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.
No download data for this paper yet.
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!