/Tools-Microbiome-Analysis

A list of R environment based tools for microbiome data exploration, statistical analysis and visualization

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A list of R environment based tools for microbiome data exploration, statistical analysis and visualization

As a beginner, the entire process from sample collection to analysis for sequencing data is a daunting task. More specifically, the downstream processing of raw reads is the most time consuming and mentally draining stage. It is vital to understand the basic concepts in microbial ecology and then to use various tools at disposal to address specific research questions. Thankfully, several young researchers supported by their experienced principal investigators/supervisors are working on creating various tools for analysis and interpretation of microbial community data. A major achievement of the scientific community is the open science initiative which has led to sharing of knowledge worldwide. For microbial community analysis, several tools have been created in R, a free to use (GNU General Public License) programming language(Team, 2000). The power of R lies in its ease of working with individuals lacking programming skills and easy sharing of analysis scripts codes and packages aiding reproducibility. Using tools such as QIIME (the newer QIIME2) (Caporaso, Kuczynski, Stombaugh et al., 2010), Mothur (Schloss, Westcott, Ryabin et al., 2009), DADA2 (Callahan, McMurdie, Rosen et al., 2016) one can get from raw reads to species × samples table (OTU or ASVs amplicon sequence variants as suggested recently (Callahan, McMurdie & Holmes, 2017)). In this post, numerous resources that can be helpful for analysis of microbiome data are listed. This list may not have all the packages as this tool development space is ever growing. Feel free to add those packages or links to web tutorials related to microbiome data, there is a google docs excel sheet at this link for a list of tools which can be edited to include more tools. These are mostly for improving statistical analysis and visualisation. These tools provide convenient options for data analysis and include several steps where the user has to make decisions. The work by McMurdie PJ, Holmes S, Weiss S and Tsilimigras M.C. and Fodor A.A are useful resources to understand the data common to microbiome census. It can be tricky and frustrating in the beginning but patience and perseverance will be fruitful at the end (personal experience).

Tools:

A

Adaptive gPCA A method for structured dimensionality reduction
Ampvis2 Tools for visualising amplicon sequencing data
ANCOM R scripts for Analysis of Composition of Microbiomes (ANCOM)
animalcules R shiny app for interactive microbiome analysis

B

BDMMA Batch Effects Correction for Microbiome Data With Dirichlet-multinomial Regression
BEEM BEEM: Estimating Lotka-Volterra models from time-course microbiome sequencing data
biome-shiny GUI for microbiome visualization, based on the shiny package "microbiome"
bootLong The Block Bootstrap Method for Longitudinal Microbiome Data
breakaway Species Richness Estimation and Modeling

C

CCREPE Compositionality Corrected by PErmutation and REnormalization
corncob Modeling microbial abundances and dysbiosis with beta-binomial regression
curatedMetagenomicData Accessible, curated metagenomic data through ExperimentHub

D

dacomp Testing for Differential Abundance in Compositional Counts Data, with Application to Microbiome Studies
DADA2 Divisive Amplicon Denoising Algorithm
DECIPHER Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R
DECIPHER/IIDTAXA IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences
decontam Simple Statistical Identification and Removal of Contaminant Sequences in Marker-Gene and Metagenomics Data
DESeq2 Differential expression analysis for sequence count data
DMBC A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis With Microbial Compositions

E

edgeR empirical analysis of DGE in R

G

GLMMMiRKAT A distance-based kernel association test based on the generalized linear mixed model

