Mouse Muscle Stem Cell Project

Part 1: data QC and preprocessing.

Génome Québec Innovation Centre, McGill University, Montreal, Canada

Prepare analysis workflow

Set filepaths and parameters

set.seed(42)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
options(
  readr.show_progress = FALSE,
  digits = 2
)

Load packages

suppressPackageStartupMessages({
  library(tximeta)
  library(rjson)
  library(scater)
  library(alevinQC)
  library(biomaRt)
  library(tidyverse)
})

Define functions

create_qc_report <- function(sample_dir, sample_name, reports_dir) {
  checkAlevinInputFiles(sample_dir)

  alevinQCReport(
    baseDir = sample_dir,
    sampleId = sample_name,
    outputFile = paste0(sample_name, "_alevin_report.html"),
    outputFormat = "html_document",
    outputDir = reports_dir,
    forceOverwrite = TRUE
  )
}

get_markers <- function(sce10x) {
  macrophage <-
    which(rowData(sce10x)$gene_name %in% c("Adgre1", "Ptprc", "Itgam"))
  stem <- which(rowData(sce10x)$gene_name %in% c("Pax7"))
  # other <- which(rowData(sce10x)$gene_name %in% c("Pecam1"))
  fap <- which(rowData(sce10x)$gene_name %in% c("Pdgfra"))

  markers_dt <-
    assay(sce10x)[c(stem, fap, macrophage), ] %>%
    as.data.frame.matrix()
  barcode_labels <-
    colnames(markers_dt)

  no_markers <-
    markers_dt %>%
    summarize_all(list(~ (sum(.) == 0)))

  markers <-
    markers_dt %>%
    summarize_all(which.max)

  markers[unlist(no_markers)] <- 0L
  markers <-
    markers %>%
    data.table::transpose() %>%
    set_names("marker")

  markers <-
    markers %>%
    mutate(
      marker = case_when(
        marker == 0L ~ "none",
        marker == 1L ~ "stem",
        marker == 2L ~ "fap",
        marker %in% c(3L, 4L, 5L) ~ "macrophage"
      )
    ) %>%
    pull(marker)
  return(markers)
}


add_metadata <- function(sce10x, gene_metadata, sample_name, mito_thresh = 15) {
  rowData(sce10x) <-
    gene_metadata %>%
    left_join(tibble(ensembl_gene_id_version = rownames(sce10x)),
      .,
      by = "ensembl_gene_id_version"
    ) %>%
    dplyr::rename(chr = "chromosome_name") %>%
    mutate(gene_name = uniquifyFeatureNames(
      ensembl_gene_id_version,
      external_gene_name
    ))
  rownames(sce10x) <- rowData(sce10x)$gene_name

  combined_metrics <-
    perCellQCMetrics(
      sce10x,
      subsets = list(mito = which(rowData(sce10x)$chr ==
        "MT")),
      percent_top = c(500),
      exprs_values = c("counts"),
      flatten = TRUE
    ) %>%
    as_tibble() %>%
    dplyr::select(-total)

  spliced_metrics <-
    perCellQCMetrics(
      sce10x,
      exprs_values = c("spliced"),
      percent_top = NULL,
      flatten = TRUE
    ) %>%
    as_tibble() %>%
    dplyr::select(-total) %>%
    set_names(c("sum_spliced", "detected_spliced"))


  unspliced_metrics <-
    perCellQCMetrics(
      sce10x,
      exprs_values = c("unspliced"),
      percent_top = NULL,
      flatten = TRUE
    ) %>%
    as_tibble() %>%
    dplyr::select(-total) %>%
    set_names(c("sum_unspliced", "detected_unspliced"))

  counts_dt <-
    bind_cols(
      combined_metrics,
      spliced_metrics,
      unspliced_metrics
    ) %>%
    mutate(
      unspliced_ov_spliced = sum_unspliced / sum_spliced,
      unspliced_ov_total = sum_unspliced / sum,
      sample = sample_name,
      condition = case_when(
        str_sub(sample, 1, 1) == "y" ~ "yng",
        str_sub(sample, 1, 1) == "a" ~ "aged"
      ),
      high_mito = combined_metrics$subsets_mito_percent > mito_thresh
    ) %>%
    DataFrame(.)


