# 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) accross 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")
    library("magrittr")
    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 4.3.3 (2024-02-29)
    #> Platform: x86_64-pc-linux-gnu (64-bit)
    #> Running under: Debian GNU/Linux 11 (bullseye)
    #>
    #> 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-r0.3.13.so;  LAPACK version 3.9.0
    #>
    #> 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] magrittr_2.0.3    MiscMetabar_0.9.1 purrr_1.0.2       dplyr_1.1.4
    #> [5] dada2_1.30.0      Rcpp_1.0.12       ggplot2_3.5.0     phyloseq_1.46.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] bitops_1.0-7                deldir_2.0-4
    #>   [3] permute_0.9-7               rlang_1.1.3
    #>   [5] ade4_1.7-22                 matrixStats_1.3.0
    #>   [7] compiler_4.3.3              mgcv_1.9-1
    #>   [9] png_0.1-8                   vctrs_0.6.5
    #>  [11] reshape2_1.4.4              stringr_1.5.1
    #>  [13] pkgconfig_2.0.3             crayon_1.5.2
    #>  [15] fastmap_1.1.1               XVector_0.42.0
    #>  [17] labeling_0.4.3              utf8_1.2.4
    #>  [19] Rsamtools_2.18.0            rmarkdown_2.26
    #>  [21] xfun_0.43                   zlibbioc_1.48.2
    #>  [23] cachem_1.0.8                GenomeInfoDb_1.38.8
    #>  [25] jsonlite_1.8.8              biomformat_1.30.0
    #>  [27] highr_0.10                  rhdf5filters_1.14.1
    #>  [29] DelayedArray_0.28.0         Rhdf5lib_1.24.2
    #>  [31] BiocParallel_1.36.0         jpeg_0.1-10
    #>  [33] parallel_4.3.3              cluster_2.1.6
    #>  [35] R6_2.5.1                    bslib_0.7.0
    #>  [37] stringi_1.8.3               RColorBrewer_1.1-3
    #>  [39] ComplexUpset_1.3.3          GenomicRanges_1.54.1
    #>  [41] jquerylib_0.1.4             SummarizedExperiment_1.32.0
    #>  [43] iterators_1.0.14            knitr_1.46
    #>  [45] IRanges_2.36.0              Matrix_1.6-5
    #>  [47] splines_4.3.3               igraph_2.0.3
    #>  [49] tidyselect_1.2.1            rstudioapi_0.16.0
    #>  [51] abind_1.4-5                 yaml_2.3.8
    #>  [53] vegan_2.6-4                 codetools_0.2-19
    #>  [55] hwriter_1.3.2.1             lattice_0.22-6
    #>  [57] tibble_3.2.1                plyr_1.8.9
    #>  [59] Biobase_2.62.0              withr_3.0.0
    #>  [61] ShortRead_1.60.0            evaluate_0.23
    #>  [63] survival_3.5-8              RcppParallel_5.1.7
    #>  [65] Biostrings_2.70.3           pillar_1.9.0
    #>  [67] MatrixGenerics_1.14.0       DT_0.33
    #>  [69] foreach_1.5.2               stats4_4.3.3
    #>  [71] generics_0.1.3              RCurl_1.98-1.14
    #>  [73] S4Vectors_0.40.2            munsell_0.5.1
    #>  [75] scales_1.3.0                glue_1.7.0
    #>  [77] tools_4.3.3                 interp_1.1-6
    #>  [79] data.table_1.15.4           GenomicAlignments_1.38.2
    #>  [81] rhdf5_2.46.1                grid_4.3.3
    #>  [83] tidyr_1.3.1                 ape_5.8
    #>  [85] crosstalk_1.2.1             latticeExtra_0.6-30
    #>  [87] colorspace_2.1-0            patchwork_1.2.0
    #>  [89] networkD3_0.4               nlme_3.1-164
    #>  [91] GenomeInfoDbData_1.2.11     cli_3.6.2
    #>  [93] fansi_1.0.6                 S4Arrays_1.2.1
    #>  [95] gtable_0.3.4                sass_0.4.9
    #>  [97] digest_0.6.35               BiocGenerics_0.48.1
    #>  [99] SparseArray_1.2.4           htmlwidgets_1.6.4
    #> [101] farver_2.1.1                htmltools_0.5.8.1
    #> [103] multtest_2.58.0             lifecycle_1.0.4
    #> [105] MASS_7.3-60.0.1