# Getting Started with `scDiagnostics`

# Purpose

Annotation transfer from a reference dataset is a key process for annotating cell types in new single-cell RNA-sequencing (scRNA-seq) experiments. This approach provides a quick, automated, and reproducible alternative to manual annotation based on marker gene expression. Despite its advantages, challenges such as dataset imbalance and unrecognized discrepancies between query and reference datasets can lead to inaccurate annotations and affect subsequent analyses.

The `scDiagnostics` package is designed to address these issues by offering a suite of diagnostic tools for the systematic evaluation of cell type assignments in scRNA-seq data. It provides functionality to assess whether query and reference datasets are well-aligned, which is crucial for ensuring accurate annotation transfer. In addition, `scDiagnostics` helps evaluate annotation ambiguity, cluster heterogeneity, and marker gene alignment. By providing insights into these aspects, `scDiagnostics` enables researchers to determine the precision with which cells from a new scRNA-seq experiment can be assigned to known cell types, thereby supporting more accurate and reliable downstream analysis.

# Installation

## Installation from Bioconductor (Release)

Users interested in using the stable release version of the `scDiagnostics` package: please follow the installation instructions here. This is the recommended way of installing the package.

## Installation from GitHub (Development)

To install the development version of the package from Github, use the following command:

    BiocManager::install("ccb-hms/scDiagnostics")

To build the package vignettes upon installation use:

    BiocManager::install("ccb-hms/scDiagnostics",
                         build_vignettes = TRUE,
                         dependencies = TRUE)

Once you have installed the package, you can load it with the following code:

    library(scDiagnostics)

# Preliminaries

To explore the full capabilities of the `scDiagnostics` package, you have the option to use your own data or leverage the datasets included within the `scDiagnostics` package itself. In this guide, we will focus on utilizing these built-in datasets, which provide a practical and convenient resource for demonstrating the features of `scDiagnostics.` These datasets are specifically designed to facilitate the exploration of the package’s functionalities and to help evaluate the accuracy of cell type assignments. You can learn more about the datasets by looking at the documentation of the datasets available in the reference manual.

## Loading Datasets

In these datasets available in the `scDiagnostics` package, `reference_data`, `query_data`, and `qc_data` are all SingleCellExperiment objects that include a `logcounts` assay, which stores the log-transformed expression values for the genes.

The `reference_data` and `query_data` objects both originate from scRNA-seq experiments on hematopoietic tissues, specifically bone marrow samples, as provided by the scRNAseq package. These datasets have undergone comprehensive processing and cleaning, ensuring high-quality data for downstream analysis. Log-normalized counts were added to both datasets using the scuttle package. The `query_data` object has been further annotated with cell type assignments using the SingleR package, and it includes `annotation_scores` that reflect the confidence in these annotations. Additionally, gene set scores were computed and incorporated into the `query_data` using the AUCell package. For feature selection, the top 500 highly variable genes (HVGs) common to both datasets were identified and retained using the scran package. Finally, dimensionality reduction techniques including PCA, t-SNE, and UMAP were applied to both datasets, with the results stored within each object using the scater package.

The `qc_data` dataset in this package is derived from the `hpca` dataset available in the celldex package. Like the other datasets, `qc_data` has undergone significant cleaning and processing to ensure high data quality. Quality control (QC) metrics were added using the scuttle package. Cell type annotations and associated annotation_scores were generated using the SingleR package. Additionally, the top highly variable genes were selected using the scran package to enhance the dataset’s utility for downstream analyses.

    # Load datasets
    data("reference_data")
    data("query_data")
    data("qc_data")

    # Set seed for reproducibility
    set.seed(0)

The `reference_data` contains a column data labeled `expert_annotation`, which provides cell type annotations assigned by experts. On the other hand, `query_data` also includes `expert_annotation`, but it additionally features `SingleR_annotation`, which is the cell type annotation generated by the SingleR package, a popular package for cell type assignment based on reference datasets. The `qc_data` object contains a special column called `annotation_scores`, which holds the scores from the `SingleR` annotations, providing a measure of confidence or relevance for the assigned cell types.

By working with these datasets, you can gain hands-on experience with the various diagnostic tools and functions offered by `scDiagnostics`, allowing you to better understand how well it aligns query and reference datasets, assesses annotation ambiguity, and evaluates cluster heterogeneity and marker gene alignment.

## Subsetting the Datasets

Some functions in the vignette are designed to work with SingleCellExperiment objects that contain data from only one cell type. We will create separate SingleCellExperiment objects that only CD4 cells, to ensure compatibility with these functions.

    # Load library
    library(scran)
    library(scater)

    # Subset to CD4 cells
    ref_data_cd4 <- reference_data[, which(
        reference_data$expert_annotation == "CD4")]
    query_data_cd4 <- query_data_cd4 <- query_data[, which(
        query_data$expert_annotation == "CD4")]

    # Select highly variable genes
    ref_top_genes <- getTopHVGs(ref_data_cd4, n = 500)
    #> Warning in getTopHVGs(ref_data_cd4, n = 500): 'getTopHVGs' is deprecated.
    #> Use 'scrapper::chooseHighlyVariableGenes' instead.
    #> See help("Deprecated")
    #> Warning in .model_gene_var(x = assay(x, i = assay.type), ...): 'modelGeneVar' is deprecated.
    #> Use 'scrapper::modelGeneVariances' instead.
    #> See help("Deprecated")
    #> Warning in fitTrendVar(fm, fv, ...): 'fitTrendVar' is deprecated.
    #> Use 'scrapper::fitVarianceTrend' instead.
    #> See help("Deprecated")
    #> Warning in combineBlocks(collected, method = method, equiweight = equiweight, : 'combineBlocks' is deprecated.
    #> See help("Deprecated")
    query_top_genes <- getTopHVGs(query_data_cd4, n = 500)
    #> Warning in getTopHVGs(query_data_cd4, n = 500): 'getTopHVGs' is deprecated.
    #> Use 'scrapper::chooseHighlyVariableGenes' instead.
    #> See help("Deprecated")
    #> Warning in .model_gene_var(x = assay(x, i = assay.type), ...): 'modelGeneVar' is deprecated.
    #> Use 'scrapper::modelGeneVariances' instead.
    #> See help("Deprecated")
    #> Warning in fitTrendVar(fm, fv, ...): 'fitTrendVar' is deprecated.
    #> Use 'scrapper::fitVarianceTrend' instead.
    #> See help("Deprecated")
    #> Warning in combineBlocks(collected, method = method, equiweight = equiweight, : 'combineBlocks' is deprecated.
    #> See help("Deprecated")
    common_genes <- intersect(ref_top_genes, query_top_genes)

    # Subset data by common genes
    ref_data_cd4 <- ref_data_cd4[common_genes,]
    query_data_cd4 <- query_data_cd4[common_genes,]

    # Run PCA on both datasets
    ref_data_cd4 <- runPCA(ref_data_cd4)
    query_data_cd4 <- runPCA(query_data_cd4)

# Getting Started with `scDiagnostics`

The functions introduced in this section represent just a subset of the functions available in the `scDiagnostics` package.

For a complete overview and detailed demonstrations of all the functions included in the package, please refer to the designated vignettes which you may browse from the pkgdown site for `scDiagnostics`. Each vignette is designed to address specific aspects of `scDiagnostics`, and this vignette highlights key functionalities to illustrate their applications. These vignettes provide in-depth guidance and examples for each function, helping users fully leverage the capabilities of `scDiagnostics` in their single-cell analyses.

# Visualization of Cell Type Annotations

For a detailed example of all possible functions to visualize reference and query datasets, please refer to the Visualization of Cell Type Annotations vignette.

## Visualization of Cell Type Annotations in Reduced Dimensions

### `plotCellTypePCA()`

The `plotCellTypePCA()` function provides a visual comparison of principal components (PCs) for different cell types across query and reference datasets. By projecting the query data onto the PCA space of the reference dataset, it creates informative plots to help you understand how various cell types are distributed in the principal component space.

