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OtherTools.Rmd
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OtherTools.Rmd
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---
title: "Other Tools"
output: html_document
---
1. ggplot2 [An implementation of the Grammar of Graphics in R](https://ggplot2.tidyverse.org/)
* Widely used package for data visualization
2. ggvegan [ggplot-based versions of the plots produced by the vegan package](https://github.com/gavinsimpson/ggvegan)
* Convert base plots of vegan to ggplot.
3. ggord [A simple package for creating ordination plots with ggplot2](https://fawda123.github.io/ggord/)
* Alternative to ggvegan
4. cowplot [cowplot: Streamlined Plot Theme and Plot Annotations for ggplot2](https://wilkelab.org/cowplot/)
* Widely used package for combining multiple plots
4. ggridges [Ridgeline plots in ggplot2](https://wilkelab.org/ggridges)
5. ggtext [Improved text rendering support for ggplot2](https://wilkelab.org/ggtext/)
* More power in controlling annotations in plots (e.g. italicize taxa names in plots)
6. ggpubr [Extension of ggplot2 based data visualization](http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/)
* Publication ready plots
7. ggraph [Grammar of Graph Graphics](https://ggraph.data-imaginist.com)
* Network graphs using ggplot2
8. gganimate [A Grammar of Animated Graphics](https://gganimate.com)
* Animate ggplot2 (Useful for presenting time-series dynamics of microbial communities)
9. ggforce [Accelerating ggplot2](https://ggforce.data-imaginist.com)
* Zoom specific regions of the plots
10. factoextra [Extract and Visualize the Results of Multivariate Data Analyses](https://rpkgs.datanovia.com/factoextra/index.html)
* Powerful package for multivvariate data analysis
11. ggcorrplot [Visualization of a correlation matrix using ggplot2](https://rpkgs.datanovia.com/ggcorrplot/)
12. tidyverse [R packages for data science](https://www.tidyverse.org/)
* Universe of several useful R packages for data handling, analysis and visualization
13. Extensions of ggplot [Gallary of numerous data visualistion R pacakges](https://exts.ggplot2.tidyverse.org/gallery/)
14. ggtree [Visualization and annotation of phylogenetic trees (in R) ](https://github.com/YuLab-SMU/ggtree)
15. patchwork [The Composer of ggplots](https://patchwork.data-imaginist.com)
* Combining multiple plots made easy
16. pheatmap [Pretty Heatmaps](https://cran.r-project.org/web/packages/pheatmap/pheatmap.pdf)
17. gggenes [Draw gene arrow maps](https://wilkox.org/gggenes/)
18. gggenomes [A grammar of graphics for comparative genomics](https://thackl.github.io/gggenomes/)
19. ggplot2 extensions [gallery](https://exts.ggplot2.tidyverse.org/gallery/)
20. TreeHeatmap [A package to plot heatmaps at different levels of a tree](https://github.com/fionarhuang/TreeHeatmap)
21. Fastverse [The fastverse is a suite of complementary high-performance packages for statistical computing and data manipulation in R](https://sebkrantz.github.io/fastverse/)
22. DataExplorer [Automated data exploration process for analytic tasks and predictive modeling](https://boxuancui.github.io/DataExplorer/)
23. LinDA [a simple, robust and highly scalable approach to tackle the compositional effects in differential abundance analysis.](https://github.com/zhouhj1994/LinDA)
-------------------------------------------------------
### Proteomics resources
1. <sup>*</sup>RforProteomics [Using R for proteomics data analysis](https://bioconductor.org/packages/release/data/experiment/vignettes/RforProteomics/inst/doc/RforProteomics.html)
2. <sup>*</sup>RforProteomics [Visualisation of proteomics data using R and Bioconductor](https://onlinelibrary.wiley.com/doi/full/10.1002/pmic.201400392)
3. <sup>*</sup>proteomics [proteomics: Mass spectrometry and proteomics data analysis](http://master.bioconductor.org/packages/release/workflows/vignettes/proteomics/inst/doc/proteomics.html)
4. [Introduction to analysing microbial proteomics data in R](https://microsud.github.io/Bacterial-Proteomics-in-R/tutorial_main_doc.html)
-------------------------------------------------------
### RNAseq resources<sup>*<sup>
1. RNA-seq analysis in R [Workflow by Shulin Cao](https://rstudio-pubs-static.s3.amazonaws.com/462299_a9bc385f89b94b0aa95de0f3b7040b04.html)
2. RNA-seq workflow [RNA-seq workflow: gene-level exploratory analysis and differential expression](https://www.bioconductor.org/packages/devel/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html)
*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](https://f1000research.com/articles/5-1492/v2).
2. [Comeau AM and Colleagues: Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research](http://msystems.asm.org/content/2/1/e00127-16)
3. Schloss, P. D: [The Riffomonas Reproducible Research Tutorial Series](http://www.riffomonas.org/reproducible_research/)
4. [Shetty SA, Lahti L., et al: Tutorial from microbiome data analysis spring school 2018, Wageningen University and Research](https://mibwurrepo.github.io/Microbial-bioinformatics-introductory-course-Material-2018/introduction.html)
5. [Holmes S, Huber W.: Modern statistics for modern biology. Cambridge University Press; 2018 Nov 30.](http://web.stanford.edu/class/bios221/book/)
6. [Xu S, Yu G.: Workshop of microbiome dataset analysis using MicrobiotaProcess](https://yulab-smu.top/MicrobiotaProcessWorkshop/articles/MicrobiotaProcessWorkshop.html)
7. [Antoni Susin, Yiwen Wang, Kim-Anh Lê Cao, M.Luz Calle.: Variable selection in microbiome compositional data analysis: tutorial](https://malucalle.github.io/Microbiome-Variable-Selection/)
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](https://www.youtube.com/watch?v=cWJzjHh_3kk&t=337s) and
[RStudio Overview](https://www.youtube.com/watch?v=n3uue28FD0w)
-------------------------------------------------------
[View this webiste repository on GitHub](https://github.com/microsud/Tools-Microbiome-Anlaysis)
[Follow me on Twitter](https://twitter.com/gutmicrobe)
[googlescholar](https://scholar.google.nl/citations?hl=en&user=Vahc6LUAAAAJ&view_op=list_works&sortby=pubdate)
[ORCID ID: 0000-0001-7280-9915](http://orcid.org/0000-0001-7280-9915)
-------------------------------------------------------
### 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
* and so on .....
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