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Metabarcoding with R

Source: vignettes/articles/states_of_fields_in_R.Rmd
states_of_fields_in_R.Rmd

This is a short introduction to other R packages in the field of metabarcoding analysis.

State of the Field in R

The metabarcoding ecosystem in the R language is mature, well-constructed, and relies on a very active community in both the bioconductor and cran projects. The bioconductor even creates specific task views in Metagenomics and Microbiome.

R package dada2 (Callahan et al. 2016) provides a highly cited and recommended clustering method (Pauvert et al. 2019). dada2 also provides tools to complete the metabarcoding analysis pipeline, including chimera detection and taxonomic assignment. phyloseq (McMurdie and Holmes 2013) (https://bioconductor.org/packages/release/bioc/html/phyloseq.html) facilitate metagenomics analysis by providing a way to store data (the phyloseq class) and both graphical and statistical functions.

The phyloseq package introduces the S4 class object (class physeq), which contains (i) an OTU sample matrix, (ii) a taxonomic table, (iii) a sample metadata table, and two optional slots for (iv) a phylogenetic tree and (v) reference sequences.

Some packages already extend the phyloseq packages. For example, the microbiome package collection (Ernst et al. 2023) provides some scripts and functions for manipulating microbiome datasets.The speedyseq package (McLaren 2020) provides faster versions of phyloseq’s plotting and taxonomic merging functions, some of which ([merge_samples2()] and [merge_taxa_vec()]) are integrated in MiscMetabar (thanks to Mike. R. McLaren). The phylosmith Smith (2023) package already provides some functions to extend and simplify the use of the phyloseq packages.

Other packages (mia forming the microbiome package collection and MicrobiotaProcess (Xu et al. 2023)) extend a new data structure using the comprehensive Bioconductor ecosystem of the SummarizedExperiment family.

MiscMetabar enriches this R ecosystem by providing functions to (i) describe your dataset visually, (ii) transform your data, (iii) explore biological diversity (alpha, beta, and taxonomic diversity), and (iv) simplify reproducibility. MiscMetabar is designed to complement and not compete with other R packages mentioned above. For example. The mia package is recommended for studies focusing on phylogenetic trees, and phylosmith allows easy visualization of co-occurrence networks. Using the MicrobiotaProcess::as.MPSE function, most of the utilities in the MicrobiotaProcess package are available with functions from the MiscMetabar.

I do not try to reinvent the wheel and prefer to rely on existing packages and classes rather than building a new framework. MiscMetabar is based on the phyloseq class from phyloseq, the most cited package in metagenomics (Wen et al. 2023). For a description and comparison of these integrated packages competing with phyloseq (e.g. microeco by C. Liu et al. (2020), EasyAmplicon by Y.-X. Liu et al. (2023) and MicrobiomeAnalystR by Lu et al. (2023)) see Wen et al. (2023). Note that some limitations of the phyloseq packages are circumvented thanks to phylosmith (Smith 2023), microViz ((Barnett, Arts, and Penders 2021)) and MiscMetabar.

Some packages provide an interactive interface useful for rapid exploration and for code-beginner biologists. Animalcules (Zhao et al. 2021) and microViz (Barnett, Arts, and Penders 2021) provides shiny interactive interface whereas MicrobiomeAnalystR (Lu et al. 2023) is a web-based platform.

Session information 

## R version 4.3.3 (2024-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 11 (bullseye)
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## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.13.so;  LAPACK version 3.9.0
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## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
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## loaded via a namespace (and not attached):
##  [1] digest_0.6.35     desc_1.4.3        R6_2.5.1          fastmap_1.1.1    
##  [5] xfun_0.43         magrittr_2.0.3    cachem_1.0.8      knitr_1.46       
##  [9] memoise_2.0.1     htmltools_0.5.8.1 rmarkdown_2.26    lifecycle_1.0.4  
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## [21] purrr_1.0.2       rstudioapi_0.16.0 tools_4.3.3       ragg_1.3.0       
## [25] bslib_0.7.0       evaluate_0.23     yaml_2.3.8        jsonlite_1.8.8   
## [29] rlang_1.1.3       fs_1.6.3