I

igraph Network Analysis and Visualization in R

L

labdsv Ordination and Multivariate Analysis for Ecology

M

MaAsLin2 MaAsLin2: Microbiome Multivariate Association with Linear Models
mare Microbiota Analysis in R Easily
massMap A Two-Stage Microbial Association Mapping Framework With Advanced FDR Control
mbtools Collection of R tools to analyze microbiome data
MDiNE MDiNE: a model to estimate differential co-occurrence networks in microbiome studies
MDPbiome MDPbiome: microbiome engineering through prescriptive perturbations
microDecon An R package for removing contamination from metabarcoding (e.g., microbiome) datasets post-sequencing
microbiotaPair An R Package for Simplified Microbiome Analysis Workflows on Intervention Study
MedTest A Distance-Based Approach for Testing the Mediation Effect of the Human Microbiome
MegaR An Interactive R Package for Metagenomic Sample Classification and Disease Prediction using Microbiota and Machine Learning
MelonnPan Model-based Genomically Informed High-dimensional Predictor of Microbial Community Metabolic Profiles
Metacoder An R package for visualization and manipulation of community taxonomic diversity data
metagenomeSeq Differential abundance analysis for microbial marker-gene surveys
MetaLonDA METAgenomic LONgitudinal Differential Abundance method
metamicrobiomeR Analysis of Microbiome Relative Abundance Data using Zero Inflated Beta GAMLSS and Meta-Analysis Across Studies using Random Effects Model
microbiome R package Tools for microbiome analysis in R
MicrobiomeDDA An Omnibus Test for Differential Distribution Analysis of Microbiome Sequencing Data
MicrobiomeHD A standardized database of human gut microbiome studies in health and disease Case-Control
microbiomeMarker R package for microbiome biomarker discovery
MicrobiomeR MicrobiomeR: An R Package for Simplified and Standardized Microbiome Analysis Workflows
microbiomeutilities Extending and supporting package based on microbiome and phyloseq R package
MicrobiotaProcess MicrobiotaProcess: an R package for analysis, visualization and biomarker discovery of microbiome
miLineage A General Framework for Association Analysis of Microbial Communities on a Taxonomic Tree
MINT Multivariate INTegrative method
mixDIABLO Data Integration Analysis for Biomarker discovery using Latent variable approaches for ‘Omics studies
mixMC Multivariate Statistical Framework to Gain Insight into Microbial Communities
MMinte Methodology for the large-scale assessment of microbial metabolic interactions (MMinte) from 16S rDNA data
MTA Microbial trend analysis (MTA) for common dynamic trend, group comparison and classification in longitudinal microbiome study

N

NMIT Microbial Interdependence Association Test--a Non-parametric Microbial Interdependence Test

O

OMiSA Optimal Microbiome-based Survival Analysis (OMiSA)

P

pathostat Statistical Microbiome Analysis on metagenomics results from sequencing data samples
phylofactor Phylogenetic factorization of compositional data
phylogeo Geographic analysis and visualization of microbiome data
Phyloseq Import, share, and analyze microbiome census data using R
Pldist Pldist: Ecological Dissimilarities for Paired and Longitudinal Microbiome Association Analysis
powmic Power assessment in microbiome case-control studies

Q

qgraph Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation
qiime2R Importing QIIME2 artifacts and associated data into R sessions
qiimer R tools compliment qiime

R

RAM R for Amplicon-Sequencing-Based Microbial-Ecology
RCM A unified framework for unconstrained and constrained ordination of microbiome read count data
RevEcoR Reverse Ecology analysis in R
Rhea A pipeline with modular R scripts

S

ShinyPhyloseq Web-tool with user interface for Phyloseq
SIAMCAT Statistical Inference of Associations between Microbial Communities And host phenoTypes
SigTree Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree
SparseMCMM Estimating and testing the microbial causal mediation effect with the high-dimensional and compositional microbiome data (SparseMCMM)
SPIEC-EASI Sparse and Compositionally Robust Inference of Microbial Ecological Networks
SplinectomeR SplinectomeR Enables Group Comparisons in Longitudinal Microbiome Studies
StructFDR False Discovery Rate Control Incorporating Phylogenetic Tree Increases Detection Power in Microbiome-Wide Multiple Testing
structSSI Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data

T

Tax4Fun Predicting functional profiles from metagenomic 16S rRNA gene data
taxize Taxonomic Information from Around the Web
TreeSummarizedExperiment An extension class of SummarizedExperiment to allow hierarchical structure on the row or column dimension
themetagenomics Exploring Thematic Structure and Predicted Functionality of 16S rRNA Amplicon Data

V

vegan R package for community ecologists

Y

yingtools2 Tools and functions for working with clinical and microbiome data

Z

zeroSum Reference Point Insensitive Molecular Data Analysis
ZIBBSeqDiscovery A Zero-inflated Beta-binomial Model for Microbiome Data Analysis

