# Introduction

# Introduction to MiscMetabar: an R package to facilitate visualization and reproducibility in metabarcoding analysis

## Raison d’être

- Complete R packages dada2 and phyloseq
- Useful visualizations (`biplot_pq`, `circle_pq`, `upset_pq`, `ggvenn_pq`)
- Facilitate the use of targets package

## Quick overview

For an introduction to metabarcoding in R, Please visite the state of the field vignettes. The import, export and track vignette explains how import and export `phyloseq` object. Its also show how to summarize useful information (number of sequences, samples and clusters) across bioinformatic pipelines.

If you are interested in ecological metrics, see the vignettes describing alpha-diversity and beta-diversity analysis. The vignette filter taxa and samples describes some data-filtering processes using MiscMetabar and the reclustering tutorial introduces the different way of clustering already-clustered OTU/ASV. The vignette tengeler explore the dataset from Tengeler et al. (2020) using some MiscMetabar functions.

For developers, I also wrote a vignette describing som rules of codes.

### Summarize a physeq object

    library("MiscMetabar")
    library("phyloseq")
    data("data_fungi")
    summary_plot_pq(data_fungi)

### Create an interactive table of the tax_table

    data("GlobalPatterns", package = "phyloseq")
    tax_datatable(subset_taxa(
      GlobalPatterns,
      rowSums(GlobalPatterns@otu_table) > 100000
    ))

### Sankey diagram of the tax_table

    gp <- subset_taxa(GlobalPatterns, GlobalPatterns@tax_table[, 1] == "Archaea")
    sankey_pq(gp, taxa = c(1:5))

### Upset plot for visualize distribution of taxa in function of samples variables

    upset_pq(gp, "SampleType", taxa = "Class")

# References

Tengeler, A.C., Dam, S.A., Wiesmann, M. et al. Gut microbiota from persons with attention-deficit/hyperactivity disorder affects the brain in mice. Microbiome 8, 44 (2020). https://doi.org/10.1186/s40168-020-00816-x

# Session inform

    sessionInfo()
    #> R version 4.5.0 (2025-04-11)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Kali GNU/Linux Rolling
    #>
    #> Matrix products: default
    #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.29.so;  LAPACK version 3.12.0
    #>
    #> locale:
    #>  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C
    #>  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=C
    #>  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8
    #>  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C
    #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    #> [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
    #>
    #> time zone: Europe/Paris
    #> tzcode source: system (glibc)
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] MiscMetabar_0.14.3 purrr_1.0.4        dplyr_1.1.4        dada2_1.36.0
    #> [5] Rcpp_1.0.14        ggplot2_3.5.2      phyloseq_1.52.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] bitops_1.0-9                deldir_2.0-4
    #>   [3] permute_0.9-7               rlang_1.1.6
    #>   [5] magrittr_2.0.3              ade4_1.7-23
    #>   [7] matrixStats_1.5.0           compiler_4.5.0
    #>   [9] mgcv_1.9-3                  png_0.1-8
    #>  [11] vctrs_0.6.5                 reshape2_1.4.4
    #>  [13] stringr_1.5.1               pwalign_1.4.0
    #>  [15] pkgconfig_2.0.3             crayon_1.5.3
    #>  [17] fastmap_1.2.0               XVector_0.48.0
    #>  [19] labeling_0.4.3              Rsamtools_2.24.0
    #>  [21] rmarkdown_2.29              UCSC.utils_1.4.0
    #>  [23] xfun_0.52                   cachem_1.1.0
    #>  [25] GenomeInfoDb_1.44.0         jsonlite_2.0.0
    #>  [27] biomformat_1.36.0           rhdf5filters_1.20.0
    #>  [29] DelayedArray_0.34.1         Rhdf5lib_1.30.0
    #>  [31] BiocParallel_1.42.1         jpeg_0.1-11
    #>  [33] data.tree_1.1.0             parallel_4.5.0
    #>  [35] cluster_2.1.8.1             R6_2.6.1
    #>  [37] bslib_0.9.0                 stringi_1.8.7
    #>  [39] RColorBrewer_1.1-3          ComplexUpset_1.3.3
    #>  [41] GenomicRanges_1.60.0        jquerylib_0.1.4
    #>  [43] SummarizedExperiment_1.38.1 iterators_1.0.14
    #>  [45] knitr_1.50                  IRanges_2.42.0
    #>  [47] Matrix_1.7-3                splines_4.5.0
    #>  [49] igraph_2.1.4                tidyselect_1.2.1
    #>  [51] rstudioapi_0.17.1           abind_1.4-8
    #>  [53] yaml_2.3.10                 vegan_2.7-1
    #>  [55] codetools_0.2-20            hwriter_1.3.2.1
    #>  [57] lattice_0.22-7              tibble_3.3.0
    #>  [59] plyr_1.8.9                  Biobase_2.68.0
    #>  [61] withr_3.0.2                 ShortRead_1.66.0
    #>  [63] evaluate_1.0.3              survival_3.8-3
    #>  [65] RcppParallel_5.1.10         Biostrings_2.76.0
    #>  [67] pillar_1.10.2               MatrixGenerics_1.20.0
    #>  [69] DT_0.33                     foreach_1.5.2
    #>  [71] stats4_4.5.0                generics_0.1.4
    #>  [73] S4Vectors_0.46.0            scales_1.4.0
    #>  [75] glue_1.8.0                  tools_4.5.0
    #>  [77] interp_1.1-6                data.table_1.17.4
    #>  [79] GenomicAlignments_1.44.0    rhdf5_2.52.1
    #>  [81] grid_4.5.0                  tidyr_1.3.1
    #>  [83] ape_5.8-1                   crosstalk_1.2.1
    #>  [85] latticeExtra_0.6-30         colorspace_2.1-1
    #>  [87] patchwork_1.3.0             networkD3_0.4.1
    #>  [89] nlme_3.1-168                GenomeInfoDbData_1.2.14
    #>  [91] cli_3.6.5                   S4Arrays_1.8.1
    #>  [93] gtable_0.3.6                sass_0.4.10
    #>  [95] digest_0.6.37               BiocGenerics_0.54.0
    #>  [97] SparseArray_1.8.0           htmlwidgets_1.6.4
    #>  [99] farver_2.1.2                htmltools_0.5.8.1
    #> [101] multtest_2.64.0             lifecycle_1.0.4
    #> [103] httr_1.4.7                  MASS_7.3-65