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
  18. gggenes Draw gene arrow maps
  19. gggenomes A grammar of graphics for comparative genomics
  20. ggplot2 extensions gallery
  21. TreeHeatmap A package to plot heatmaps at different levels of a tree
  22. Fastverse The fastverse is a suite of complementary high-performance packages for statistical computing and data manipulation in R
  23. DataExplorer Automated data exploration process for analytic tasks and predictive modeling
  24. LinDA a simple, robust and highly scalable approach to tackle the compositional effects in differential abundance analysis.

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
  7. Antoni Susin, Yiwen Wang, Kim-Anh Lê Cao, M.Luz Calle.: Variable selection in microbiome compositional data analysis: tutorial

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.

TODO

Any help is welcome