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

# 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/)

# 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

> 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