CANTARE: finding and visualizing network-based multi-omic predictive models

Abstract

Background One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be “ome aware.” Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. Methods We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting “top table” of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). Results We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10–5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User’s Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/. Conclusion CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

Using empirical biological knowledge to infer regulatory networks from multi-omics data

“…Finally, these fitted models are aggregated in the final heterogeneous multi- omics network. CANTARE [ 23 ] focuses mainly on relationships between omics modalities. CANTARE fits pairwise regression models across all pairs of omics data resulting in the network.…”

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

“…In another study [ 54 ], the author proposes the CANTARE method, a novel method for creating interpretable multi-omic models shown in an IBD case study. With features from more genomes, the system competes favourably with RFs and elastic net penalized regression.…”

Undisclosed, unmet and neglected challenges in multi-omics studies

“…mixOmics 8 is a good example of a dimension reduction approach that creates an outcome prediction model, performs a Lasso variable selection on the predictors and returns a coregulation network of predictors. Similarly, the recently published CANTARE 54 strategy first fits linear models to build the interaction network and then uses logistic regression to predict the outcome from highly connected subnetworks and other non-omics variables.…”