    # Plot PCA data
    pc_plot <- plotCellTypePCA(
        query_data = query_data,
        reference_data = reference_data,
        cell_types = c("CD4", "CD8", "B_and_plasma", "Myeloid"),
        query_cell_type_col = "expert_annotation",
        ref_cell_type_col = "expert_annotation",
        pc_subset = 1:3
    )
    # Display plot
    pc_plot
    #> Picking joint bandwidth of 0.522
    #> Picking joint bandwidth of 0.513
    #> Picking joint bandwidth of 0.472

The function returns a `ggplot` object featuring pairwise scatter plots of the selected principal components. Each plot compares how different cell types from the query and reference datasets project onto the PCA space. This visualization aids in identifying how cell types distribute across PCs and facilitates comparisons between datasets.

The `reference_data` argument contains the reference cell data, which serves as the foundation for defining the PC space. The `query_data` parameter includes the query cell data that will be projected. The function uses the `ref_cell_type_col` and `query_cell_type_col` to identify the relevant cell type annotations in the reference and query datasets.

### `calculateDiscriminantSpace()`

Alternatively, you can also use the `calculateDiscriminantSpace()` function, which projects query single-cell RNA-seq data onto a discriminant space defined by a reference dataset. This approach helps evaluate the similarity between the query and reference data, offering insights into the classification of query cells.

    disc_output <- calculateDiscriminantSpace(
        reference_data = reference_data,
        query_data = query_data,
        ref_cell_type_col = "expert_annotation",
        query_cell_type_col = "SingleR_annotation"
    )

The function returns a comprehensive output that includes discriminant eigenvalues and eigenvectors, which represent the variance explained by each discriminant axis and are used to project the data. It also provides the projections of the reference and query data onto the discriminant space. The Mahalanobis distances between the query and reference cell types are calculated, offering insights into how close the query projections are to the reference. The cosine similarity scores provide another metric to assess the similarity between the datasets.

    plot(disc_output, plot_type = "scatterplot")
    #> Picking joint bandwidth of 0.103
    #> Picking joint bandwidth of 0.155
    #> Picking joint bandwidth of 0.164

Alternatively, you can create a boxplot comparing query and reference projections for a specific cell type. See the reference manual for an example.

## Visualization of Marker Expressions

Visualizing gene expression distributions is crucial for understanding dataset similarity and cell type-specific expression patterns. The `plotMarkerExpression()` function allows you to compare the expression levels of a specific gene between a reference dataset and a query dataset, both overall and within a specified cell type. This comparison is done using density plots, which help in assessing the alignment and potential discrepancies between datasets.

    plotMarkerExpression(reference_data = reference_data,
                         query_data = query_data,
                         ref_cell_type_col = "expert_annotation",
                         query_cell_type_col = "SingleR_annotation",
                         gene_name = "VPREB3",
                         cell_type = "B_and_plasma")
    #> Picking joint bandwidth of 0.324
    #> Picking joint bandwidth of 0.234

## Visualization of QC and Annotation Scores

The scatter plot illustrates the relationship between key QC metrics (e.g., total library size, percentage of mitochondrial genes) and cell type annotation scores. This visualization aids in assessing how QC factors might influence or correlate with the assigned cell type annotations.

    # Remove cell types with very few cells
    qc_data_subset <- qc_data[, !(qc_data$SingleR_annotation
                                  %in% c("Chondrocytes", "DC",
                                         "Neurons","Platelets"))]

    # Generate scatter plot
    library(ggplot2)
    p1 <- plotQCvsAnnotation(sce_object = qc_data_subset,
                             cell_type_col = "SingleR_annotation",
                             qc_col = "total",
                             score_col = "annotation_scores")
    p1 + xlab("Library Size")

This scatter plot can uncover patterns, such as whether cells with larger library sizes or higher mitochondrial content are linked to specific annotations. For example, cells exhibiting unusually high mitochondrial content may be flagged as low-quality or stressed, which could impact their annotation accuracy.

# Evaluation of Dataset and Marker Gene Alignment

For a detailed example of all possible functions to assess the alignment of datasets and marker genes, please refer to the Evaluation of Dataset and Marker Gene Alignment vignette.

## `comparePCASubspace()`

In single-cell RNA-seq analysis, evaluating how closely the subspaces defined by the leading principal components (PCs) of query and reference datasets match is crucial. This evaluation is key to understanding how each dataset captures and represents structure and variation. The `comparePCASubspace()` function is specifically designed to assess this alignment by calculating the cosine similarity between the loadings of the most significant variables for each principal component. This analysis is essential for measuring the extent of similarity between datasets, which is vital for precise cell type annotation and effective data integration.

