In xia-lab/MicrobiomeAnalystR: MicrobiomeAnalystR - A comprehensive R package for statistical, visual, and functional analysis of the microbiome.

Note - MicrobiomeAnalystR 2.0 is still under development - we cannot guarantee full functionality ###

Description

MicrobiomeAnalystR-2.0 is an update to the version 1 from 2020 and contains the R functions and libraries underlying the popular MicrobiomeAnalyst web server, including > 200 functions for statistical, functional, and visual analysis of microbiome data. The new version adds three new modules: 16S raw data processing, Microbiome-metabolomics integrative analysis and meta-analysis. The package is synchronized with the MicrobiomeAnalyst web server. After installing and loading the package, users will be able to reproduce the same results from their local computers using the corresponding R command history downloaded from MicrobiomeAnalyst, thereby achieving maximum flexibility and reproducibility.

Getting Started

Step 1. Install package dependencies

If you are using RStudio, ensure that it has been updated to the latest version for smoother installation of MicrobiomeAnalystR and overall better compatibility with all other R packages.

To use MicrobiomeAnalystR , first install all package dependencies. Ensure that you are able to download packages from Bioconductor - the Bioconductor package ("BiocManager") and RTools should be pre-installed. To install package dependencies, one can use the pacman R package (for those with >R 3.5.1). Note that some of these packages may require additional library dependencies that need to be installed prior to their own successful installation. For users who wish to perform raw sequence data processing, dada2 will also need to be installed.

MicrobiomeAnalystR will not be successfully installed until all package dependencies and their associated dependencies are also installed. An example message signaling the R package installation failure is "non-zero exit status". The most common reason is that not all R package dependencies were successfully installed. If you are unable to run the pacman function, you will have to install each R package dependency one by one using install.packages("x", dependencies = TRUE) if the package is from CRAN or BiocManager::install("x") if the package is from Bioconductor. Note to know where the package is deposited, simply google the R package - i.e. "phyloseq R" will return the Bioconductor page where you can follow the installation instructions for that R package.

Note about Tax4Fun: the older version is no longer supported on CRAN but can still be installed from nick-youngblut/Tax4Fun by ensuring the following dependencies are also installed - rhdf5; qiimer; joey711/biom.

install.packages("pacman")

library(pacman)

pacman::p_load(phyloseq, metacoder, pryr, biomformat, RColorBrewer, ggplot2, gplots, Cairo, igraph, BiocParallel, randomForest, metagenomeSeq, MASS, DESeq2, vegan, RJSONIO, ggfortify, pheatmap, xtable, genefilter, data.table, reshape, stringr, ape, grid, gridExtra, splitstackshape, edgeR, globaltest, R.utils, viridis, ggrepel, ppcor, qs, dplyr, limma, memoise, tidyverse)

Step 2. Install the package

MicrobiomeAnalystR is freely available from GitHub. Note that the R package is currently under construction. The package documentation, including the vignettes for each module and user manual is available within the downloaded R package file. If all package dependencies were installed, you will be able to install the MicrobiomeAnalystR. There are three options, A) using the R package devtools, B) cloning the github, C) manually downloading the .tar.gz file. Note that the MicrobiomeAnalystR github will have the most up-to-date version of the package.

Option A) Install the package directly from github using the devtools package. Open R and enter:

Due to issues with Latex, some users may find that they are only able to install MicrobiomeAnalystR without any documentation (i.e. vignettes).

# Step 1: Install devtools
install.packages("devtools")
library(devtools)

# Step 2: Install MicrobiomeAnalystR WITHOUT documentation
devtools::install_github("xia-lab/MicrobiomeAnalystR", build = TRUE, build_opts = c("--no-resave-data", "--no-manual", "--no-build-vignettes"))

# Step 2: Install MicrobiomeAnalystR WITH documentation
devtools::install_github("xia-lab/MicrobiomeAnalystR", build = TRUE, build_opts = c("--no-resave-data", "--no-manual"))

Option B) Clone Github and install locally

The * must be replaced by what is actually downloaded and built. For instance, check your Downloads folder to see what tar.gz file was downloaded. So if you download MicrobiomeAnalystR_1.0.1.tar.gz, replace the * with the downloaded version number.

git clone https://github.com/xia-lab/MicrobiomeAnalystR.git
R CMD build MicrobiomeAnalystR
R CMD INSTALL MicrobiomeAnalystR_*.tar.gz

Case Studies

MicrobiomeAnalyst Workflow

To showcase how to utilize MicrobiomeAnalystR, we provide a detailed tutorial to perform a comprehensive end-to-end workflow from raw sequence data preprocessing to knowledge-based analysis. The dataset showcased in the tutorial consists of a subset of pediatric IBD stool samples obtained from the Integrative Human Microbiome Project Consortium (https://ibdmdb.org/). The tutorial is available inside the R package as a vignette.

For detailed tutorials on how to use MicrobiomeAnalystR, please refer to the R package vignettes. These vignettes include a comprehensive tutorial introducing MicrobiomeAnalystR, four detailed step-by-step tutorials with example data for each of the main MetaboAnalytR modules, and a case-study showcasing the end-to-end functionality of MicrobiomeAnalystR. Note, the functions below work only if the R package vignettes were built.

Tutorials for new features of version 2.0 are currently under development.

Within R:

vignette(package="MicrobiomeAnalystR")

Within a web-browser:

browseVignettes("MicrobiomeAnalystR")

Tutorials of new features - In progress

Meta-analysis

1. To start every analysis, you need to initiate the R objects and environment with Init.MbSetObj() , For meta-analysis please run this function Init.DataMeta(mbSet, F) as well:

#set mode to local = FALSE
library(MicrobiomeAnalystR)
mbSet <- Init.MbSetObj();
mbSet <- Init.DataMeta(mbSet, F);
mbSet <- SetModuleType(mbSet, "meta")

1. Read your datasets. For the purpose of this tutorial, we are going to read three example datasets studying the effect of colorectal cancer on stool. microbiota with a primary metadata group consisting of Control vs CRC (Colorectal cancer samples). For a successful meta-analysis, the data selection step is critical to limit confounding factors and minimize study specific biases. Most importantly, the datasets need to be generated by same or similar sequencing technologies, metadata groups

2.1 Read data table of your datasets ```{eval=FALSE} mbSet<-Read16SAbundDataMeta(mbSet, "data1.csv","text","GreengenesID","F","false"); mbSet<-Read16SAbundDataMeta(mbSet, "data2.csv","text","GreengenesID","F","false"); mbSet<-Read16SAbundDataMeta(mbSet, "data3.csv","text","GreengenesID","F","false");

2.2 Read metadata table of your datasets
```{eval=FALSE}
#the first argument is used to indicate the dataset to be updated
mbSet<-ReadSampleTableMetaInd(mbSet, "data1.csv", "data1_meta.csv");
mbSet<-ReadSampleTableMetaInd(mbSet, "data2.csv", "data2_meta.csv");
mbSet<-ReadSampleTableMetaInd(mbSet, "data3.csv", "data3_meta.csv");

2.3 Read taxa table of your datasets ```{eval=FALSE} mbSet<-Read16STaxaTableMeta(mbSet, "data1.csv", "data1_tax.csv"); mbSet<-Read16STaxaTableMeta(mbSet, "data2.csv", "data2_tax.csv"); mbSet<-Read16STaxaTableMeta(mbSet, "data3.csv", "data3_tax.csv");

3. Sanity check your data table, metadata table, individually. Followed by the creation of phyloseq Object.
```{eval=FALSE}
mbSet<-SanityCheckDataMeta(mbSet, "data1.csv", "text");
mbSet<-SanityCheckSampleDataMeta(mbSet, "data1.csv");
mbSet<-CreatePhyloseqObjMeta(mbSet,  "data1.csv", "text","GreengenesID","F","F")
mbSet<-SanityCheckDataMeta(mbSet, "data2.csv", "text");
mbSet<-SanityCheckSampleDataMeta(mbSet, "data2.csv");
mbSet<-CreatePhyloseqObjMeta(mbSet,  "data2.csv", "text","GreengenesID","F","F")
mbSet<-SanityCheckDataMeta(mbSet, "data3.csv", "text");
mbSet<-SanityCheckSampleDataMeta(mbSet, "data3.csv");
mbSet<-CreatePhyloseqObjMeta(mbSet,  "data3.csv", "text","GreengenesID","F","F")

1. Verify whether the datasets can be integrated together. Check that there is a subset of OTUs that are shared, primary metadata group is shared. ```{eval=FALSE} mbSet<-CheckMetaDataIntegrity(mbSet, "OTU");

5. Plot diagnostic plots to have an overview of the uploaded datasets. This step is useful to determine the uploaded datasets are on similar scale and overall sample separation.
```{eval=FALSE}
PlotMetaPCA("qc_meta_pca_0","72", "png", "");
PlotMetaDensity("qc_meta_density_0","72", "png", "");
mbSet<-PlotLibSizeView(mbSet, "norm_libsizes_0","png");

1. Process the datasets individually - Filtering + Normalization ```{eval=FALSE} mbSet<-ApplyAbundanceFilterMeta(mbSet, "data1.csv", "prevalence", 0, 0.1); mbSet<-ApplyVarianceFilterMeta(mbSet, "data1.csv", "iqr", 0.0); mbSet<-PerformNormalizationMeta(mbSet, "data1.csv", "none", "colsum", "none");

mbSet<-ApplyAbundanceFilterMeta(mbSet, "data2.csv", "prevalence", 0, 0.1); mbSet<-ApplyVarianceFilterMeta(mbSet, "data2.csv", "iqr", 0.0); mbSet<-PerformNormalizationMeta(mbSet, "data2.csv", "none", "colsum", "none");

mbSet<-ApplyAbundanceFilterMeta(mbSet, "data3.csv", "prevalence", 0, 0.1); mbSet<-ApplyVarianceFilterMeta(mbSet, "data3.csv", "iqr", 0.0); mbSet<-PerformNormalizationMeta(mbSet, "data3.csv", "none", "colsum", "none");

merge the datasets into a merged phyloseq object

mbSet<-MergeDatasets(mbSet, "OTU", "study_condition")

7. Perform batch correction to minimize study specific bias. MicrobiomeAnalyst is using batch correction method from MMUPHin packaged that has been adapted for zero-inflated microbiome datasets.
```{eval=FALSE}
PerformBatchCorrection();
mbSet<-MergeDatasets(mbSet, "OTU", "study_condition");

1. Perform biomarker meta-analysis using method from MMUPHin.The method first performs differential abundance analysis for each individual dataset followed by combining effect-size of the results. This will identify list of features that are consistently changed across datasets,

```{eval=FALSE} mbSet<-PerformMetaEffectSize(mbSet, "metafeature_bar_0", "OTU","study_condition", 0.05,"LM","REML","png",72)

detailed result table is saved into a csv file called: meta_sig_genes_effectsize.csv

bar plot of top features is saved in an image called metafeature_bar_0.png

```

xia-lab/MicrobiomeAnalystR documentation built on April 17, 2024, 7:45 p.m.