# mia: Microbiome analysis tools
Date: 2022-11-01

#### 2022-11-01

#### Package

mia 1.6.0

# Contents

`mia` implements tools for microbiome analysis based on the `SummarizedExperiment` (Morgan et al. 2020), `SingleCellExperiment` (Amezquita et al. 2020) and `TreeSummarizedExperiment` (Huang 2021) infrastructure. Data wrangling and analysis are the main scope of this package.

# 1 Installation

To install `mia`, install `BiocManager` first, if it is not installed. Afterwards use the `install` function from `BiocManager`.

    if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")
    BiocManager::install("mia")

# 2 Load mia

    library("mia")

# 3 Loading a `TreeSummarizedExperiment` object

A few example datasets are available via `mia`. For this vignette the `GlobalPatterns` dataset is loaded first.

    data(GlobalPatterns, package = "mia")
    tse <- GlobalPatterns
    tse

    ## class: TreeSummarizedExperiment
    ## dim: 19216 26
    ## metadata(0):
    ## assays(1): counts
    ## rownames(19216): 549322 522457 ... 200359 271582
    ## rowData names(7): Kingdom Phylum ... Genus Species
    ## colnames(26): CL3 CC1 ... Even2 Even3
    ## colData names(7): X.SampleID Primer ... SampleType Description
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: a LinkDataFrame (19216 rows)
    ## rowTree: 1 phylo tree(s) (19216 leaves)
    ## colLinks: NULL
    ## colTree: NULL

# 4 Functions for working with microbiome data

One of the main topics for analysing microbiome data is the application of taxonomic data to describe features measured. The interest lies in the connection between individual bacterial species and their relation to each other.

`mia` does not rely on a specific object type to hold taxonomic data, but uses specific columns in the `rowData` of a `TreeSummarizedExperiment` object. `taxonomyRanks` can be used to construct a `character` vector of available taxonomic levels. This can be used, for example, for subsetting.

    # print the available taxonomic ranks
    colnames(rowData(tse))

    ## [1] "Kingdom" "Phylum"  "Class"   "Order"   "Family"  "Genus"   "Species"

    taxonomyRanks(tse)

    ## [1] "Kingdom" "Phylum"  "Class"   "Order"   "Family"  "Genus"   "Species"

    # subset to taxonomic data only
    rowData(tse)[,taxonomyRanks(tse)]

    ## DataFrame with 19216 rows and 7 columns
    ##            Kingdom        Phylum        Class        Order        Family
    ##        <character>   <character>  <character>  <character>   <character>
    ## 549322     Archaea Crenarchaeota Thermoprotei           NA            NA
    ## 522457     Archaea Crenarchaeota Thermoprotei           NA            NA
    ## 951        Archaea Crenarchaeota Thermoprotei Sulfolobales Sulfolobaceae
    ## 244423     Archaea Crenarchaeota        Sd-NA           NA            NA
    ## 586076     Archaea Crenarchaeota        Sd-NA           NA            NA
    ## ...            ...           ...          ...          ...           ...
    ## 278222    Bacteria           SR1           NA           NA            NA
    ## 463590    Bacteria           SR1           NA           NA            NA
    ## 535321    Bacteria           SR1           NA           NA            NA
    ## 200359    Bacteria           SR1           NA           NA            NA
    ## 271582    Bacteria           SR1           NA           NA            NA
    ##              Genus                Species
    ##        <character>            <character>
    ## 549322          NA                     NA
    ## 522457          NA                     NA
    ## 951     Sulfolobus Sulfolobusacidocalda..
    ## 244423          NA                     NA
    ## 586076          NA                     NA
    ## ...            ...                    ...
    ## 278222          NA                     NA
    ## 463590          NA                     NA
    ## 535321          NA                     NA
    ## 200359          NA                     NA
    ## 271582          NA                     NA

The columns are recognized case insensitive. Additional functions are available to check for validity of taxonomic information or generate labels based on the taxonomic information.

    table(taxonomyRankEmpty(tse, "Species"))

    ##
    ## FALSE  TRUE
    ##  1413 17803

    head(getTaxonomyLabels(tse))

    ## [1] "Class:Thermoprotei"               "Class:Thermoprotei_1"
    ## [3] "Species:Sulfolobusacidocaldarius" "Class:Sd-NA"
    ## [5] "Class:Sd-NA_1"                    "Class:Sd-NA_2"

For more details see the man page `?taxonomyRanks`.