    # Compare PCA subspaces between query and reference data
    subspace_comparison <- comparePCASubspace(
        query_data = query_data_cd4,
        reference_data = ref_data_cd4,
        query_cell_type_col = "expert_annotation",
        ref_cell_type_col = "expert_annotation",
        pc_subset = 1:5
    )

    # View weighted cosine similarity score
    subspace_comparison$weighted_cosine_similarity
    #> [1] 0.2620419

    # Plot output for PCA subspace comparison (if a plot method is available)
    plot(subspace_comparison)

## `plotPairwiseDistancesDensity()`

The `plotPairwiseDistancesDensity()` function calculates and visualizes pairwise distances or correlations between cell types in query and reference datasets, aiding in the evaluation of cell type annotation consistency in single-cell RNA sequencing (scRNA-seq) analysis. Operating on SingleCellExperiment objects, it allows users to specify cell types of interest and compute either distances or correlation coefficients, with the option to project data into PCA space for focused analysis. The function generates a density plot using `ggplot2`, comparing cell relationships within and between datasets.

    # Example usage of the function
    plotPairwiseDistancesDensity(query_data = query_data,
                                 reference_data = reference_data,
                                 query_cell_type_col = "expert_annotation",
                                 ref_cell_type_col = "expert_annotation",
                                 cell_type = "CD8",
                                 pc_subset = 1:10,
                                 distance_metric = "correlation",
                                 correlation_method = "pearson")

## `calculateWassersteinDistance()`

The code below illustrates how to use the `calculateWassersteinDistance()` function to compare the Wasserstein distances between CD4 cells in the reference and query datasets. The resulting plot provides insight into whether the differences between the datasets are statistically significant.

    # Generate the Wasserstein distance density plot
    wasserstein_data <- calculateWassersteinDistance(
        query_data = query_data_cd4,
        reference_data = ref_data_cd4,
        query_cell_type_col = "expert_annotation",
        ref_cell_type_col = "expert_annotation",
        pc_subset = 1:10,
    )
    plot(wasserstein_data)
    #> Picking joint bandwidth of 0.00895

## `calculateVarImpOverlap()`

Imagine you have two sets of data: one called reference_data and another called query_data. Both sets include information about gene expression and cell types, with columns named `expert_annotation` for the reference data and `SingleR_annotation` for the query data. You want to find out which genes are most important for each dataset and then compare them.

Here’s how you can do it with the function:

    # RF function to compare (between datasets) which genes are best at differentiating cell types
    rf_output <- calculateVarImpOverlap(reference_data = reference_data,
                                        query_data = query_data,
                                        query_cell_type_col = "SingleR_annotation",
                                        ref_cell_type_col = "expert_annotation",
                                        n_tree = 500,
                                        n_top = 50)

    # Comparison table
    rf_output$var_imp_comparison
    #>              CD4-CD8     CD4-B_and_plasma          CD4-Myeloid
    #>                 0.80                 0.76                 0.86
    #>     CD8-B_and_plasma          CD8-Myeloid B_and_plasma-Myeloid
    #>                 0.76                 0.86                 0.78

## `calculateAveragePairwiseCorrelation()`

The `calculateAveragePairwiseCorrelation()` function computes the average pairwise correlations between specified cell types in single-cell gene expression data. It calculates pairwise correlations between query and reference cells using a specified correlation method, and then averages these correlations for each cell type pair. This approach helps assess the similarity between cells in reference and query datasets and provides insights into the reliability of cell type annotations.

    # Compute pairwise correlations between specified cell types
    cor_matrix_avg <- calculateAveragePairwiseCorrelation(
      query_data = query_data,
      reference_data = reference_data,
      query_cell_type_col = "SingleR_annotation",
      ref_cell_type_col = "expert_annotation",
      cell_types = c("CD4", "CD8", "B_and_plasma"),
      pc_subset = 1:5,
      correlation_method = "spearman"
    )

    # Visualize the average pairwise correlation matrix
    plot(cor_matrix_avg)

# Detection and Analysis of Annotation Anomalies

For a detailed example of all possible functions to detect and analyze potentially anomalous cells, please refer to the Detection and Analysis of Annotation Anomalies vignette.

