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Diversity profiling

Diversity profiling

Metagenomic resistome data are multi-dimensional, often represented by hundreds or thousands of different ARGs, and therefore deducing meaningful results is not only dependent on the abundance of single ARGs, but also on the overall scope of the resistome, such as the diversity of associated ARGs. Visual exploration of resistome composition is commonly followed-up by diversity profiling or analysis. In general, the variation in ARG distribution within and between samples is typically assessed by calculating alpha and beta diversity indices respectively, similar to practices employed for determining differences in microbial community profiling. Now let’s go through each of them individually and learn how can we do such analysis for our own resistome data in ResistoXplorer.

Alpha diversity is a measure of mean feature diversity within-sample. To quantify it, several indices have been developed, most of which account for richness (total number of ARGs/features) and/or evenness (abundance distribution across ARGs/features) within a sample. Currently, eight alpha diversity measures are supported in ResistoXplorer, each assessing different aspects of the community. For example, the ‘Observed’ index calculates the total number of features (ARGs) present per sample, while ‘ACE’ and ‘Chao1’ indexes estimate feature richness by also accounting for unobserved features that are undetected because of low abundance. ‘Shannon’ and ‘Simpson’ indexes take both feature richness and evenness into account, with varying importance given to evenness. In ResistoXplorer, we can perform alpha diversity analysis at different functional levels to see whether the same pattern can be observed at higher functional levels as observed at ARG-level. Additionally, we can also summarise the alpha diversity measures across groups and calculate its statistical significance using either parametric or non-parametric tests. One more important thing to note here is that the alpha diversity analysis is performed on the original uploaded dataset and not on processed (filtered or normalised) data in ResistoXplorer.

Now let’s go the next step where you will watch a video on how to do alpha diversity analysis in ResistoXplorer.

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