shinydiversity

Interactive application to explore various ecological diversity metrics

Table of Contents

• To Run
• Prerequisites for Local Development
• Problem
• Proposed Project
• More Comprehensive Tools
• Acknowledgements

To Run

```R

Install Shiny

install.packages("shiny")

Run application

library(shiny) runGitHub("shinydiversity", "erictleung") ```

Prerequisites for Local Development

Development Environments

• R (https://www.r-project.org/)
• RStudio (https://www.rstudio.com/)

R Packages

• shiny (http://shiny.rstudio.com/)
• phyloseq (http://joey711.github.io/phyloseq/)
• ggplot2 (ggplot2.tidyverse.org)
• knitr (https://yihui.name/knitr/)
• markdown (https://cran.r-project.org/package=markdown)
• rmarkdown (https://rmarkdown.rstudio.com/)
• plyr (http://had.co.nz/plyr/)

```R

Install prerequisite R packages

pkgs <- c("shiny", "ggplot2", "knitr", "markdown" , "rmarkdown", "plyr") install.packages(pkgs)

Try http:// if https:// URLs are not supported

source("https://bioconductor.org/biocLite.R") biocLite('phyloseq') ```

Problem

There are many alpha and beta diversity metrics to analyze microbial ecological or microbiome data. Although there are other more comprehensive tools to analyze microbial data, each of them assumes sufficient amount of knowledge on the differences among the diversity indices and how underlying assumptions of the indices may interpret your data in unexpected ways. Alpha diversity describes an estimate of the total number of species in a sample. Beta diversity describes the differences between samples. Below are some example of the number of metrics you can use.

Plot from "Alpha diversity graphics" page for phyloseq showing various alpha diversity metrics to choose from http://joey711.github.io/phyloseq/plot_richness-examples

Below are just a few beta diversity metrics to choose from

```R

library(phyloseq) unlist(distanceMethodList) UniFrac1 UniFrac2 DPCoA JSD vegdist1 vegdist2 "unifrac" "wunifrac" "dpcoa" "jsd" "manhattan" "euclidean" vegdist3 vegdist4 vegdist5 vegdist6 vegdist7 vegdist8 "canberra" "bray" "kulczynski" "jaccard" "gower" "altGower" vegdist9 vegdist10 vegdist11 vegdist12 vegdist13 vegdist14 "morisita" "horn" "mountford" "raup" "binomial" "chao" vegdist15 betadiver1 betadiver2 betadiver3 betadiver4 betadiver5 "cao" "w" "-1" "c" "wb" "r" betadiver6 betadiver7 betadiver8 betadiver9 betadiver10 betadiver11 "I" "e" "t" "me" "j" "sor" betadiver12 betadiver13 betadiver14 betadiver15 betadiver16 betadiver17 "m" "-2" "co" "cc" "g" "-3" betadiver18 betadiver19 betadiver20 betadiver21 betadiver22 betadiver23 "l" "19" "hk" "rlb" "sim" "gl" betadiver24 dist1 dist2 dist3 designdist "z" "maximum" "binary" "minkowski" "ANY" length(unlist(distanceMethodList)) [1] 47 ```

With so many metrics to choose from, how do you know which is the "best" and how will your data affect the calculation of these metrics?

Proposed Project

Create an interactive Shiny application to show changes in your chosen alpha or beta diversity metrics to see how each changes based on simulated or real data. Some of these metrics are sensitive to single or double counts of species so this will be good to see how different distributions of counts will change these metrics and your interpretations of them. The project should be designed to give an intuitive understanding of how these metrics work.

More Comprehensive Tools

For more comprehensive microbiome data analysis that goes beyond the scope of just diversity indices and includes provenance of the analysis, we suggest looking at these other tools:

• Shiny-phyloseq
  • McMurdie and Holmes (2014). Shiny-phyloseq: Web Application for Interactive Microbiome Analysis with Provenance Tracking. Bioinformatics (Oxford, England) 31(2), 282–283.
  • Comprehensive microbiome analysis and interactive web application using phyloseq and Shiny with provenance tracking in order to reproduce graphically performed analyses.
• Dynamic Assessment of Microbial Ecology (DAME)
  • Piccolo, Brian D., et al. Dynamic Assessment of Microbial Ecology (DAME): A web app for interactive analysis and visualization of microbial sequencing data. Bioinformatics 1 (2017): 3.
  • Comprehensive interactive web application to analyze microbial ecology data specifically designed to work directly with output files from the QIIME1 software suite with as minimal file processing as possible.
• MicrobiomeAnalyst
  • Dhariwal, Achal, et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic acids research (2017): gkx295.
  • Comprehensive statistical, visual and meta-analysis of microbiome data of various kinds: 16S rRNA, shotgun metagenomics, taxon set enrichment analysis.

Acknowledgements

This project was initiated as a selected project at genomics hackathon hackseq in 2017.

Key developers in coding and brainstorming during the hackathon are:

• Eric Leung
• Roshan Noronha
• Ali Mirza
• Akiff Manji
• Reva Shenwai
• Stelios Batziakas