for Compositional Data

By Catherine A Lozupone, Michael Shaffer, Kumar Thurimella

Posted 16 Nov 2020
bioRxiv DOI: 10.1101/2020.11.13.380733

Background: Microbiome studies are often limited by a lack of statistical power due to small sample sizes and a large number of features. This problem is exacerbated in correlative studies of multi-omic datasets. Statistical power can be increased by finding and summarizing modules of correlated observations. Additionally, modules provide biological insight as groups of microbes can have relationships among themselves. Results: To address these challenges we developed SCNIC: Sparse Cooccurrence Network Investigation for Compositional data. SCNIC is open-source software that can generate correlation networks and detect and summarize modules of highly correlated features. We applied SCNIC to a published dataset comparing microbiome composition in men who have sex with men (MSM) who were at a high risk of contracting HIV to non-MSM. By applying SCNIC we achieved increased statistical power and identified microbes that not only differed with MSM-status, but also correlated strongly with each other, suggesting shared environmental drivers or cooperative relationships among them. Conclusions: SCNIC provides an easy way to generate correlation networks, identify modules of correlated features and summarize them for downstream statistical analysis. Although SCNIC was designed considering properties of microbiome data, such as compositionality, it can be applied to a variety of data types including metabolomics data and used to integrate multiple data types. Using SCNIC allows for the identification of functional microbial relationships at scale while increasing statistical power. ### Competing Interest Statement The authors have declared no competing interest.

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