SCNIC: Sparse Correlation Network Investigation for Compositional Data

Abstract

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, which is one dimensionality reduction method. Additionally, modules provide biological insight as correlated 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. 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. 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 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.

Stability of a dominant sponge‐symbiont in spite of antibiotic‐induced microbiome disturbance

“…Bacterial co‐occurrence networks were calculated based on relative abundances with the SparCC methodology (Friedman & Alm, 2012) in SCNIC (version 2020.10) (Shaffer et al, 2022) within QIIME2 (version 2020.8). Significant interactions were defined as −0.5 > R > 0.5.…”

Ear mite infection restructures otic microbial networks in conservation–reliant Santa Catalina Island foxes (Urocyon littoralis catalinae)

“…We first created separate feature tables for mite‐infected and uninfected samples and ran downstream analyses in parallel for each phenotype group. We used the QIIME2 plug‐in Sparse Co‐occurrence Network Investigation for Compositional data (SCNIC) to construct two networks: one for the infected samples and another for the uninfected samples (Shaffer et al, 2020).…”

“…SCNIC begins the network construction process by generating a matrix of pairwise correlations between each pair of microbial taxa in a given phenotype group (Shaffer et al, 2020). We used the sparCC metric to generate this matrix, as it has been recommended for use with compositional data sets (Friedman & Alm, 2012).…”

Clinical NEC prevention practices drive different microbiome profiles and functional responses in the preterm intestine

“…The network was created by using SparCC 85,86 within the SCNIC tool (Sparse Cooccurrence Network Investigation for Compositional data) 87,88 to calculate co-occurences from CSS normalized metagenomic observations. Apart from default settings we used 10 bootstraps to calculate p-values for the SparCC R value, filtered the dataset with activated -sparcc_filter parameter and used the recommended minimum correlation value of 0.35 to determine edges.…”