  rownames(counts_dt) <-
    paste(colnames(sce10x),
      str_sub(counts_dt$sample, start = -2),
      sep = "_"
    )

  colData(sce10x) <- counts_dt


  return(sce10x)
}

load_data <- function(sample_name, sample_dir, spliced_unspliced) {
  col_data <-
    data.frame(
      names = sample_name,
      files = file.path(
        sample_dir,
        "alevin",
        "quants_mat.gz"
      ),
      stringsAsFactors = FALSE
    )

  sce10x <-
    tximeta::tximeta(
      coldata = col_data,
      type = "alevin"
    )

  # split into spliced and nspliced abundances matrices

  sce10x <-
    tximeta::splitSE(sce10x,
      as.data.frame(spliced_unspliced),
      assayName = "counts"
    )

  sce10x <- as(sce10x, "SingleCellExperiment")

  assays(sce10x) <- list(
    counts = assay(sce10x, "spliced") + assay(sce10x, "unspliced"),
    spliced = assay(sce10x, "spliced"),
    unspliced = assay(sce10x, "unspliced")
  )

  return(sce10x)
}

create_diag_plots <- function(sce10x) {
  plot_dt <-
    colData(sce10x) %>%
    as_tibble()

  p1 <-
    ggplot(plot_dt) +
    geom_point(aes(sum_spliced,
      sum_unspliced,
      colour = subsets_mito_percent
    ),
    size = 0.3,
    alpha = 0.3
    ) +
    scale_x_log10() +
    scale_y_log10() +
    # geom_vline(xintercept = unlist(metrics[10,2]), color = "green", linetype = 2) +
    # geom_hline(yintercept =  unlist(metrics[11,2]), color = "green", linetype = 2) +
    facet_wrap(sample ~ ., ncol = 2)

  p2 <-
    ggplot(plot_dt) +
    geom_histogram(aes(unspliced_ov_total, fill = markers), bins = 150) +
    facet_wrap(sample ~ ., ncol = 2)


  p3 <-
    ggplot(colData(sce10x) %>%
      as_tibble()) +
    geom_histogram(aes(detected), bins = 150) +
    facet_wrap(sample ~ ., ncol = 2) +
    scale_x_log10() +
    geom_vline(xintercept = c(300, 7000), color = "green", linetype = 2)


  p4 <-
    plotColData(sce10x,
      x = "sum",
      by_exprs_values = "counts",
      y = "detected",
      colour_by = "subsets_mito_percent",
      point_size = 1
    ) +
    scale_x_log10() +
    scale_y_log10() +
    facet_wrap(~ colData(sce10x)$sample, ncol = 2) +
    geom_vline(aes(xintercept = 1000), color = "green", linetype = 2) +
    geom_hline(aes(yintercept = 500), color = "green", linetype = 2)


  p5 <-
    plotScater(sce10x,
      nfeatures = 500,
      block1 = "sample",
      ncol = 2,
      exprs_values = "counts",
      colour_by = "subsets_mito_percent",
      line_width = 0.2
    ) +
    geom_hline(aes(yintercept = .95))

  ggsave(file.path(figures_dir, "spliced_vs_unspliced_scatter.pdf"), p1)
  ggsave(file.path(figures_dir, "spliced_ov_unspliced_histogram.pdf"), p2)
  ggsave(file.path(figures_dir, "detected_histogram.pdf"), p3)
  ggsave(file.path(figures_dir, "sum_vs_detected.pdf"), p4)
  ggsave(file.path(figures_dir, "cumulative_expression.jpeg"), p5)

  return(list(p1 = p1, p2 = p2, p3 = p3, p3 = p4, p3 = p5))
}

Define file paths

data_dir <- "./data"
salmon_dir <- file.path(data_dir, "salmon_out")
reports_dir <- file.path(data_dir, "alevin_reports")
figures_dir <- file.path("./figures", "qc_cells")

index_dir <-
  "/run/user/1000/gvfs/sftp:host=graham.computecanada.ca,user=rfarouni/home/rfarouni/projects/rrg-hsn/rfarouni/data/salmon_indices/m24/spliced_introns_collapsed_flank90"
json_file <-
  file.path(
    data_dir,
    "metadata",
    "gencode.vM24.annotation.expanded.json"
  )
gene_expanded_file <-
  file.path(
    index_dir,
    "gencode.vM24.annotation.expanded.features.tsv"
  )

list.files(index_dir)

 [1] "gencode.vM24.annotation.expanded.fa"           "gencode.vM24.annotation.expanded.features.tsv"
 [3] "gencode.vM24.annotation.expanded.gtf"          "gencode.vM24.annotation.expanded.sidx"        
 [5] "gencode.vM24.annotation.expanded.tx2gene.tsv"  "gencode.vM24.annotation.gtf.gz"               
 [7] "GRCm38.primary_assembly.genome.chrnames.txt"   "GRCm38.primary_assembly.genome.fa"            
 [9] "salmon_index.job"                              "salmon_m24_index.out"                         

Define sample names

sample_dirs <-
  list.dirs(salmon_dir,
    full.names = T,
    recursive = FALSE
  )

sample_names <- c(
  paste0("aged", 1:4),
  paste0("yng", 1:3)
)
sample_names

[1] "aged1" "aged2" "aged3" "aged4" "yng1"  "yng2"  "yng3" 

Step 1: Generate alevin summary QC report

purrr::map2(sample_dirs,
  sample_names,
  create_qc_report,
  reports_dir = reports_dir
)

Step 2: Get gene metadata

spliced_unspliced <-
  read_tsv(gene_expanded_file) %>%
  dplyr::rename(unspliced = "intron") # Rename the 'intron' column 'unspliced' to make it compatible with scVelo

Parsed with column specification:
cols(
  spliced = col_character(),
  intron = col_character()
)

spliced_unspliced

mart <-
  useDataset(
    "mmusculus_gene_ensembl",
    useMart("ensembl")
  )

gene_metadata <-
  getBM(
    filters = "ensembl_gene_id_version",
    attributes = c(
      "ensembl_gene_id_version",
      "external_gene_name",
      "gene_biotype",
      "chromosome_name",
      "description"
    ),
    values = spliced_unspliced$spliced,
    mart = mart
  )
gene_metadata

Step 3: Import abundances into R with tximeta

Create a linked transcriptome with tximeta.

tximeta::makeLinkedTxome(
  indexDir = file.path(index_dir, "gencode.vM24.annotation.expanded.sidx"),
  source = "GENCODE",
  genome = "GRCm38",
  organism = "Mus musculus",
  release = "M24",
  fasta = file.path(index_dir, "gencode.vM24.annotation.expanded.fa"),
  gtf = file.path(index_dir, "gencode.vM24.annotation.expanded.gtf"),
  write = TRUE,
  jsonFile = json_file
)

writing linkedTxome to ./data/metadata/gencode.vM24.annotation.expanded.json
linkedTxome is same as already in bfc

rjson::fromJSON(file = json_file)

[[1]]
[[1]]$index
[1] "gencode.vM24.annotation.expanded.sidx"

[[1]]$source
[1] "GENCODE"

[[1]]$organism
[1] "Mus musculus"

[[1]]$release
[1] "M24"

[[1]]$genome
[1] "GRCm38"

[[1]]$fasta
[1] "/run/user/1000/gvfs/sftp:host=graham.computecanada.ca,user=rfarouni/home/rfarouni/projects/rrg-hsn/rfarouni/data/salmon_indices/m24/spliced_introns_collapsed_flank90/gencode.vM24.annotation.expanded.fa"

[[1]]$gtf
[1] "/run/user/1000/gvfs/sftp:host=graham.computecanada.ca,user=rfarouni/home/rfarouni/projects/rrg-hsn/rfarouni/data/salmon_indices/m24/spliced_introns_collapsed_flank90/gencode.vM24.annotation.expanded.gtf"

[[1]]$sha256
[1] "ea9dfa0cdf7ed85fe348bf8ed32215c2261b01ba3dae7ebf007ef2afaf0213b1"

