SCNIC: Sparse correlation network investigation for compositional data. (1st September 2022)

• Record Type: Journal Article
• Authors: Shaffer, Michael; Thurimella, Kumar; Sterrett, John D.; Lozupone, Catherine A.
• Is Part Of: Molecular ecology resources. Volume 23:Number 1(2023)
• Page Start: 312
• Page End: 325
• Publication Date: 2022-09-01
• Subjects: bioinformatics/phyloinformatics -- microbial ecology -- network analysis -- species interactions; Molecular ecology -- Periodicals; 572.8
• DOI: 10.1111/1755-0998.13704
• Languages: English
• ISSNs: 1755-098X

Abstract

Abstract: 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, which is one dimensionality reduction method. Additionally, modules provide biological insight as correlated groups of microbes can have relationships among themselves. 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. Modules can be formed using either the Louvain Modularity Maximization (LMM) algorithm or a Shared Minimum Distance algorithm (SMD) that we newly describe here and relate to LMM using simulated data. We applied SCNIC to two published datasets and we achieved increased statistical power and identified microbes that not only differed across groups, but also correlated strongly with each other, suggesting shared environmental drivers or cooperative relationships among them. 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 and sparsity, it can be applied to a variety of data types including metabolomics data and used to integrate multiple data types. SCNIC allows for the identification of functional microbial relationships at scale while increasing statistical power through feature reduction.