ShinyDiversity - Understanding Alpha and Beta Diversity through Interactive Visualizations

Implementation

ShinyDiversity is an interactive HTML web application that utilizes the shiny (version 1.5.5.872) R package⁴. The application allows users to interactively visualize both alpha and beta diversity of multiple datasets. All diversity plots are generated using the phyloseq (version 1.16.2) R package which conveniently allows for phylogenetic analysis and visualization of microbial communities and provides 44 supported distance methods⁵. The underlying data used for calculations is an operational taxonomic unit (OTU) abundance table. An OTU abundance table is a matrix where the rows represent the various taxa and the columns are different samples. The table values are the counts of how often those taxa are observed.

Operation

System requirements are computers that can successfully install Bioconductor (Release 3.6) and R (≥ 3.4.0).

Data

Our application utilizes two built-in datasets from phyloseq (version 1.16.2): GlobalPatterns and esophagus. GlobalPatterns is a dataset composed of nine different sample types obtained from areas ranging from freshwater to the human gut⁶. The esophagus dataset is a small example dataset of three samples of a human esophageal community, with one sample from each of the three subjects⁷. In addition to these two datasets, we created a third dataset GP3, which is a subset of the GlobalPatterns dataset. The following R code generates this dataset.

library(phyloseq) 
data("GlobalPatterns")
GP3 <- subset_samples(GlobalPatterns,  SampleType  %in%  c("Skin",  "Tongue",  "Feces"))

GP3 only includes human feces, skin, and tongue samples and was created for easier visualization of multiple sample groups.

Alpha diversity

The alpha diversity page (Figure 2) currently gives users the option to visualize up to five different alpha diversity indices: Abundance Coverage-based Estimator (ACE), Shannon, Simpson, Inverse Simpson, and Fisher. The application dynamically produces side by side comparisons of the original data and any indices selected by the user. The side by side comparison allows the user to compare and contrast their selected indices.

Figure 2. The alpha diversity page.

Here is an example with the GlobalPatterns dataset and three plots for alpha diversity indices: observed taxa (i.e. number of different taxa), ACE, and Inverse Simpson. The dropdown box allows the user to choose different datasets.

It was also important for users to have a top level and individual sample view of their data in order to quickly identify interesting features (Figure 3). The alpha diversity page features a heat map displaying the frequency count of each sample in the dataset. A barplot right beside the heat map shows the intensity pattern for a single sample, which provides a quick way to identify and focus on interesting samples.

Figure 3. Top level and individual sample view of data.

The heat map gives a cursory view of the similarities and differences between samples. The bar graph shows an individual sample where the x-axis are the taxa and the y-axis are the taxa counts. The slider allows the user to move through the different samples to change the bar plot. These plots are shown at the bottom of the alpha diversity page.

Lastly, the alpha diversity page also shows the singleton and doubleton count for each sample (Figure 4). Some of the alpha diversity indices are sensitive to singletons and doubletons, which are OTUs that appear in the data only once or twice, respectively. These rare OTUs may suggest undersampling and hence a higher, true abundance in the population⁸. If a OTU is found more than two times then we can be more confident that it is not a false positive.

Figure 4. Singleton and doubleton summary table.

The table shows the different samples and the number of single and double taxa.

Beta diversity

Currently, the beta diversity page (Figure 5 and Figure 6) allows users to dynamically visualize and compare two groups of the most common beta diversity indices: 1) non-phylogenetic distance indices - Euclidean, Bray-Curtis, Jaccard and 2) phylogenetic distance indices - unweighted UniFrac and weighted UniFrac. All distance indices were visualized with principal coordinate analysis (PCoA) plots, which have two principal coordinates that explain the greatest distance between samples. The dataset used to visualize beta diversity is the GP3 normalized dataset. The dataset is normalized by rarefying, a resampling method⁹, to the sample with the smallest library size (N = 100,187). Users have the option to rarefy the samples to any library size below 100,187. This option allows users to visualize how rarefying affects beta diversity.

Figure 5. The beta diversity page with non-phylogenetic distance indices.

The Euclidean, Bray-Curtis, and Jaccard distances are plotted. The slider will rarefy the GP3 dataset to the specified library size.

Figure 6. The beta diversity page with phylogenetic distance indices.

The indices plotted are the unweighted UniFrac and weighted UniFrac.