## 4.1 Merging and agglomeration based on taxonomic information.

Agglomeration of data based on these taxonomic descriptors can be performed using functions implemented in `mia`. In addition to the `aggValue` functions provide by `TreeSummarizedExperiment` `agglomerateByRank` is available. `agglomerateByRank` does not require tree data to be present.

`agglomerateByRank` constructs a `factor` to guide merging from the available taxonomic information. For more information on merging have a look at the man page via `?mergeRows`.

    # agglomerate at the Family taxonomic rank
    x1 <- agglomerateByRank(tse, rank = "Family")
    ## How many taxa before/after agglomeration?
    nrow(tse)

    ## [1] 19216

    nrow(x1)

    ## [1] 603

Tree data can also be shrunk alongside agglomeration, but this is turned of by default.

    # with agglomeration of the tree
    x2 <- agglomerateByRank(tse, rank = "Family",
                            agglomerateTree = TRUE)
    nrow(x2) # same number of rows, but

    ## [1] 603

    rowTree(x1) # ... different

    ##
    ## Phylogenetic tree with 19216 tips and 19215 internal nodes.
    ##
    ## Tip labels:
    ##   549322, 522457, 951, 244423, 586076, 246140, ...
    ## Node labels:
    ##   , 0.858.4, 1.000.154, 0.764.3, 0.995.2, 1.000.2, ...
    ##
    ## Rooted; includes branch lengths.

    rowTree(x2) # ... tree

    ##
    ## Phylogenetic tree with 496 tips and 260 internal nodes.
    ##
    ## Tip labels:
    ##   Family:Cenarchaeaceae, Family:SAGMA-X, Family:Nitrososphaeraceae, Family:Sulfolobaceae, Family:Halobacteriaceae, Family:MSBL1, ...
    ## Node labels:
    ##   root:ALL, Kingdom:Archaea, Phylum:Crenarchaeota, Class:C2, Class:Sd-NA, Class:Thaumarchaeota, ...
    ##
    ## Rooted; includes branch lengths.

For `agglomerateByRank` to work, taxonomic data must be present. Even though only one rank is available for the `enterotype` dataset, agglomeration can be performed effectively de-duplicating entries for the genus level.

    data(enterotype, package = "mia")
    taxonomyRanks(enterotype)

    ## [1] "Genus"

    agglomerateByRank(enterotype)

    ## class: TreeSummarizedExperiment
    ## dim: 552 280
    ## metadata(1): agglomerated_by_rank
    ## assays(1): counts
    ## rownames(552): NA Prosthecochloris ... Syntrophococcus Mogibacterium
    ## rowData names(1): Genus
    ## colnames(280): AM.AD.1 AM.AD.2 ... TS98_V2 TS99.2_V2
    ## colData names(9): Enterotype Sample_ID ... Age ClinicalStatus
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: NULL
    ## rowTree: NULL
    ## colLinks: NULL
    ## colTree: NULL

To keep data tidy, the agglomerated data can be stored as an alternative experiment in the object of origin. With this synchronized sample subsetting becomes very easy.

    altExp(tse, "family") <- x2

Keep in mind, that if you set `na.rm = TRUE`, rows with `NA` or similar value (defined via the `empty.fields` argument) will be removed. Depending on these settings different number of rows will be returned.

    x1 <- agglomerateByRank(tse, rank = "Species", na.rm = TRUE)
    altExp(tse,"species") <- agglomerateByRank(tse, rank = "Species", na.rm = FALSE)
    dim(x1)

    ## [1] 944  26

    dim(altExp(tse,"species"))

    ## [1] 2307   26

For convenience the function `splitByRanks` is available, which agglomerates data on all `ranks` selected. By default all available ranks will be used. The output is compatible to be stored as alternative experiments.

    altExps(tse) <- splitByRanks(tse)
    tse

    ## class: TreeSummarizedExperiment
    ## dim: 19216 26
    ## metadata(0):
    ## assays(1): counts
    ## rownames(19216): 549322 522457 ... 200359 271582
    ## rowData names(7): Kingdom Phylum ... Genus Species
    ## colnames(26): CL3 CC1 ... Even2 Even3
    ## colData names(7): X.SampleID Primer ... SampleType Description
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(7): Kingdom Phylum ... Genus Species
    ## rowLinks: a LinkDataFrame (19216 rows)
    ## rowTree: 1 phylo tree(s) (19216 leaves)
    ## colLinks: NULL
    ## colTree: NULL

    altExpNames(tse)

    ## [1] "Kingdom" "Phylum"  "Class"   "Order"   "Family"  "Genus"   "Species"

## 4.2 Constructing a tree from taxonomic data

Constructing a taxonomic tree from taxonomic data stored in `rowData` is quite straightforward and uses mostly functions implemented in `TreeSummarizedExperiment`.

    taxa <- rowData(altExp(tse,"Species"))[,taxonomyRanks(tse)]
    taxa_res <- resolveLoop(as.data.frame(taxa))
    taxa_tree <- toTree(data = taxa_res)
    taxa_tree$tip.label <- getTaxonomyLabels(altExp(tse,"Species"))
    rowNodeLab <- getTaxonomyLabels(altExp(tse,"Species"), make_unique = FALSE)
    altExp(tse,"Species") <- changeTree(altExp(tse,"Species"),
                                       rowTree = taxa_tree,
                                       rowNodeLab = rowNodeLab)

## 4.3 Transformation of assay data

Transformation of count data stored in `assays` is also a main task when work with microbiome data. `transformCounts` can be used for this and offers a few choices of available transformations. A modified object is returned and the transformed counts are stored in a new `assay`.

    assayNames(enterotype)

    ## [1] "counts"

    anterotype <- transformCounts(enterotype, method = "log10", pseudocount = 1)
    assayNames(enterotype)

    ## [1] "counts"

For more details have a look at the man page `?transformCounts`.

Sub-sampling to equal number of counts per sample. Also known as rarefying.

    data(GlobalPatterns, package = "mia")

    tse.subsampled <- subsampleCounts(GlobalPatterns,
                                      min_size = 60000,
                                      name = "subsampled",
                                      replace = TRUE,
                                      seed = 1938)
    tse.subsampled

    ## class: TreeSummarizedExperiment
    ## dim: 12403 25
    ## metadata(1): subsampleCounts_min_size
    ## assays(2): counts subsampled
    ## rownames(12403): 549322 522457 ... 535321 200359
    ## rowData names(7): Kingdom Phylum ... Genus Species
    ## colnames(25): CL3 CC1 ... Even2 Even3
    ## colData names(7): X.SampleID Primer ... SampleType Description
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: a LinkDataFrame (12403 rows)
    ## rowTree: 1 phylo tree(s) (19216 leaves)
    ## colLinks: NULL
    ## colTree: NULL

Alternatively, one can save both original TreeSE and subsampled TreeSE within a MultiAssayExperiment object.

    library(MultiAssayExperiment)
    mae <- MultiAssayExperiment(c("originalTreeSE" = GlobalPatterns,
                                  "subsampledTreeSE" = tse.subsampled))
    mae

    ## A MultiAssayExperiment object of 2 listed
    ##  experiments with user-defined names and respective classes.
    ##  Containing an ExperimentList class object of length 2:
    ##  [1] originalTreeSE: TreeSummarizedExperiment with 19216 rows and 26 columns
    ##  [2] subsampledTreeSE: TreeSummarizedExperiment with 12403 rows and 25 columns
    ## Functionality:
    ##  experiments() - obtain the ExperimentList instance
    ##  colData() - the primary/phenotype DataFrame
    ##  sampleMap() - the sample coordination DataFrame
    ##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
    ##  *Format() - convert into a long or wide DataFrame
    ##  assays() - convert ExperimentList to a SimpleList of matrices
    ##  exportClass() - save data to flat files

    # To extract specifically the subsampled TreeSE
    experiments(mae)$subsampledTreeSE

    ## class: TreeSummarizedExperiment
    ## dim: 12403 25
    ## metadata(1): subsampleCounts_min_size
    ## assays(2): counts subsampled
    ## rownames(12403): 549322 522457 ... 535321 200359
    ## rowData names(7): Kingdom Phylum ... Genus Species
    ## colnames(25): CL3 CC1 ... Even2 Even3
    ## colData names(7): X.SampleID Primer ... SampleType Description
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: a LinkDataFrame (12403 rows)
    ## rowTree: 1 phylo tree(s) (19216 leaves)
    ## colLinks: NULL
    ## colTree: NULL

## 4.4 Community indices

In the field of microbiome ecology several indices to describe samples and community of samples are available. In this vignette we just want to give a very brief introduction.

Functions for calculating alpha and beta diversity indices are available. Using `estimateDiversity` multiple diversity indices are calculated by default and results are stored automatically in `colData`. Selected indices can be calculated individually by setting `index = "shannon"` for example.

    tse <- estimateDiversity(tse)
    colnames(colData(tse))[8:ncol(colData(tse))]

    ## [1] "coverage"            "faith"               "fisher"
    ## [4] "gini_simpson"        "inverse_simpson"     "log_modulo_skewness"
    ## [7] "shannon"

Beta diversity indices are used to describe inter-sample connections. Technically they are calculated as `dist` object and reduced dimensions can be extracted using `cmdscale`. This is wrapped up in the `runMDS` function of the `scater` package, but can be easily used to calculated beta diversity indices using the established functions from the `vegan` package or any other package using comparable inputs.

    library(scater)
    altExp(tse,"Genus") <- runMDS(altExp(tse,"Genus"),
                                  FUN = vegan::vegdist,
                                  method = "bray",
                                  name = "BrayCurtis",
                                  ncomponents = 5,
                                  exprs_values = "counts",
                                  keep_dist = TRUE)

JSD and UniFrac are implemented in `mia` as well. `calculateJSD` can be used as a drop-in replacement in the example above (omit the `method` argument as well) to calculate the JSD. For calculating the UniFrac distance via `calculateUniFrac` either a `TreeSummarizedExperiment` must be used or a tree supplied via the `tree` argument. For more details see `?calculateJSD`, `?calculateUnifrac` or `?calculateDPCoA`.

`runMDS` performs the decomposition. Alternatively `runNMDS` can also be used.

## 4.5 Other indices

`estimateDominance` and `estimateEvenness` implement other sample-wise indices. The function behave equivalently to `estimateDiversity`. For more information see the corresponding man pages.

# 5 Utility functions

To make migration and adoption as easy as possible several utility functions are available.

## 5.1 Data loading functions

Functions to load data from `biom` files, `QIIME2` output, `DADA2` objects (Callahan et al. 2016) or `phyloseq` objects are available.

    data("esophagus", package = "phyloseq")

    esophagus

    ## phyloseq-class experiment-level object
    ## otu_table()   OTU Table:         [ 58 taxa and 3 samples ]
    ## phy_tree()    Phylogenetic Tree: [ 58 tips and 57 internal nodes ]

    esophagus <- makeTreeSEFromPhyloseq(esophagus)
    esophagus

    ## class: TreeSummarizedExperiment
    ## dim: 58 3
    ## metadata(0):
    ## assays(1): counts
    ## rownames(58): 59_8_22 59_5_13 ... 65_9_9 59_2_6
    ## rowData names(0):
    ## colnames(3): B C D
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: a LinkDataFrame (58 rows)
    ## rowTree: 1 phylo tree(s) (58 leaves)
    ## colLinks: NULL
    ## colTree: NULL

For more details have a look at the man page, for examples `?makeTreeSEFromPhyloseq`.

## 5.2 General wrapper functions

`getAbundanceFeature` and `getAbundanceSample` are wrappers on row-wise or column-wise assay data subsetting.

    abund <- getAbundanceSample(tse, "CC1", assay_name = "counts")
    all(abund == assay(tse, "counts")[,"CC1"])

    ## [1] TRUE

    abund <- getAbundanceFeature(tse, "522457", assay_name = "counts")
    all(abund == assay(tse, "counts")["522457",])

    ## [1] TRUE

## 5.3 Selecting most interesting features

`getTopTaxa` returns a vector of the most `top` abundant feature IDs.

    data("esophagus", package = "mia")
    top_taxa <- getTopTaxa(esophagus,
                           method = "mean",
                           top = 5,
                           assay_name = "counts")
    top_taxa

    ## [1] "59_2_6"  "59_7_6"  "59_8_22" "59_5_19" "65_6_2"

## 5.4 Generating tidy data

To generate tidy data as used and required in most of the tidyverse, `meltAssay` can be used. A `data.frame` in the long format will be returned.

    molten_data <- meltAssay(tse,
                             assay_name = "counts",
                             add_row_data = TRUE,
                             add_col_data = TRUE
    )
    molten_data

    ## # A tibble: 499,616 × 24
    ##    FeatureID SampleID counts Kingdom Phylum     Class Order Family Genus Species
    ##    <fct>     <fct>     <dbl> <chr>   <chr>      <chr> <chr> <chr>  <chr> <chr>
    ##  1 549322    CL3           0 Archaea Crenarcha… Ther… <NA>  <NA>   <NA>  <NA>
    ##  2 549322    CC1           0 Archaea Crenarcha… Ther… <NA>  <NA>   <NA>  <NA>
    ##  3 549322    SV1           0 Archaea Crenarcha… Ther… <NA>  <NA>   <NA>  <NA>
    ##  4 549322    M31Fcsw       0 Archaea Crenarcha… Ther… <NA>  <NA>   <NA>  <NA>
    ##  5 549322    M11Fcsw       0 Archaea Crenarcha… Ther… <NA>  <NA>   <NA>  <NA>
    ##  6 549322    M31Plmr       0 Archaea Crenarcha… Ther… <NA>  <NA>   <NA>  <NA>
    ##  7 549322    M11Plmr       0 Archaea Crenarcha… Ther… <NA>  <NA>   <NA>  <NA>
    ##  8 549322    F21Plmr       0 Archaea Crenarcha… Ther… <NA>  <NA>   <NA>  <NA>
    ##  9 549322    M31Tong       0 Archaea Crenarcha… Ther… <NA>  <NA>   <NA>  <NA>
    ## 10 549322    M11Tong       0 Archaea Crenarcha… Ther… <NA>  <NA>   <NA>  <NA>
    ## # … with 499,606 more rows, and 14 more variables: X.SampleID <fct>,
    ## #   Primer <fct>, Final_Barcode <fct>, Barcode_truncated_plus_T <fct>,
    ## #   Barcode_full_length <fct>, SampleType <fct>, Description <fct>,
    ## #   coverage <int>, faith <dbl>, fisher <dbl>, gini_simpson <dbl>,
    ## #   inverse_simpson <dbl>, log_modulo_skewness <dbl>, shannon <dbl>

# 6 Session info

    sessionInfo()

    ## R version 4.2.1 (2022-06-23)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 20.04.5 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
    ## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_GB              LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] phyloseq_1.42.0                scater_1.26.0
    ##  [3] ggplot2_3.3.6                  scuttle_1.8.0
    ##  [5] mia_1.6.0                      MultiAssayExperiment_1.24.0
    ##  [7] TreeSummarizedExperiment_2.6.0 Biostrings_2.66.0
    ##  [9] XVector_0.38.0                 SingleCellExperiment_1.20.0
    ## [11] SummarizedExperiment_1.28.0    Biobase_2.58.0
    ## [13] GenomicRanges_1.50.0           GenomeInfoDb_1.34.0
    ## [15] IRanges_2.32.0                 S4Vectors_0.36.0
    ## [17] BiocGenerics_0.44.0            MatrixGenerics_1.10.0
    ## [19] matrixStats_0.62.0             BiocStyle_2.26.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] ggbeeswarm_0.6.0            colorspace_2.0-3
    ##   [3] ellipsis_0.3.2              BiocNeighbors_1.16.0
    ##   [5] ggrepel_0.9.1               bit64_4.0.5
    ##   [7] fansi_1.0.3                 decontam_1.18.0
    ##   [9] codetools_0.2-18            splines_4.2.1
    ##  [11] sparseMatrixStats_1.10.0    cachem_1.0.6
    ##  [13] knitr_1.40                  ade4_1.7-19
    ##  [15] jsonlite_1.8.3              cluster_2.1.4
    ##  [17] BiocManager_1.30.19         compiler_4.2.1
    ##  [19] assertthat_0.2.1            Matrix_1.5-1
    ##  [21] fastmap_1.1.0               lazyeval_0.2.2
    ##  [23] cli_3.4.1                   BiocSingular_1.14.0
    ##  [25] htmltools_0.5.3             tools_4.2.1
    ##  [27] igraph_1.3.5                rsvd_1.0.5
    ##  [29] gtable_0.3.1                glue_1.6.2
    ##  [31] GenomeInfoDbData_1.2.9      reshape2_1.4.4
    ##  [33] dplyr_1.0.10                Rcpp_1.0.9
    ##  [35] jquerylib_0.1.4             rhdf5filters_1.10.0
    ##  [37] vctrs_0.5.0                 multtest_2.54.0
    ##  [39] ape_5.6-2                   nlme_3.1-160
    ##  [41] DECIPHER_2.26.0             iterators_1.0.14
    ##  [43] DelayedMatrixStats_1.20.0   xfun_0.34
    ##  [45] stringr_1.4.1               beachmat_2.14.0
    ##  [47] lifecycle_1.0.3             irlba_2.3.5.1
    ##  [49] zlibbioc_1.44.0             MASS_7.3-58.1
    ##  [51] scales_1.2.1                biomformat_1.26.0
    ##  [53] parallel_4.2.1              rhdf5_2.42.0
    ##  [55] yaml_2.3.6                  memoise_2.0.1
    ##  [57] gridExtra_2.3               yulab.utils_0.0.5
    ##  [59] sass_0.4.2                  stringi_1.7.8
    ##  [61] RSQLite_2.2.18              foreach_1.5.2
    ##  [63] ScaledMatrix_1.6.0          tidytree_0.4.1
    ##  [65] permute_0.9-7               BiocParallel_1.32.0
    ##  [67] rlang_1.0.6                 pkgconfig_2.0.3
    ##  [69] bitops_1.0-7                evaluate_0.17
    ##  [71] lattice_0.20-45             Rhdf5lib_1.20.0
    ##  [73] purrr_0.3.5                 treeio_1.22.0
    ##  [75] bit_4.0.4                   tidyselect_1.2.0
    ##  [77] plyr_1.8.7                  magrittr_2.0.3
    ##  [79] bookdown_0.29               R6_2.5.1
    ##  [81] generics_0.1.3              DelayedArray_0.24.0
    ##  [83] DBI_1.1.3                   pillar_1.8.1
    ##  [85] withr_2.5.0                 mgcv_1.8-41
    ##  [87] survival_3.4-0              RCurl_1.98-1.9
    ##  [89] tibble_3.1.8                crayon_1.5.2
    ##  [91] utf8_1.2.2                  rmarkdown_2.17
    ##  [93] viridis_0.6.2               grid_4.2.1
    ##  [95] data.table_1.14.4           blob_1.2.3
    ##  [97] vegan_2.6-4                 digest_0.6.30
    ##  [99] tidyr_1.2.1                 munsell_0.5.0
    ## [101] DirichletMultinomial_1.40.0 beeswarm_0.4.0
    ## [103] viridisLite_0.4.1           vipor_0.4.5
    ## [105] bslib_0.4.0

# References

Amezquita, Robert, Aaron Lun, Etienne Becht, Vince Carey, Lindsay Carpp, Ludwig Geistlinger, Federico Marini, et al. 2020. “Orchestrating Single-Cell Analysis with Bioconductor.” Nature Methods 17: 137–45. https://www.nature.com/articles/s41592-019-0654-x.

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: 581–83. https://doi.org/10.1038/nmeth.3869.

Huang, Ruizhu. 2021. TreeSummarizedExperiment: TreeSummarizedExperiment: A S4 Class for Data with Tree Structures.

Morgan, Martin, Valerie Obenchain, Jim Hester, and Hervé Pagès. 2020. SummarizedExperiment: SummarizedExperiment Container. https://bioconductor.org/packages/SummarizedExperiment.