https://github.com/erictleung/shinydiversity
Interactive application to explore various ecological diversity metrics
https://github.com/erictleung/shinydiversity
amplicon-sequencing diversity ecology interactive-visualizations metagenomics microbiome r shiny
Last synced: about 1 year ago
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Interactive application to explore various ecological diversity metrics
- Host: GitHub
- URL: https://github.com/erictleung/shinydiversity
- Owner: erictleung
- License: gpl-3.0
- Created: 2017-08-18T21:51:09.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2018-05-02T17:48:31.000Z (about 8 years ago)
- Last Synced: 2025-03-19T08:39:51.670Z (about 1 year ago)
- Topics: amplicon-sequencing, diversity, ecology, interactive-visualizations, metagenomics, microbiome, r, shiny
- Language: R
- Homepage: https://erictleung.shinyapps.io/shinydiversity
- Size: 287 KB
- Stars: 5
- Watchers: 5
- Forks: 8
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# shinydiversity
[](https://travis-ci.org/erictleung/shinydiversity)
[](https://gitter.im/erictleung/shinydiversity?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://doi.org/10.5281/zenodo.1188304)
Interactive application to explore various ecological diversity metrics
**Table of Contents**
- [To Run](#to-run)
- [Prerequisites for Local Development](#prerequisites-for-local-development)
- [Problem](#problem)
- [Proposed Project](#proposed-project)
- [More Comprehensive Tools](#more-comprehensive-tools)
- [Acknowledgements](#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](http://joey711.github.io/shiny-phyloseq/)
- McMurdie and Holmes (2014). [Shiny-phyloseq: Web Application for
Interactive Microbiome Analysis with Provenance Tracking][shinyphyloseq].
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)](https://acnc-shinyapps.shinyapps.io/DAME/)
- Piccolo, Brian D., et al. [Dynamic Assessment of Microbial Ecology
(DAME): A web app for interactive analysis and visualization of microbial
sequencing data][dame]. 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](http://www.microbiomeanalyst.ca/)
- Dhariwal, Achal, et al. [MicrobiomeAnalyst: a web-based tool for
comprehensive statistical, visual and meta-analysis of microbiome
data][microbiomeanalyst]. 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.
[shinyphyloseq]: https://doi.org/10.1093/bioinformatics/btu616
[dame]: https://doi.org/10.1093/bioinformatics/btx686
[microbiomeanalyst]: https://doi.org/10.1093/nar/gkx295
## Acknowledgements
This project was initiated as a selected project at genomics hackathon
[hackseq](https://www.hackseq.com/) in 2017.
Key developers in coding and brainstorming during the hackathon are:
- [Eric Leung](https://github.com/erictleung)
- [Roshan Noronha](https://github.com/roshannoronha)
- [Ali Mirza](https://github.com/aimirza)
- [Akiff Manji](https://github.com/amanji)
- Reva Shenwai
- [Stelios Batziakas](https://github.com/ampatzia)