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).

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Tools:

1. Ampvis2 Tools for visualising amplicon sequencing data
2. CCREPE Compositionality Corrected by PErmutation and REnormalization
3. DADA2 Divisive Amplicon Denoising Algorithm
4. DESeq2 Differential expression analysis for sequence count data
5. edgeR empirical analysis of DGE in R
6. mare Microbiota Analysis in R Easily
7. Metacoder An R package for visualization and manipulation of community taxonomic diversity data
8. metagenomeSeq Differential abundance analysis for microbial marker-gene surveys
9. microbiome R package Tools for microbiome analysis in R
10. MINT Multivariate INTegrative method
11. mixDIABLO Data Integration Analysis for Biomarker discovery using Latent variable approaches for ‘Omics studies
12. mixMC Multivariate Statistical Framework to Gain Insight into Microbial Communities
13. MMinte Methodology for the large-scale assessment of microbial metabolic interactions (MMinte) from 16S rDNA data
14. pathostat Statistical Microbiome Analysis on metagenomics results from sequencing data samples
15. phylofactor Phylogenetic factorization of compositional data
16. phylogeo Geographic analysis and visualization of microbiome data
17. Phyloseq Import, share, and analyze microbiome census data using R
18. qiimer R tools compliment qiime
19. RAM R for Amplicon-Sequencing-Based Microbial-Ecology
20. ShinyPhyloseq Web-tool with user interface for Phyloseq
21. SigTree Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree
22. SPIEC-EASI Sparse and Compositionally Robust Inference of Microbial Ecological Networks
23. structSSI Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data
24. Tax4Fun Predicting functional profiles from metagenomic 16S rRNA gene data
25. taxize Taxonomic Information from Around the Web
26. labdsv Ordination and Multivariate Analysis for Ecology
27. Vegan R package for community ecologists
28. igraph Network Analysis and Visualization in R
29. MicrobiomeHD A standardized database of human gut microbiome studies in health and disease Case-Control
30. Rhea A pipeline with modular R scripts
31. microbiomeutilities Extending and supporting package based on microbiome and phyloseq R package
32. breakaway Species Richness Estimation and Modeling
33. corncob Modeling microbial abundances and dysbiosis with beta-binomial regression
34. MicrobiomeR MicrobiomeR: An R Package for Simplified and Standardized Microbiome Analysis Workflows
35. powmic Power assessment in microbiome case-control studies
36. yingtools2 Tools and functions for working with clinical and microbiome data
37. animalcules R shiny app for interactive microbiome analysis
38. biome-shiny GUI for microbiome visualization, based on the shiny package "microbiome"
39. MelonnPan Model-based Genomically Informed High-dimensional Predictor of Microbial Community Metabolic Profiles
40. MaAsLin2 MaAsLin2: Microbiome Multivariate Association with Linear Models
41. mbtools Collection of R tools to analyze microbiome data
42. ANCOM R scripts for Analysis of Composition of Microbiomes (ANCOM)
43. MetaLonDA METAgenomic LONgitudinal Differential Abundance method
44. dacomp Testing for Differential Abundance in Compositional Counts Data, with Application to Microbiome Studies
45. BEEM BEEM: Estimating Lotka-Volterra models from time-course microbiome sequencing data
46. metamicrobiomeR Analysis of Microbiome Relative Abundance Data using Zero Inflated Beta GAMLSS and Meta-Analysis Across Studies using Random Effects Model
47. GLMMMiRKAT A distance-based kernel association test based on the generalized linear mixed model
48. MDPbiome MDPbiome: microbiome engineering through prescriptive perturbations
49. bootLong The Block Bootstrap Method for Longitudinal Microbiome Data
50. OMiSA Optimal Microbiome-based Survival Analysis (OMiSA)
51. DECIPHER Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R
52. DECIPHER/IIDTAXA IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences
53. curatedMetagenomicData Accessible, curated metagenomic data through ExperimentHub
54. themetagenomics Exploring Thematic Structure and Predicted Functionality of 16S rRNA Amplicon Data
55. MDiNE MDiNE: a model to estimate differential co-occurrence networks in microbiome studies
56. StructFDR False Discovery Rate Control Incorporating Phylogenetic Tree Increases Detection Power in Microbiome-Wide Multiple Testing
57. metamicrobiomeR metamicrobiomeR: An R Package for Analysis of Microbiome Relative Abundance Data Using Zero-Inflated Beta GAMLSS and Meta-Analysis Across Studies Using Random Effects Models
58. Pldist Pldist: Ecological Dissimilarities for Paired and Longitudinal Microbiome Association Analysis
59. BDMMA Batch Effects Correction for Microbiome Data With Dirichlet-multinomial Regression
60. RCM A unified framework for unconstrained and constrained ordination of microbiome read count data
61. decontam Simple Statistical Identification and Removal of Contaminant Sequences in Marker-Gene and Metagenomics Data
62. ZIBBSeqDiscovery A Zero-inflated Beta-binomial Model for Microbiome Data Analysis
63. massMap A Two-Stage Microbial Association Mapping Framework With Advanced FDR Control
64. SplinectomeR SplinectomeR Enables Group Comparisons in Longitudinal Microbiome Studies
65. DMBC A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis With Microbial Compositions
66. MicrobiomeDDA An Omnibus Test for Differential Distribution Analysis of Microbiome Sequencing Data
67. NMIT Microbial Interdependence Association Test--a Non-parametric Microbial Interdependence Test
68. SparseMCMM Estimating and testing the microbial causal mediation effect with the high-dimensional and compositional microbiome data (SparseMCMM)
69. MTA Microbial trend analysis (MTA) for common dynamic trend, group comparison and classification in longitudinal microbiome study
70. miLineage A General Framework for Association Analysis of Microbial Communities on a Taxonomic Tree
71. zeroSum Reference Point Insensitive Molecular Data Analysis
72. MedTest A Distance-Based Approach for Testing the Mediation Effect of the Human Microbiome
73. qgraph Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation
74. Adaptive gPCA A method for structured dimensionality reduction

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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. patchwork The Composer of ggplots
   • Combining multiple plots made easy
8. ggpubr Extension of ggplot2 based data visualization
   • Publication ready plots
9. ggraph Grammar of Graph Graphics
   • Network graphs using ggplot2
10. gganimate A Grammar of Animated Graphics
    • Animate ggplot2 (Useful for presenting time-series dynamics of microbial communities)
11. ggforce Accelerating ggplot2
    • Zoom specific regions of the plots
12. factoextra Extract and Visualize the Results of Multivariate Data Analyses
    • Powerful package for multivvariate data analysis
13. ggcorrplot Visualization of a correlation matrix using ggplot2
14. tidyverse R packages for data science
    • Universe of several useful R packages for data handling, analysis and visualization
15. Extensions of ggplot Gallary of numerous data visualistion R pacakges

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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

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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!

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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.

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
RStudio Overview

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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.

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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
• List of 'good' research paper reproducible repositories
• 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.
Pre-print
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Google doc link

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