## Detection of Annotation Anomalies

The `detectAnomaly()` function is designed to identify anomalies in single-cell data by leveraging PCA projections and the Isolation Forest algorithm. This method is useful for detecting anomalies or unusual patterns in single-cell datasets, whether you’re analyzing a reference dataset or comparing a query dataset against it.

The function projects single-cell data onto a PCA space and builds an Isolation Forest model on this PCA space to detect anomalies. If a query dataset is provided, the function computes anomaly scores for the query data based on its PCA projections relative to the reference data. If no query data is provided, it computes anomaly scores for the reference data itself.

    # Perform anomaly detection
    anomaly_output <- detectAnomaly(reference_data = reference_data,
                                    query_data = query_data,
                                    ref_cell_type_col = "expert_annotation",
                                    query_cell_type_col = "SingleR_annotation",
                                    pc_subset = 1:5)
    # Plot the results for a specific cell type
    plot(anomaly_output,
         cell_type = "CD4",
         data_type = "query",
         pc_subset = 1:5)

## Analysis of Annotation Anomalies

The `calculateCellDistances()` function computes distances both within a reference dataset and between query cells and reference cells for each specified cell type. By first projecting data onto a PCA space, the function calculates Euclidean distances to quantify similarities and dissimilarities. For each cell type, it generates pairwise distances within the reference dataset and measures how far each query cell is from all reference cells. This approach enables detailed analysis of cell type-specific distances, aiding in the identification of outliers and other patterns of interest.

To identify anomalous cells within the query data, we first use the `detectAnomaly()` function, focusing specifically on the CD4 cell type. This function will compute anomaly scores for each CD4 cell in the query dataset based on their projection in the PCA space of the reference data. Next, we will plot the distance distributions for the top 6 CD4 cells with the highest anomaly scores. These distances, computed using the `calculateCellDistances()` function, will illustrate how these anomalous cells differ from the reference cells, providing insight into their potential outlier status and helping to visualize patterns within the data.

    # Identify outliers for CD4
    cd4_anomalies <- detectAnomaly(reference_data = reference_data,
                                   query_data = query_data,
                                   query_cell_type_col = "SingleR_annotation",
                                   ref_cell_type_col = "expert_annotation")
    #> Warning in .model_gene_var(x = assay(x, i = assay.type), ...): 'modelGeneVar' is deprecated.
    #> Use 'scrapper::modelGeneVariances' instead.
    #> See help("Deprecated")
    #> Warning in fitTrendVar(fm, fv, ...): 'fitTrendVar' is deprecated.
    #> Use 'scrapper::fitVarianceTrend' instead.
    #> See help("Deprecated")
    #> Warning in combineBlocks(collected, method = method, equiweight = equiweight, : 'combineBlocks' is deprecated.
    #> See help("Deprecated")
    #> Warning in scran::getTopHVGs(var_ref, n = n_hvgs): 'scran::getTopHVGs' is deprecated.
    #> Use 'scrapper::chooseHighlyVariableGenes' instead.
    #> See help("Deprecated")
    #> Warning in .model_gene_var(x = assay(x, i = assay.type), ...): 'modelGeneVar' is deprecated.
    #> Use 'scrapper::modelGeneVariances' instead.
    #> See help("Deprecated")
    #> Warning in fitTrendVar(fm, fv, ...): 'fitTrendVar' is deprecated.
    #> Use 'scrapper::fitVarianceTrend' instead.
    #> See help("Deprecated")
    #> Warning in combineBlocks(collected, method = method, equiweight = equiweight, : 'combineBlocks' is deprecated.
    #> See help("Deprecated")
    #> Warning in scran::getTopHVGs(var_query, n = n_hvgs): 'scran::getTopHVGs' is deprecated.
    #> Use 'scrapper::chooseHighlyVariableGenes' instead.
    #> See help("Deprecated")

    # Get the names of the top 6 anomalies
    cd4_top6_anomalies <- names(sort(cd4_anomalies$CD4$query_anomaly_scores,
                                     decreasing = TRUE)[1:6])

    # Fix: Add the "Query_" prefix so the names match the distance_data matrix!
    cd4_top6_anomalies <- paste0("Query_", cd4_top6_anomalies)

    # Plot the PC data
    distance_data <- calculateCellDistances(
        query_data = query_data,
        reference_data = reference_data,
        query_cell_type_col = "SingleR_annotation",
        ref_cell_type_col = "expert_annotation"
    )

    # Plot the densities of the distances
    plot(distance_data, ref_cell_type = "CD4", cell_names = cd4_top6_anomalies)

# R Session Info

    R version 4.6.1 (2026-06-24)
    Platform: x86_64-pc-linux-gnu
    Running under: Ubuntu 26.04 LTS

    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-r0.3.32.so;  LAPACK version 3.12.0

    locale:
     [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
     [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
     [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

    time zone: Etc/UTC
    tzcode source: system (glibc)

    attached base packages:
    [1] stats4    stats     graphics  grDevices utils     datasets  methods
    [8] base

    other attached packages:
     [1] scater_1.41.2               ggplot2_4.0.3
     [3] scran_1.41.1                scuttle_1.23.1
     [5] SingleCellExperiment_1.35.1 SummarizedExperiment_1.43.0
     [7] Biobase_2.73.1              GenomicRanges_1.65.0
     [9] Seqinfo_1.3.0               IRanges_2.47.2
    [11] S4Vectors_0.51.5            BiocGenerics_0.59.10
    [13] generics_0.1.4              MatrixGenerics_1.25.0
    [15] matrixStats_1.5.0           scDiagnostics_1.7.2
    [17] BiocStyle_2.41.0

    loaded via a namespace (and not attached):
     [1] gridExtra_2.3.1     cramer_0.9-4        rlang_1.3.0
     [4] magrittr_2.0.5      otel_0.2.0          ggridges_0.5.7
     [7] compiler_4.6.1      systemfonts_1.3.2   vctrs_0.7.3
    [10] stringr_1.6.0       pkgconfig_2.0.3     fastmap_1.2.0
    [13] XVector_0.53.0      labeling_0.4.3      rmarkdown_2.31
    [16] ggbeeswarm_0.7.3    ragg_1.5.2          purrr_1.2.2
    [19] xfun_0.59           bluster_1.23.0      cachem_1.1.0
    [22] beachmat_2.29.0     jsonlite_2.0.0      DelayedArray_0.39.3
    [25] BiocParallel_1.47.0 irlba_2.3.7         parallel_4.6.1
    [28] cluster_2.1.8.2     R6_2.6.1            bslib_0.11.0
    [31] stringi_1.8.7       RColorBrewer_1.1-3  ranger_0.18.0
    [34] limma_3.69.2        GGally_2.4.0        boot_1.3-32
    [37] jquerylib_0.1.4     Rcpp_1.1.2          knitr_1.51
    [40] Matrix_1.7-5        igraph_2.3.3        tidyselect_1.2.1
    [43] abind_1.4-8         yaml_2.3.12         viridis_0.6.5
    [46] codetools_0.2-20    lattice_0.22-9      tibble_3.3.1
    [49] withr_3.0.3         S7_0.2.2            evaluate_1.0.5
    [52] ggstats_0.13.0      pillar_1.11.1       BiocManager_1.30.27
    [55] scales_1.4.0        RhpcBLASctl_0.23-42 glue_1.8.1
    [58] metapod_1.21.0      maketools_1.3.2     tools_4.6.1
    [61] BiocNeighbors_2.7.2 sys_3.4.3           data.table_1.18.4
    [64] ScaledMatrix_1.21.0 locfit_1.5-9.12     buildtools_1.0.0
    [67] grid_4.6.1          tidyr_1.3.2         edgeR_4.11.4
    [70] beeswarm_0.4.0      BiocSingular_1.29.0 vipor_0.4.7
    [73] cli_3.6.6           rsvd_1.0.5          textshaping_1.0.5
    [76] S4Arrays_1.13.0     viridisLite_0.4.3   dplyr_1.2.1
    [79] gtable_0.3.6        isotree_0.6.1-5     sass_0.4.10
    [82] digest_0.6.39       SparseArray_1.13.2  ggrepel_0.9.8
    [85] dqrng_0.4.1         farver_2.1.2        htmltools_0.5.9
    [88] lifecycle_1.0.5     statmod_1.5.2       transport_0.15-4