PlotMicrobiome is a user-friendly statistical analysis and visualization pipeline for microbiome analysis. Plotmicrobiome integrates novel approaches in analyses and visualization of microbiome data in a user-interactive way and generates publication ready statistical results and figures for both 16S rRNA gene amplicon sequencing and shotgun metagenome sequencing.

Installation

There are four ways to use the pipeline: 1.GUI with R on your computer, 2. shiny apps website, 3. Docker container and 4. R package.

1. R shiny (recommended)

Run in Command line:

cd $HOME
git clone https://github.com/ssun6/plotmicrobiome.git

Start an R session, and make sure you have installed shiny and shinyjs packages. Run the following code in R:

library(shiny)
library(shinyjs)
runApp('~/plotmicrobiome')

If you want to look at the code:

runApp('~/plotmicrobiome', display.mode = "showcase")

2. Website

To explore Plotmicrobiome before installing it locally, visit https://ssun6.shinyapps.io/plotmicrobiome/. It is freely available, but the website is currently limited to 1GB memory and 25 active hours per month.

3. Docker container

Use a Docker container so you do not need to install all the packages

Install Docker https://www.docker.com/get-started/

Run in Command line:

docker login
docker run --rm -ti -e USER=plotmicrobiome -e PASSWORD=12345 -p 8787:8787 shansun1809/plotmicrobiome

Open a web browser (Chrome,Safari,...) and copy http://localhost:8787/ in the address.

username: plotmicrobiome password: 12345

It should open Rstudio in the browser.

Run the following commands:

library(shiny)
library(shinyjs)
runApp("../../plotmicrobiome")

4. R package

Install following packages separately:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("ggtree")

BiocManager::install("rhdf5")

Install plotmicrobiome:

install.packages("devtools")
devtools::install_github("ssun6/plotmicrobiome")

Load data

Getting ready

1. Count table files in .csv, .tsv or .biom formats. For .csv and .tsv files, samples should be in columns and features should be in rows.
2. Metadata table with matching sample IDs as the count table file, with samples in rows and metadata in columns.

Data input

Plotmicrobiome supports the common output files from sequencing analysis pipelines. The example files can be found in data-raw.

Amplicon with taxonomic structure:

1. DADA2 amplicon sequence variant (ASV) table with taxonomic classification in .csv, .tsv and .biom formats. The .csv and .tsv tables have the ASVs in rows, and the taxonomic classification of ASVs in the last column. If you used 'biom convert' to convert .biom file to .tsv file, please remove the '#' from the first line. Examples: 16S_biom_taxonomy.biom, table_taxonomy.txt.

2. A single file with all taxonomic levels and in .csv, .tsv and .biom formats. The taxonomic levels are in rows and should include from phylum to species level. Examples: taxa_all.txt, taxa_all.csv.

3. Multiple files with one for each taxonomic level separately. All the files should be stored in one directory and there shouldn't be other files in the folder. Examples: multiple_biom, multiple_tsv.

Metagenomics with taxonomic structure

The output of the most commonly used software MetaPhlAn and Kraken. The examples can be found in data-raw: wgs_metaphlan2.txt, wgs_kraken2.txt

Table without taxonomic structure

Data without taxonomic structure, for example, the pathway abundance table from HUMAnN. Functions such as the taxonomic tree cannot be applied to this type of data. Examples: humann2_pathway.txt.

App example:

Metadata input

Metadata should include sample IDs that are consistent with Data input files and variables for testing. Please specify the column of sample IDs. Shared samples between count table and metadata will be used for downstream analysis.

App example:

Analysis

MDS plot

Plotmicrobiome can generate both PCoA and non-parametric NMDS plots with selected dissimilarity measures. PERMANOVA test results of the association between microbiome and the selected variable are shown as headers.

App example:

Alpha-diversity plot

Plotmicrobiome provides test results and visualization of the alpha-diversity difference between groups at each taxonomic level. The metrics analyzed include Shannon index, Simpson index, Inverse Simpson index and numbers of taxa.

App example:

Taxa barplot

Visualize the average taxonomic composition of each group from phylum to genus level.

App example:

Data filter

The data can be stratified by metadata to only include samples that are used for statistical analysis. Abundance and Prevalence cutoffs can be used to exclude taxa of low abundance and/or low prevalence.

App example:

Statistical tests

Plotmicrobiome supports the following analyses.

• Categorical data: t-test, Wilcoxon test, ANOVA and kruskal-wallis.
  
• Continuous data: Pearson, Spearman and Kendall correlation.
  
• Both categorical and continuous data: generalized linear models (glm), logistic regression (lr), mixed effects linear models (lme).
  

Tips:

• The default family variable is binomial for categorical metadata and gaussian for continuous metadata.
  
• For logistic regression (lr), the testing metadata should be a two level variable (e.g., case and control). And use the following parameters: Is the metadata outcome? True. Is the metadata for testing continuous? False.
  
• Mixed effects linear models (lme) adjust for random effects. For example, when multiple samples were taken from one rat, the Rat ID can be included as random effect variable.
  
• ANOVA can be run on linear models. It is helpful when the testing metadata is a multiple level variable.
  

Statistical tests use the output from the data filter step. Please run data filter first before running statistical tests

App example:

Wilcoxon test:

Tree plots

Significant taxa are highlighted in a taxonomic tree. The taxonomic tree is pruned to remove some less prevalent and less abundant branches for visualization. These parameters can be adjusted in the tool to include less or more branches.

App example:

Boxplots

Boxplots are used to visualize the differetial abundance of taxa identified in statistical tests. If there are not figures shown, please try adjusting the FDR cutoff.

App example:

Correlation plots

The associations between taxa and continuous variables can be tested and visualized in this step. If there are not figures shown, please try adjusting the FDR cutoff.

App example:

P values vs P values plots

• P values vs P values plots can be used to compare the testing results from two groups.
  
• The log10(P1) from dataset1 and log10(P2) from dataset2 are plotted against each other, and the correlations are calculated.
  
• If there is a direction of changes, the log10(P) will be multiplied by +1/-1 to include the direction. ANOVA and Kruskal-wallis do not have direction of changes.
  
• The P value files can be downloaded from the Statistical tests step or calculated at this step.
  

App example: