Interactive application to explore various ecological diversity metrics
Table of Contents
# Install Shiny
install.packages("shiny")
# Run application
library(shiny)
runGitHub("shinydiversity", "erictleung")Development Environments
- R (https://www.r-project.org/)
- RStudio (https://www.rstudio.com/)
R Packages
shinyR package (http://shiny.rstudio.com/)phyloseqR package (http://joey711.github.io/phyloseq/)
There are many alpha and beta diversity metrics to analyze microbial ecological or microbiome data. 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 then 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 is are just a few beta diversity metrics 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] 47With 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?
Create an interactive Shiny application to show changes in your chosen alpha or beta diversity metrics to see how each change 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. This should be designed to give an intuitive understanding of how these metrics work.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent