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suppressPackageStartupMessages({
    library(here)
    library(readxl)
    library(BiocStyle)
    library(ggplot2)
    library(cowplot)
    library(patchwork)
    library(demuxmix)
    library(tidyverse)
    library(SingleCellExperiment)
    library(DropletUtils)
    library(scater)
})

Overview

  • There are 8 samples in this batch.
  • Each sample comes from a different donor (i.e. each sample is genetically distinct).
  • Each has a unique HTO label.

We used simple HTO labelling whereby each sample is labelled with 1 HTO, shown in the table below:

sample_metadata_df <- read_excel(
  here("data/C133_Neeland_batch1/data/sample_sheets/CITEseq_48 samples_design_2.xlsx"),
  col_types = 
    c("text", "text", "text", "numeric", "text", "numeric", "text", "date"))
sample_metadata_df$`HASHTAG ID` <- paste0(
  "Human_HTO_",
  sample_metadata_df$`HASHTAG ID`)
knitr::kable(sample_metadata_df[sample_metadata_df$Batch == 1, ])
Donor Sample name Disease Age Sex Batch HASHTAG ID DATE OF CAPTURE
1 1 CF 6 F 1 Human_HTO_1 2021-02-04
2 2 CF 6 F 1 Human_HTO_2 2021-02-04
3 3 CF 5 F 1 Human_HTO_3 2021-02-04
4 4 CF 6 F 1 Human_HTO_4 2021-02-04
5 5 CF 6 F 1 Human_HTO_5 2021-02-04
6 6 CF 5 M 1 Human_HTO_6 2021-02-04
7 7 CF 5 M 1 Human_HTO_7 2021-02-04
8 8 CF 6 M 1 Human_HTO_8 2021-02-04

Setting up the data

sce <- readRDS(here("data", "C133_Neeland_batch1",
                    "data", "SCEs", "C133_Neeland_batch1.CellRanger.SCE.rds"))
sce$Capture <- factor(sce$Sample)
capture_names <- levels(sce$Capture)
capture_names <- setNames(capture_names, capture_names)
sce$Sample <- NULL
sce
class: SingleCellExperiment 
dim: 36601 6600194 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(3): ID Symbol Type
colnames(6600194): 1_AAACCCAAGAAACCCA-1 1_AAACCCAAGAAACCCG-1 ...
  2_TTTGTTGTCTTTGCTA-1 2_TTTGTTGTCTTTGGAG-1
colData names(2): Barcode Capture
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(1): Antibody Capture

Calling cells from empty droplets

par(mfrow = c(1, 2))
lapply(capture_names, function(cn) {
  sce <- sce[, sce$Capture == cn]
  bcrank <- barcodeRanks(counts(sce))
  # Only showing unique points for plotting speed.
  uniq <- !duplicated(bcrank$rank)
  plot(
    x = bcrank$rank[uniq],
    y = bcrank$total[uniq],
    log = "xy",
    xlab = "Rank",
    ylab = "Total UMI count",
    main = cn,
    cex.lab = 1.2,
    xlim = c(1, 500000),
    ylim = c(1, 200000))
  abline(h = metadata(bcrank)$inflection, col = "darkgreen", lty = 2)
  abline(h = metadata(bcrank)$knee, col = "dodgerblue", lty = 2)
})
Total UMI count for each barcode in the dataset, plotted against its rank (in decreasing order of total counts). The inferred locations of the inflection (dark green dashed lines) and knee points (blue dashed lines) are also shown.

Total UMI count for each barcode in the dataset, plotted against its rank (in decreasing order of total counts). The inferred locations of the inflection (dark green dashed lines) and knee points (blue dashed lines) are also shown.

Remove empty droplets.

empties <- do.call(rbind, lapply(capture_names, function(cn) {
  message(cn)
  empties <- readRDS(
    here("data",
         "C133_Neeland_batch1",
         "data",
         "emptyDrops", paste0(cn, ".emptyDrops.rds")))
  empties$Capture <- cn
  empties
}))
tapply(
  empties$FDR,
  empties$Capture,
  function(x) sum(x <= 0.001, na.rm = TRUE)) |>
  knitr::kable(
    caption = "Number of non-empty droplets identified using `emptyDrops()` from **DropletUtils**.")
Number of non-empty droplets identified using emptyDrops() from DropletUtils.
x
C133_batch1_1 11900
C133_batch1_2 12923
sce <- sce[, which(empties$FDR <= 0.001)]
sce
class: SingleCellExperiment 
dim: 36601 24823 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(3): ID Symbol Type
colnames(24823): 1_AAACCCACACTTCCTG-1 1_AAACCCACAGACAAAT-1 ...
  2_TTTGTTGTCATTGGTG-1 2_TTTGTTGTCGATGGAG-1
colData names(2): Barcode Capture
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(1): Antibody Capture

Adding per cell quality control information

sce <- scuttle::addPerCellQC(sce)
head(colData(sce)) %>%
  data.frame %>%
  knitr::kable()
Barcode Capture sum detected altexps_Antibody.Capture_sum altexps_Antibody.Capture_detected altexps_Antibody.Capture_percent total
1_AAACCCACACTTCCTG-1 AAACCCACACTTCCTG-1 C133_batch1_1 25500 4750 1410 113 5.239688 26910
1_AAACCCACAGACAAAT-1 AAACCCACAGACAAAT-1 C133_batch1_1 31507 4604 1103 109 3.382398 32610
1_AAACCCACAGGACGAT-1 AAACCCACAGGACGAT-1 C133_batch1_1 24109 4620 1352 118 5.310082 25461
1_AAACCCACATCCTAAG-1 AAACCCACATCCTAAG-1 C133_batch1_1 24570 4672 1152 125 4.478656 25722
1_AAACCCAGTAACATCC-1 AAACCCAGTAACATCC-1 C133_batch1_1 17761 4216 1956 122 9.920373 19717
1_AAACCCAGTACAGTTC-1 AAACCCAGTACAGTTC-1 C133_batch1_1 32424 4139 2284 125 6.580615 34708

Demultiplexing with hashtag oligos (HTOs)

is_adt <- grepl("^A[0-9]+", rownames(altExp(sce, "Antibody Capture")))
is_hto <- grepl("^Human_HTO", rownames(altExp(sce, "Antibody Capture")))
altExp(sce, "HTO") <- altExp(sce, "Antibody Capture")[is_hto, ]
altExp(sce, "ADT") <- altExp(sce, "Antibody Capture")[is_adt, ]
altExp(sce, "Antibody Capture") <- NULL
hto_counts <- counts(altExp(sce, "HTO"))
xmax <- ceiling(max(log2(hto_counts + 1)))

C133_batch1_1

par(mfrow = c(3, 3))
lapply(rownames(hto_counts), function(i) {
  hist(
    log2(hto_counts[i, sce$Capture == "C133_batch1_1"] + 1),
    xlab = "log2(UMIs + 1)", 
    main = paste0("C133_1: ", i), 
    xlim = c(0, xmax), 
    breaks = seq(0, xmax, 0.5),
    cex.main = 0.8)
})
Number of UMIs for each HTO across all non-empty droplets.

Number of UMIs for each HTO across all non-empty droplets.

Prepare the data.

hto <- as.matrix(counts(altExp(sce[, sce$Capture == "C133_batch1_1"], "HTO")))
detected <- sce$detected[sce$Capture == "C133_batch1_1"]

df <- data.frame(t(hto), 
                 detected = detected, 
                 hto = colSums(hto))

df %>% 
  pivot_longer(cols = starts_with("Human_HTO")) %>%
  mutate(logged = log(value + 1)) %>%
  ggplot(aes(x = logged)) +
  geom_density(adjust = 5) +
  facet_wrap(~name, scales = "free")

df %>% 
  pivot_longer(cols = starts_with("Human_HTO")) %>%
  ggplot(aes(x = detected, y = hto)) +
  geom_density_2d() +
  facet_wrap(~name) 

Run demultiplexing.