Load linked transcriptome

tximeta::loadLinkedTxome(json_file)

linkedTxome is same as already in bfc

Step 4: Load, split, and annotate data

load_annotate_data <- function(sample_name, sample_dir, spliced_unspliced, gene_metadata) {
  sce10x <- load_data(sample_name, sample_dir, spliced_unspliced)
  sce10x <- add_metadata(sce10x, gene_metadata, sample_name)
  colData(sce10x)$markers <- get_markers(sce10x)
  return(sce10x)
}

sce10x <-
  map2(sample_names,
    sample_dirs,
    load_annotate_data,
    spliced_unspliced = spliced_unspliced,
    gene_metadata = gene_metadata
  )

importing quantifications
importing alevin data is much faster after installing `fishpond` (>= 1.2.0)
reading in alevin gene-level counts across cells 
found matching linked transcriptome:
[ GENCODE - Mus musculus - release M24 ]
loading existing TxDb created: 2020-04-30 19:48:32
Loading required package: GenomicFeatures
Loading required package: AnnotationDbi

Attaching package: 'AnnotationDbi'

The following object is masked from 'package:dplyr':

    select

generating gene ranges
loading existing gene ranges created: 2020-04-30 19:48:34
fetching genome info for GENCODE
importing quantifications
importing alevin data is much faster after installing `fishpond` (>= 1.2.0)
reading in alevin gene-level counts across cells 
found matching linked transcriptome:
[ GENCODE - Mus musculus - release M24 ]
loading existing TxDb created: 2020-04-30 19:48:32
generating gene ranges
loading existing gene ranges created: 2020-04-30 19:48:34
fetching genome info for GENCODE
importing quantifications
importing alevin data is much faster after installing `fishpond` (>= 1.2.0)
reading in alevin gene-level counts across cells 
found matching linked transcriptome:
[ GENCODE - Mus musculus - release M24 ]
loading existing TxDb created: 2020-04-30 19:48:32
generating gene ranges
loading existing gene ranges created: 2020-04-30 19:48:34
fetching genome info for GENCODE
importing quantifications
importing alevin data is much faster after installing `fishpond` (>= 1.2.0)
reading in alevin gene-level counts across cells 
found matching linked transcriptome:
[ GENCODE - Mus musculus - release M24 ]
loading existing TxDb created: 2020-04-30 19:48:32
generating gene ranges
loading existing gene ranges created: 2020-04-30 19:48:34
fetching genome info for GENCODE
importing quantifications
importing alevin data is much faster after installing `fishpond` (>= 1.2.0)
reading in alevin gene-level counts across cells 
found matching linked transcriptome:
[ GENCODE - Mus musculus - release M24 ]
loading existing TxDb created: 2020-04-30 19:48:32
generating gene ranges
loading existing gene ranges created: 2020-04-30 19:48:34
fetching genome info for GENCODE
importing quantifications
importing alevin data is much faster after installing `fishpond` (>= 1.2.0)
reading in alevin gene-level counts across cells 
found matching linked transcriptome:
[ GENCODE - Mus musculus - release M24 ]
loading existing TxDb created: 2020-04-30 19:48:32
generating gene ranges
loading existing gene ranges created: 2020-04-30 19:48:34
fetching genome info for GENCODE
importing quantifications
importing alevin data is much faster after installing `fishpond` (>= 1.2.0)
reading in alevin gene-level counts across cells 
found matching linked transcriptome:
[ GENCODE - Mus musculus - release M24 ]
loading existing TxDb created: 2020-04-30 19:48:32
generating gene ranges
loading existing gene ranges created: 2020-04-30 19:48:34
fetching genome info for GENCODE

Combine the samples into one sce object

sce10x <- reduce(sce10x, cbind)
colData(sce10x)

DataFrame with 25952 rows and 16 columns
                          sum  detected percent_top_500 subsets_mito_sum subsets_mito_detected subsets_mito_percent sum_spliced detected_spliced
                    <numeric> <integer>       <numeric>        <numeric>             <integer>            <numeric>   <numeric>        <integer>
ATACCGATCACCTGGG_d1     17510      5013         49.1488          425.500                    14              2.43004     10878.1             3681
GACTATGTCCGGCTTT_d1     19538      5122         53.1112          526.998                    13              2.69730     14653.1             4088
ATGGTTGGTTGTAAAG_d1     18438      4910         52.3674         1327.485                    14              7.19973     14088.2             3978
GAGTCTACAGAAGTTA_d1     20504      5063         56.9594         1087.560                    14              5.30414     17555.5             4408
GATTGGTAGGGAGTGG_d1     21025      5503         50.6502          653.396                    13              3.10771     15001.1             4277
...                       ...       ...             ...              ...                   ...                  ...         ...              ...
GTTCCGTTCCGAAATC_g3       155        45             100          104.986                    11              67.7329     148.000               38
ACGTACATCGGCTGGT_g3       200       102             100          105.490                    11              52.7451     184.417               85
AGTTCGAGTTCTCCTG_g3       175        54             100          116.490                     9              66.5658     166.000               44
AATTTCCTCATGGATC_g3       155        33             100          129.990                     9              83.8645     150.000               28
AAGCGTTAGAAATTCG_g3       165        55             100          109.990                    11              66.6608     158.500               46
                    sum_unspliced detected_unspliced unspliced_ov_spliced unspliced_ov_total      sample   condition high_mito     markers
                        <numeric>          <integer>            <numeric>          <numeric> <character> <character> <logical> <character>
ATACCGATCACCTGGG_d1       6631.95               2446             0.609663           0.378752       aged1        aged     FALSE        none
GACTATGTCCGGCTTT_d1       4884.93               1993             0.333372           0.250022       aged1        aged     FALSE         fap
ATGGTTGGTTGTAAAG_d1       4349.80               1889             0.308755           0.235915       aged1        aged     FALSE  macrophage
GAGTCTACAGAAGTTA_d1       2948.46               1249             0.167950           0.143799       aged1        aged     FALSE         fap
GATTGGTAGGGAGTGG_d1       6023.90               2377             0.401564           0.286511       aged1        aged     FALSE         fap
...                           ...                ...                  ...                ...         ...         ...       ...         ...
GTTCCGTTCCGAAATC_g3        7.0000                  7            0.0472973          0.0451613        yng3         yng      TRUE        none
ACGTACATCGGCTGGT_g3       15.5833                 17            0.0845007          0.0779167        yng3         yng      TRUE        none
AGTTCGAGTTCTCCTG_g3        9.0000                 10            0.0542169          0.0514286        yng3         yng      TRUE        none
AATTTCCTCATGGATC_g3        5.0000                  5            0.0333333          0.0322581        yng3         yng      TRUE        none
AAGCGTTAGAAATTCG_g3        6.5000                  9            0.0410095          0.0393939        yng3         yng      TRUE        none

table(colData(sce10x)$sample)

aged1 aged2 aged3 aged4  yng1  yng2  yng3 
 4048  4341  3403  3383  3390  3987  3400 

n_exprs_genes <-
  nexprs(sce10x, byrow = TRUE, exprs_values = "counts")
sce10x <- sce10x[n_exprs_genes > 0, ]

sce10x

class: SingleCellExperiment 
dim: 34559 25952 
metadata(42): tximetaInfo quantInfo ... txomeInfo txdbInfo
assays(3): counts spliced unspliced
rownames(34559): Gm1992 Gm37587 ... mt-Cytb mt-Nd6
rowData names(6): ensembl_gene_id_version external_gene_name ... description gene_name
colnames(25952): ATACCGATCACCTGGG_d1 GACTATGTCCGGCTTT_d1 ... AATTTCCTCATGGATC_g3 AAGCGTTAGAAATTCG_g3
colData names(16): sum detected ... high_mito markers
reducedDimNames(0):
altExpNames(0):

Save the single cell experiment objects

saveRDS(sce10x, file.path(data_dir, "preprocessed", "sce10x.rds"))

Step 5: Create diagnostic plots

Make and save plots

plots <- create_diag_plots(sce10x)

Saving 7 x 7 in image

plots

$p1

$p2

$p3

$p3

$p3

sessionInfo()

R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] GenomicFeatures_1.40.0      AnnotationDbi_1.50.0        forcats_0.5.0               stringr_1.4.0               dplyr_0.8.5                
 [6] purrr_0.3.4                 readr_1.3.1                 tidyr_1.0.2                 tibble_3.0.1                tidyverse_1.3.0            
[11] biomaRt_2.44.0              alevinQC_1.4.0              scater_1.16.0               ggplot2_3.3.0               SingleCellExperiment_1.10.1
[16] SummarizedExperiment_1.18.1 DelayedArray_0.14.0         matrixStats_0.56.0          Biobase_2.48.0              GenomicRanges_1.40.0       
[21] GenomeInfoDb_1.24.0         IRanges_2.22.1              S4Vectors_0.26.0            BiocGenerics_0.34.0         rjson_0.2.20               
[26] tximeta_1.6.1              

loaded via a namespace (and not attached):
  [1] readxl_1.3.1                  backports_1.1.6               AnnotationHub_2.20.0          BiocFileCache_1.12.0         
  [5] plyr_1.8.6                    lazyeval_0.2.2                shinydashboard_0.7.1          BiocParallel_1.22.0          
  [9] digest_0.6.25                 ensembldb_2.12.0              htmltools_0.4.0               viridis_0.5.1                
 [13] fansi_0.4.1                   magrittr_1.5                  memoise_1.1.0                 Biostrings_2.56.0            
 [17] modelr_0.1.7                  askpass_1.1                   prettyunits_1.1.1             colorspace_1.4-1             
 [21] blob_1.2.1                    rvest_0.3.5                   rappdirs_0.3.1                haven_2.2.0                  
 [25] xfun_0.13                     crayon_1.3.4                  RCurl_1.98-1.2                jsonlite_1.6.1               
 [29] tximport_1.16.0               glue_1.4.0                    gtable_0.3.0                  zlibbioc_1.34.0              
 [33] XVector_0.28.0                BiocSingular_1.4.0            scales_1.1.0                  DBI_1.1.0                    
 [37] GGally_1.5.0                  Rcpp_1.0.4.6                  viridisLite_0.3.0             xtable_1.8-4                 
 [41] progress_1.2.2                bit_1.1-15.2                  rsvd_1.0.3                    DT_0.13                      
 [45] htmlwidgets_1.5.1             httr_1.4.1                    RColorBrewer_1.1-2            ellipsis_0.3.0               
 [49] farver_2.0.3                  pkgconfig_2.0.3               reshape_0.8.8                 XML_3.99-0.3                 
 [53] dbplyr_1.4.3                  labeling_0.3                  tidyselect_1.0.0              rlang_0.4.6                  
 [57] later_1.0.0                   munsell_0.5.0                 BiocVersion_3.11.1            cellranger_1.1.0             
 [61] tools_4.0.0                   cli_2.0.2                     generics_0.0.2                RSQLite_2.2.0                
 [65] broom_0.5.6                   evaluate_0.14                 fastmap_1.0.1                 yaml_2.2.1                   
 [69] knitr_1.28                    bit64_0.9-7                   fs_1.4.1                      AnnotationFilter_1.12.0      
 [73] nlme_3.1-147                  mime_0.9                      xml2_1.3.2                    compiler_4.0.0               
 [77] rstudioapi_0.11               beeswarm_0.2.3                curl_4.3                      interactiveDisplayBase_1.26.0
 [81] reprex_0.3.0                  stringi_1.4.6                 lattice_0.20-41               ProtGenerics_1.20.0          
 [85] Matrix_1.2-18                 vctrs_0.2.4                   pillar_1.4.3                  lifecycle_0.2.0              
 [89] BiocManager_1.30.10           BiocNeighbors_1.6.0           data.table_1.12.8             cowplot_1.0.0                
 [93] bitops_1.0-6                  irlba_2.3.3                   httpuv_1.5.2                  rtracklayer_1.48.0           
 [97] R6_2.4.1                      promises_1.1.0                gridExtra_2.3                 vipor_0.4.5                  
[101] assertthat_0.2.1              openssl_1.4.1                 rprojroot_1.3-2               withr_2.2.0                  
[105] GenomicAlignments_1.24.0      Rsamtools_2.4.0               GenomeInfoDbData_1.2.3        hms_0.5.3                    
[109] grid_4.0.0                    rmarkdown_2.1                 DelayedMatrixStats_1.10.0     base64enc_0.1-3              
[113] shiny_1.4.0.2                 lubridate_1.7.8               ggbeeswarm_0.6.0