Constructing Multi-Omic Networks Related to Schizophrenia for the dACC Dataset

Music City Center 

Multi-omics datasets allow researchers to uncover relationships across different omics layers (e.g. genome, proteome, metabolome). Analyzing multiple layers requires specialized methods to handle heterogeneity and other inherent challenges. A well-known approach is canonical correlation analysis (CCA) with more recent extensions in sparsity and incorporating phenotype. One of such extensions is SmCCNet, which is built on sparse CCA and repeated feature subsampling to construct multi-omic networks specific to a phenotype. We analyze a multi-omic schizophrenia (SCZ) dataset of 112 samples with protein, phosphorylation, lipid, and metabolomic features, plus binary schizophrenia or neurotypical (NT) diagnosis. We use a sparse CCA approach inspired by SmCCNet, incorporating the feature subsampling and similarity matrix construction. However, our method has three key differences: 1) we do not explicitly use the phenotype in the network construction to prevent signal blurring, 2) in order to increase our number of samples, we remove the case effect from the case samples, and 3) we use bagging to improve robustness and generalizability. Lastly, we identify networks that are significantly associated with schizophrenia using an enrichment analysis and covariance matrix permutation test. Of the resulting significant networks, many are also biologically meaningful and contain features consistent with existing literature.

Multi-omics

Canonical correlation analysis (CCA)

Sparse canonical correlation analysis (sCCA)

Multi-omic networks

Case-control covariance differences

Feature subsampling 

Section on Statistics in Genomics and Genetics