Other tools

  1. ggplot2 An implementation of the Grammar of Graphics in R
    • Widely used package for data visualization
  2. ggvegan ggplot-based versions of the plots produced by the vegan package
    • Convert base plots of vegan to ggplot.
  3. ggord A simple package for creating ordination plots with ggplot2
    • Alternative to ggvegan
  4. cowplot cowplot: Streamlined Plot Theme and Plot Annotations for ggplot2
    • Widely used package for combining multiple plots
  5. ggridges Ridgeline plots in ggplot2
  6. ggtext Improved text rendering support for ggplot2
    • More power in controlling annotations in plots (e.g. italicize taxa names in plots)
  7. ggpubr Extension of ggplot2 based data visualization
    • Publication ready plots
  8. ggraph Grammar of Graph Graphics
    • Network graphs using ggplot2
  9. gganimate A Grammar of Animated Graphics
    • Animate ggplot2 (Useful for presenting time-series dynamics of microbial communities)
  10. ggforce Accelerating ggplot2
    • Zoom specific regions of the plots
  11. factoextra Extract and Visualize the Results of Multivariate Data Analyses
    • Powerful package for multivvariate data analysis
  12. ggcorrplot Visualization of a correlation matrix using ggplot2
  13. tidyverse R packages for data science
    • Universe of several useful R packages for data handling, analysis and visualization
  14. Extensions of ggplot Gallary of numerous data visualistion R pacakges
  15. ggtree Visualization and annotation of phylogenetic trees (in R)
  16. patchwork The Composer of ggplots
    • Combining multiple plots made easy
  17. pheatmap Pretty Heatmaps

Proteomics resources

  1. *RforProteomics Using R for proteomics data analysis
  2. *RforProteomics Visualisation of proteomics data using R and Bioconductor
  3. *proteomics proteomics: Mass spectrometry and proteomics data analysis
  4. Introduction to analysing microbial proteomics data in R

RNAseq resources*

  1. RNA-seq analysis in R Workflow by Shulin Cao
  2. RNA-seq workflow RNA-seq workflow: gene-level exploratory analysis and differential expression

*Note: These are not focused towards microbiome data. These are listed as a reference point for beginners. If you have or know of workflows tools specific for microbiome data please let us know and we can add them here!

Useful resources are provided by:

  1. Ben J. Callahan and Colleagues: Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses.
  2. Comeau AM and Colleagues: Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research
  3. Schloss, P. D: The Riffomonas Reproducible Research Tutorial Series
  4. Shetty SA, Lahti L., et al: Tutorial from microbiome data analysis spring school 2018, Wageningen University and Research
  5. Holmes S, Huber W.: Modern statistics for modern biology. Cambridge University Press; 2018 Nov 30.
  6. Xu S, Yu G.: Workshop of microbiome dataset analysis using MicrobiotaProcess

Note:
A good practise is to use Rmarkdown for documenting your results and sharing with your collaborators and supervisors. For more information click here RStudio and
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References:

  1. Callahan, B. J., McMurdie, P. J. & Holmes, S. P. (2017). Exact sequence variants should replace operational taxonomic units in marker gene data analysis. bioRxiv, 113597.
  2. Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A. & Holmes, S. P. (2016). DADA2: high-resolution sample inference from Illumina amplicon data. Nature methods 13, 581-583.
  3. Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K. & Gordon, J. I. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature methods 7, 335-336.
  4. Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H. & Robinson, C. J. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and environmental microbiology 75, 7537-7541.
  5. Team, R. C. (2000). R language definition. Vienna, Austria: R foundation for statistical computing.
  6. Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO, Moffatt MF, Turner P, Parkhill J, Loman NJ, Walker AW. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC biology. 2014 Dec 1;12(1):87.
  7. Karstens L, Asquith M, Davin S, Fair D, Gregory WT, Wolfe AJ, Braun J, McWeeney S. Controlling for contaminants in low-biomass 16S rRNA gene sequencing experiments. MSystems. 2019 Aug 27;4(4).

TODO

Any help is welcome

  • Structure the list according to categories
    • General purpose
    • Visualization
    • Snapshot/cross-sectional stats
    • Time series/Longitudinal stats
    • Integrative -Omics
  • Include metagenomics/metabolomics
  • Include more general microbiology oriented R packages/tools
  • and so on .....

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You can cite this resource as:
Shetty, Sudarshan A., and Leo Lahti. Microbiome data science. Journal of biosciences 44, no. 5 (2019): 115.
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