VOLARE: visual analysis of disease-associated microbiome-immune system interplay

Abstract

Background: Relationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. "Omic" methodologies such as 16S ribosomal RNA (rRNA) sequencing and time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features provide one approach for quantifying relationships between "omes", and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts such as microbiologists and immunologists need to vet candidate results for follow-up experiments. Results: To support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies-microbiome:cytokine data from fecal samples in human immunodeficiency virus (HIV), microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that users frequently generated detailed regression plots, suggesting that this detail aids the vetting of results. Conclusions: Systematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

Multiomic Predictors of Short‐Term Weight Loss and Clinical Outcomes During a Behavioral‐Based Weight Loss Intervention

“…The fitted regression lines show negative associations, representing larger reductions in TG in individuals who had higher baseline levels of DNAme of cg18366782 ( r = −0.46, P = 0.0004) or TMA ( r = −0.32, P = 0.0199). Visualizing these pairwise relationships in the VOLARE framework (12) allows us to vet the underlying data for magnitude and dynamic range, assess the relationship for goodness of fit, and borrow information from a feature in one ome to better understand a feature in another ome. For example, cg18366782 is not annotated to any particular gene yet is well correlated with change in TG levels.…”

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

“…1 b), and summarizes the resulting multi-omic “top table” in a network (Fig. 1 c), as previously described [ 18 ]. Given this network, CANTARE identifies network neighborhoods of interest and builds predictive models from these neighborhoods, using standard regression techniques (Fig.…”

“…These details provide a way to vet the models and to appreciate the inherent variability of human participants that may otherwise be difficult to detect based only on ranked lists of statistics. In previous work, we developed VOLARE (Visual analysis Of LineAr Regression Elements) to demonstrate the importance of visualizing pairwise relationships across analytes from different omes [ 18 ]. In that work, we summarized a “top table” of pairwise relationships in a VOLARE network (hereafter, Vnet), and supported interactive visualization of the underlying regression models.…”

Antiretroviral treatment is less effective at reducing gut microbiome-associated inflammation and T cell activation in people living with HIV in rural versus urban Zimbabwe

“…To identify relationships that were evident in ART naïve PLWH and different from ART experienced PLWH or HCs, resulting models were ltered to include only those with an FDR-adjusted p-value on the F statistic of the overall regression < 0.2, adjusted R 2 > 0.25, p-value on the slope for the ART naïve cohort < 0.05 and different from the slope for the ART experienced cohort and/or the HCs (p < 0.05 for at least one of these slopes), and maximum absolute value of DFFITS < 2 (to exclude results that were outlier driven). The resulting network and models were visualized using the VOLARE [99] web application. A high-resolution version of the network was generated with the igraph [100] library in R. To compare microbe:immune relationships observed in ART experienced PLWH to HCs, data was ltered to exclude the ART naïve cohort.…”

“…Features were expressed as relative abundance. For each timepoint, linear regressions were performed for each pair of immune markers and microbial features as previously described [99]. The form of the model was:…”