Integrative analysis of taxonomic and resistome abundance data

The microbiome harbors a collection of antimicrobial resistance genes (ARGs) known as the resistome. Metagenomics-based studies are used to characterize the resistome and the taxonomic composition of microbial communities. The resistome and taxonomic composition are intrinsically intertwined, and to understand the complex interplay and potential associations between them, a variety of in-silico integrative analysis approaches exists. These analyses have been increasingly used to explore the associations between the bacteria and antimicrobial resistance determinants across human, animal and environmental metagenomes.

Integrative analysis approaches on microbiome and resistome abundance data derived from the same experimental unit (or metagenomic samples) are grouped into three main categories in ResistoXplorer:

Global Similarity analysis: it includes multivariate correlation-based approaches such as Procrustes and Co-inertia analysis, which enables us to evaluate the overall similarity between the microbiome and resistome dataset. These approaches have been used in several metagenomics-based resistome studies to find out whether the resistome is structured or shaped by the microbiome (or phylogeny) or not.

Omics data integration: these methods highlight the correlations between high dimensional paired ‘omics’ datasets. ResistoXplorer offers two such multivariate projection-based approaches, i.e., regularized canonical correlation analysis (rCCA) and sparse partial least square (sPLS), to integrate microbiome and resistome data.

Pairwise microbe-ARG correlation analysis: it includes several univariate approaches to determine if there are strong relationships (co-occurrence patterns) between individual microbial taxa (microbiome) and ARGs (resistome). Such analysis can be performed using four different classical and more recent approaches, including Spearman, Pearson, CCLasso and Maximal Information Coefficient in ResistoXplorer. These approaches perform differently in different conditions, and there is no clear consensus on which approach is best. Hence, it is recommended to compare results from multiple methods. These approaches are the most time-consuming and computationally intensive analyses supported in ResistoXplorer. The results from such analysis are usually represented as a co-occurrence network.

You can always watch the video tutorial on how to perform all such integrative analyses in ResistoXplorer by clicking here.