References

Barnett, David J. M., Ilja C. W. Arts, and John Penders. 2021. “microViz: An r Package for Microbiome Data Visualization and Statistics.” Journal of Open Source Software 6 (63): 3201. https://doi.org/10.21105/joss.03201.
Callahan, Benjamin J, Paul J McMurdie, Michael J Rosen, Andrew W Han, Amy Jo A Johnson, and Susan P Holmes. 2016. “DADA2: High-Resolution Sample Inference from Illumina Amplicon Data.” Nature Methods 13 (7): 581–83. https://doi.org/10.1038/nmeth.3869.
Ernst, Felix G. M., Sudarshan A. Shetty, Tuomas Borman, and Leo Lahti. 2023. Mia: Microbiome Analysis. https://doi.org/10.18129/B9.bioc.mia.
Liu, Chi, Yaoming Cui, Xiangzhen Li, and Minjie Yao. 2020. “microeco: an R package for data mining in microbial community ecology.” FEMS Microbiology Ecology 97 (2): fiaa255. https://doi.org/10.1093/femsec/fiaa255.
Liu, Yong-Xin, Lei Chen, Tengfei Ma, Xiaofang Li, Maosheng Zheng, Xin Zhou, Liang Chen, et al. 2023. “EasyAmplicon: An Easy-to-Use, Open-Source, Reproducible, and Community-Based Pipeline for Amplicon Data Analysis in Microbiome Research.” iMeta 2 (1): e83.
Lu, Yao, Guangyan Zhou, Jessica Ewald, Zhiqiang Pang, Tanisha Shiri, and Jianguo Xia. 2023. “MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data.” Nucleic Acids Research 51 (W1): W310–18. https://doi.org/10.1093/nar/gkad407.
McLaren, Michael. 2020. “Mikemc/Speedyseq: Speedyseq V0.2.0.” Zenodo. https://doi.org/10.5281/zenodo.3923184.
McMurdie, Paul J., and Susan Holmes. 2013. “Phyloseq: An r Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data.” PLoS ONE 8 (4): e61217. https://doi.org/10.1371/journal.pone.0061217.
Pauvert, Charlie, Marc Buée, Valérie Laval, Véronique Edel-Hermann, Laure Fauchery, Angélique Gautier, Isabelle Lesur, Jessica Vallance, and Corinne Vacher. 2019. “Bioinformatics Matters: The Accuracy of Plant and Soil Fungal Community Data Is Highly Dependent on the Metabarcoding Pipeline.” Fungal Ecology 41: 23–33. https://doi.org/10.1016/j.funeco.2019.03.005.
Smith, Schuyler. 2023. Phylosmith: Functions to Help Analyze Data as Phyloseq Objects. https://schuyler-smith.github.io/phylosmith/.
Wen, Tao, Guoqing Niu, Tong Chen, Qirong Shen, Jun Yuan, and Yong-Xin Liu. 2023. “The Best Practice for Microbiome Analysis Using r.” Protein & Cell, pwad024.
Xu, Shuangbin, Li Zhan, Wenli Tang, Qianwen Wang, Zehan Dai, Lang Zhou, Tingze Feng, et al. 2023. “MicrobiotaProcess: A Comprehensive r Package for Deep Mining Microbiome.” The Innovation 4 (2). https://doi.org/10.1016/j.xinn.2023.100388.
Zhao, Yue, Anthony Federico, Tyler Faits, Solaiappan Manimaran, Daniel Segrè, Stefano Monti, and W Evan Johnson. 2021. “Animalcules: Interactive Microbiome Analytics and Visualization in r.” Microbiome 9 (1): 1–16. https://doi.org/10.21203/rs.3.rs-29649/v2.