dmm <- demuxmix(hto = hto, 
                rna = detected,
                model = "naive")
summary(dmm)
         Class NumObs    RelFreq   MedProb    ExpFPs        FDR
1  Human_HTO_1    788 0.06672877 0.9690497  43.68846 0.05544221
2  Human_HTO_2   1343 0.11372682 0.9809911  65.15727 0.04851621
3  Human_HTO_3   1581 0.13388094 0.9663791  86.58712 0.05476731
4  Human_HTO_4    945 0.08002371 0.9651959  59.92080 0.06340825
5  Human_HTO_5   1318 0.11160979 0.9641228  78.04980 0.05921836
6  Human_HTO_6    997 0.08442713 0.9658428  56.52317 0.05669325
7  Human_HTO_7   1125 0.09526632 0.9640846  67.78690 0.06025502
8  Human_HTO_8   1174 0.09941570 0.9763559  59.08626 0.05032901
9      singlet   9271 0.78507918 0.9674471 516.79977 0.05574369
10   multiplet   1964 0.16631383 0.9322457 279.58552 0.14235515
11    negative    574 0.04860699 0.9347520  73.21263 0.12754813
12   uncertain     91         NA        NA        NA         NA

Examine results.

p <- vector("list", nrow(hto))
for(i in 1:nrow(hto)){
  p[[i]] <- plotDmmHistogram(dmm, hto = i) + 
    coord_cartesian(ylim = c(0, 0.001),
                    xlim = c(-50, 1000)) +
    theme(axis.title = element_text(size = 8),
          axis.text = element_text(size = 6))
}

wrap_plots(p , ncol = 3) 

p <- vector("list", nrow(hto))
for(i in 1:nrow(hto)){
  p[[i]] <- plotDmmPosteriorP(dmm, hto = i) + 
    theme(axis.title = element_text(size = 8),
          axis.text = element_text(size = 6))
}

wrap_plots(p , ncol = 3) 

pAcpt(dmm) <- 0
classes1 <- dmmClassify(dmm)
classes1$dmmHTO <- ifelse(classes1$Type == "multiplet", "Doublet",
                           ifelse(classes1$Type %in% c("negative", "uncertain"), 
                                  "Negative", classes1$HTO))
table(classes1$dmmHTO)

    Doublet Human_HTO_1 Human_HTO_2 Human_HTO_3 Human_HTO_4 Human_HTO_5 
       2037         789        1347        1582         947        1319 
Human_HTO_6 Human_HTO_7 Human_HTO_8    Negative 
        998        1126        1175         580 

C133_batch1_2

par(mfrow = c(3, 3))
lapply(rownames(hto_counts), function(i) {
  hist(
    log2(hto_counts[i, sce$Capture == "C133_batch1_2"] + 1),
    xlab = "log2(UMIs + 1)", 
    main = paste0("C133_2: ", i), 
    xlim = c(0, xmax), 
    breaks = seq(0, xmax, 0.5),
    cex.main = 0.8)
})
Number of UMIs for each HTO across all non-empty droplets.

Number of UMIs for each HTO across all non-empty droplets.

Prepare the data.

hto <- as.matrix(counts(altExp(sce[, sce$Capture == "C133_batch1_2"], "HTO")))
detected <- sce$detected[sce$Capture == "C133_batch1_2"]

df <- data.frame(t(hto), 
                 detected = detected, 
                 hto = colSums(hto))

df %>% 
  pivot_longer(cols = starts_with("Human_HTO")) %>%
  mutate(logged = log(value + 1)) %>%
  ggplot(aes(x = logged)) +
  geom_density(adjust = 5) +
  facet_wrap(~name, scales = "free")

df %>% 
  pivot_longer(cols = starts_with("Human_HTO")) %>%
  ggplot(aes(x = detected, y = hto)) +
  geom_density_2d() +
  facet_wrap(~name) 

Run demultiplexing.

dmm <- demuxmix(hto = hto, 
                rna = detected,
                model = "naive")
summary(dmm)
         Class NumObs    RelFreq   MedProb    ExpFPs        FDR
1  Human_HTO_1    814 0.06340058 0.9636953  46.04119 0.05656166
2  Human_HTO_2   1383 0.10771867 0.9762956  73.37210 0.05305286
3  Human_HTO_3   1645 0.12812524 0.9611734 101.33687 0.06160296
4  Human_HTO_4    941 0.07329231 0.9592414  68.18893 0.07246433
5  Human_HTO_5   1455 0.11332658 0.9601077  93.67750 0.06438316
6  Human_HTO_6    966 0.07523950 0.9605405  61.36488 0.06352472
7  Human_HTO_7   1154 0.08988239 0.9588789  78.26995 0.06782491
8  Human_HTO_8   1175 0.09151803 0.9737753  65.52515 0.05576609
9      singlet   9533 0.74250331 0.9622643 587.77658 0.06165704
10   multiplet   2340 0.18225719 0.9216325 340.64483 0.14557471
11    negative    966 0.07523950 0.9141909 142.04041 0.14703976
12   uncertain     84         NA        NA        NA         NA

Examine results.

p <- vector("list", nrow(hto))
for(i in 1:nrow(hto)){
  p[[i]] <- plotDmmHistogram(dmm, hto = i) + 
    coord_cartesian(ylim = c(0, 0.001),
                    xlim = c(-50, 1000)) +
    theme(axis.title = element_text(size = 8),
          axis.text = element_text(size = 6))
}

wrap_plots(p , ncol = 3) 

p <- vector("list", nrow(hto))
for(i in 1:nrow(hto)){
  p[[i]] <- plotDmmPosteriorP(dmm, hto = i) + 
    theme(axis.title = element_text(size = 8),
          axis.text = element_text(size = 6))
}

wrap_plots(p , ncol = 3) 

pAcpt(dmm) <- 0
classes2 <- dmmClassify(dmm)
classes2$dmmHTO <- ifelse(classes2$Type == "multiplet", "Doublet",
                           ifelse(classes2$Type %in% c("negative", "uncertain"), 
                                  "Negative", classes2$HTO))
table(classes2$dmmHTO)

    Doublet Human_HTO_1 Human_HTO_2 Human_HTO_3 Human_HTO_4 Human_HTO_5 
       2412         814        1384        1646         941        1456 
Human_HTO_6 Human_HTO_7 Human_HTO_8    Negative 
        967        1154        1177         972 

Save HTO assignments

classes <- rbind(classes1, classes2)
all(rownames(classes) == colnames(sce))
[1] TRUE
sce$dmmHTO <- factor(classes$dmmHTO, 
                     levels = c(sort(unique(grep("Human",
                                               classes$dmmHTO,
                                               value = TRUE))), 
                                "Doublet",
                                "Negative"))

Demultiplexing cells without genotype reference

Matching donors across captures

library(vcfR)
f <- sapply(capture_names, function(cn) {
  here("data",
       "C133_Neeland_batch1",
       "data",
       "vireo", cn, "GT_donors.vireo.vcf.gz")
})
x <- lapply(f, read.vcfR, verbose = FALSE)
# Create unique ID for each locus in each capture.
y <- lapply(x, function(xx) {
  paste(
  xx@fix[,"CHROM"],
  xx@fix[,"POS"],
  xx@fix[,"REF"],
  xx@fix[,"ALT"],
  sep = "_")
})
# Only keep the loci in common between the 2 captures.
i <- lapply(y, function(yy) {
  na.omit(match(Reduce(intersect, y), yy))
})
# Construct genotype matrix at common loci from the 2 captures.
donor_names <- paste0("donor", 0:7)
g <- mapply(
  function(xx, ii) {
    apply(
      xx@gt[ii, donor_names],
      2,
      function(x) sapply(strsplit(x, ":"), `[[`, 1))
  },
  xx = x,
  ii = i,
  SIMPLIFY = FALSE)
# Count number of genotype matches between pairs of donors (one from each 
# capture) and convert to a proportion.
z <- lapply(2:length(capture_names), function(k) {
  zz <- matrix(
    NA_real_,
    nrow = length(donor_names),
    ncol = length(donor_names),
    dimnames = list(donor_names, donor_names))
  for (ii in rownames(zz)) {
    for (jj in colnames(zz)) {
      zz[ii, jj] <- sum(g[[1]][, ii] == g[[k]][, jj]) / nrow(g[[1]])
    }
  }
  zz
})
heatmaps <- lapply(seq_along(z), function(k) {
pheatmap::pheatmap(
  z[[k]],
  color = viridisLite::inferno(101),
  cluster_rows = FALSE,
  cluster_cols = FALSE,
  main = "Proportion of matching genotypes",
  display_numbers = TRUE,
  number_color = "grey50",
  labels_row = paste0("C133_batch1_1: ", rownames(z[[k]])),
  labels_col = paste0("C133_batch1_", k + 1, ": ", colnames(z[[k]])),
  silent = TRUE,
  fontsize = 10)
})

gridExtra::grid.arrange(grobs = lapply(heatmaps, `[[`, "gtable"), ncol = 1)
Proportion of matching genotypes between pairs of captures.

Proportion of matching genotypes between pairs of captures.

The table below gives the best matches between the captures.

best_match_df <- data.frame(
  c(
    list(rownames(z[[1]])),
    lapply(seq_along(z), function(k) {
      apply(
        z[[k]], 
        1,
        function(x) colnames(z[[k]])[which.max(x)])
    })),
  row.names = NULL)
colnames(best_match_df) <- capture_names
best_match_df$GeneticDonor <- LETTERS[seq_along(donor_names)]
best_match_df <- dplyr::select(best_match_df, GeneticDonor, everything())

knitr::kable(
  best_match_df, 
  caption = "Best match of donors between the scRNA-seq captures.")
Best match of donors between the scRNA-seq captures.
GeneticDonor C133_batch1_1 C133_batch1_2
A donor0 donor5
B donor1 donor1
C donor2 donor0
D donor3 donor7
E donor4 donor6
F donor5 donor4
G donor6 donor3
H donor7 donor2

Assigning barcodes to donors

vireo_df <- do.call(
  rbind,
  c(
    lapply(capture_names, function(cn) {
      # Read data
      vireo_df <- read.table(
        here("data",
             "C133_Neeland_batch1",
             "data",
             "vireo", cn, "donor_ids.tsv"),
        header = TRUE)
      
      # Replace `donor[0-9]+` with `donor_[A-Z]` using `best_match_df`.
      best_match <- setNames(
        c(best_match_df[["GeneticDonor"]], "Doublet", "Unknown"),
        c(best_match_df[[cn]], "doublet", "unassigned"))
      vireo_df$GeneticDonor <- factor(
        best_match[vireo_df$donor_id],
        levels = c(best_match_df[["GeneticDonor"]], "Doublet", "Unknown"))
      vireo_df$donor_id <- NULL
      vireo_df$best_singlet <- best_match[vireo_df$best_singlet]
      vireo_df$best_doublet <- sapply(
        strsplit(vireo_df$best_doublet, ","),
        function(x) {
          paste0(best_match[x[[1]]], ",", best_match[x[[2]]])
        })
      
      # Add additional useful metadata
      vireo_df$Confident <- factor(
        vireo_df$GeneticDonor == vireo_df$best_singlet,
        levels = c(TRUE, FALSE))
      vireo_df$Capture <- cn
      
      # Reorder so matches SCE.
      captureNumber <- sub("C133_batch1_", "", cn)
      vireo_df$colname <- paste0(captureNumber, "_", vireo_df$cell)
      j <- match(colnames(sce)[sce$Capture == cn], vireo_df$colname)
      stopifnot(!anyNA(j))
      vireo_df <- vireo_df[j, ]
      vireo_df
    }),
    list(make.row.names = FALSE)))

Vireo summary

We add the parsed outputs of vireo to the colData of the SingleCellExperiment object so that we can incorporate it into downstream analyses.

stopifnot(identical(colnames(sce), vireo_df$colname))
sce$GeneticDonor <- vireo_df$GeneticDonor
# NOTE: We exclude redundant columns.
sce$vireo <- DataFrame(
  vireo_df[, setdiff(
    colnames(vireo_df), 
    c("cell", "colname", "Capture", "GeneticDonor"))])
tmp_df <- data.frame(
    best_singlet = sce$vireo$best_singlet, 
    Confident = sce$vireo$Confident,
    Capture = sce$Capture)
p1 <- ggplot(tmp_df) + 
  geom_bar(
    aes(x = best_singlet, fill = Confident), 
    position = position_stack(reverse = TRUE)) + 
  coord_flip() +
  ylab("Number of droplets") +
  xlab("Best singlet") +
  theme_cowplot(font_size = 7)
p2 <- ggplot(tmp_df) + 
  geom_bar(
    aes(x = best_singlet, fill = Confident), 
    position = position_fill(reverse = TRUE)) + 
  coord_flip() +
  ylab("Proportion of droplets") +
  xlab("Best singlet") +
  theme_cowplot(font_size = 7)

(p1 + p1 + facet_grid(~Capture) + plot_layout(widths = c(1, 2))) / 
  (p2 + p2 + facet_grid(~Capture) + plot_layout(widths = c(1, 2))) +
  plot_layout(guides = "collect")
Number (top) and proportion (bottom) of droplets assigned to each donor based on genetics (best singlet), and if these were confidently or not confidently assigned, overall (left) and within each capture (right).

Number (top) and proportion (bottom) of droplets assigned to each donor based on genetics (best singlet), and if these were confidently or not confidently assigned, overall (left) and within each capture (right).

p3 <- ggplot(
  data.frame(
    GeneticDonor = sce$GeneticDonor, 
    Confident = sce$vireo$Confident,
    Capture = sce$Capture)) + 
  geom_bar(
    aes(x = GeneticDonor, fill = Confident), 
    position = position_stack(reverse = TRUE)) + 
  coord_flip() +
  ylab("Number of droplets") +
  xlab("Final donor assignment") +
  theme_cowplot(font_size = 7)

(p3 + p3 + facet_grid(~Capture) + plot_layout(widths = c(1, 2))) +
  plot_layout(guides = "collect")
Number and proportion of droplets assigned to each donor based on genetics (final assignment), and if these were confidently or not confidently assigned, overall (left) and within each capture (right).

Number and proportion of droplets assigned to each donor based on genetics (final assignment), and if these were confidently or not confidently assigned, overall (left) and within each capture (right).

Overall summary

p <- scater::plotColData(
  sce,
  "dmmHTO", 
  "GeneticDonor", 
  colour_by = "GeneticDonor", 
  other_fields = "Capture") +
  scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
  guides(colour = "none")
p / (p + facet_grid(~Capture))
Number of droplets assigned to each `dmmHTO`/`GeneticDonor` combination, overall (top) and within each capture (bottom)

Number of droplets assigned to each dmmHTO/GeneticDonor combination, overall (top) and within each capture (bottom)

janitor::tabyl(
  as.data.frame(colData(sce)[, c("dmmHTO", "GeneticDonor")]),
  dmmHTO,
  GeneticDonor) |>
  janitor::adorn_title(placement = "combined") |>
  janitor::adorn_totals("both") |>
  knitr::kable(
    caption = "Number of droplets assigned to each `dmmHTO`/`GeneticDonor` combination.")
Number of droplets assigned to each dmmHTO/GeneticDonor combination.
dmmHTO/GeneticDonor A B C D E F G H Doublet Unknown Total
Human_HTO_1 2 1548 1 7 0 1 1 1 8 34 1603
Human_HTO_2 2 2 1 2628 2 0 2 4 24 66 2731
Human_HTO_3 3195 2 0 0 0 0 0 1 13 17 3228
Human_HTO_4 2 0 1865 1 0 0 0 1 8 11 1888
Human_HTO_5 0 0 0 0 0 1 2748 1 12 13 2775
Human_HTO_6 2 1 1 0 0 1929 1 1 12 18 1965
Human_HTO_7 1 0 1 1 0 1 0 2250 10 16 2280
Human_HTO_8 2 0 3 1 2183 1 2 1 21 138 2352
Doublet 451 201 321 362 238 253 448 349 1765 61 4449
Negative 141 79 198 57 84 94 65 109 168 557 1552
Total 3798 1833 2391 3057 2507 2280 3267 2718 2041 931 24823

Save data

# without ambient RNA removal
saveRDS(
   sce,
   here("data",
        "C133_Neeland_batch1",
        "data",
        "SCEs",
        "C133_Neeland_batch1.preprocessed.SCE.rds"))

Session info


sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Melbourne
tzcode source: internal

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

other attached packages:
 [1] vcfR_1.15.0                 scater_1.30.1              
 [3] scuttle_1.12.0              DropletUtils_1.22.0        
 [5] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
 [7] Biobase_2.62.0              GenomicRanges_1.54.1       
 [9] GenomeInfoDb_1.38.6         IRanges_2.36.0             
[11] S4Vectors_0.40.2            BiocGenerics_0.48.1        
[13] MatrixGenerics_1.14.0       matrixStats_1.2.0          
[15] lubridate_1.9.3             forcats_1.0.0              
[17] stringr_1.5.1               dplyr_1.1.4                
[19] purrr_1.0.2                 readr_2.1.5                
[21] tidyr_1.3.1                 tibble_3.2.1               
[23] tidyverse_2.0.0             demuxmix_1.4.0             
[25] patchwork_1.2.0             cowplot_1.1.3              
[27] ggplot2_3.4.4               BiocStyle_2.30.0           
[29] readxl_1.4.3                here_1.0.1                 
[31] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3        rstudioapi_0.15.0        
  [3] jsonlite_1.8.8            magrittr_2.0.3           
  [5] ggbeeswarm_0.7.2          farver_2.1.1             
  [7] rmarkdown_2.25            fs_1.6.3                 
  [9] zlibbioc_1.48.0           vctrs_0.6.5              
 [11] DelayedMatrixStats_1.24.0 RCurl_1.98-1.14          
 [13] janitor_2.2.0             htmltools_0.5.7          
 [15] S4Arrays_1.2.0            BiocNeighbors_1.20.2     
 [17] cellranger_1.1.0          Rhdf5lib_1.24.2          
 [19] SparseArray_1.2.4         rhdf5_2.46.1             
 [21] sass_0.4.8                bslib_0.6.1              
 [23] cachem_1.0.8              whisker_0.4.1            
 [25] lifecycle_1.0.4           pkgconfig_2.0.3          
 [27] rsvd_1.0.5                Matrix_1.6-5             
 [29] R6_2.5.1                  fastmap_1.1.1            
 [31] snakecase_0.11.1          GenomeInfoDbData_1.2.11  
 [33] digest_0.6.34             colorspace_2.1-0         
 [35] ps_1.7.6                  rprojroot_2.0.4          
 [37] dqrng_0.3.2               irlba_2.3.5.1            
 [39] vegan_2.6-4               beachmat_2.18.1          
 [41] labeling_0.4.3            fansi_1.0.6              
 [43] timechange_0.3.0          mgcv_1.9-1               
 [45] httr_1.4.7                abind_1.4-5              
 [47] compiler_4.3.2            withr_3.0.0              
 [49] BiocParallel_1.36.0       viridis_0.6.5            
 [51] highr_0.10                HDF5Array_1.30.0         
 [53] R.utils_2.12.3            MASS_7.3-60.0.1          
 [55] DelayedArray_0.28.0       permute_0.9-7            
 [57] tools_4.3.2               vipor_0.4.7              
 [59] ape_5.7-1                 beeswarm_0.4.0           
 [61] httpuv_1.6.14             R.oo_1.26.0              
 [63] glue_1.7.0                callr_3.7.3              
 [65] nlme_3.1-164              rhdf5filters_1.14.1      
 [67] promises_1.2.1            grid_4.3.2               
 [69] getPass_0.2-4             cluster_2.1.6            
 [71] memuse_4.2-3              generics_0.1.3           
 [73] isoband_0.2.7             gtable_0.3.4             
 [75] tzdb_0.4.0                R.methodsS3_1.8.2        
 [77] pinfsc50_1.3.0            hms_1.1.3                
 [79] BiocSingular_1.18.0       ScaledMatrix_1.10.0      
 [81] utf8_1.2.4                XVector_0.42.0           
 [83] ggrepel_0.9.5             pillar_1.9.0             
 [85] limma_3.58.1              later_1.3.2              
 [87] splines_4.3.2             lattice_0.22-5           
 [89] renv_1.0.3                tidyselect_1.2.0         
 [91] locfit_1.5-9.8            knitr_1.45               
 [93] git2r_0.33.0              gridExtra_2.3            
 [95] edgeR_4.0.15              xfun_0.42                
 [97] statmod_1.5.0             pheatmap_1.0.12          
 [99] stringi_1.8.3             yaml_2.3.8               
[101] evaluate_0.23             codetools_0.2-19         
[103] BiocManager_1.30.22       cli_3.6.2                
[105] munsell_0.5.0             processx_3.8.3           
[107] jquerylib_0.1.4           Rcpp_1.0.12              
[109] parallel_4.3.2            sparseMatrixStats_1.14.0 
[111] bitops_1.0-7              viridisLite_0.4.2        
[113] scales_1.3.0              crayon_1.5.2             
[115] rlang_1.1.3