Demultiplexing the C133_Neeland_batch5 data set
Peter Hickey and Jovana Maksimovic
2024-02-26
Last updated: 2024-02-26
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Knit directory: paed-inflammation-CITEseq/
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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_batch5/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$Batch == 5, ])| Donor | Sample name | Disease | Age | Sex | Batch | HASHTAG ID | DATE OF CAPTURE |
|---|---|---|---|---|---|---|---|
| 28 | 28 | True control | 1.0958904 | M | 5 | Human_HTO_6 | 2021-09-09 |
| 29 | 29 | CSLD | 7.4602740 | F | 5 | Human_HTO_7 | 2021-09-09 |
| 30 | 30 | CF | 0.9506849 | F | 5 | Human_HTO_9 | 2021-09-09 |
| 31 | 31 | CF | 1.4739726 | M | 5 | Human_HTO_10 | 2021-09-09 |
| 32 | 32 | Wheeze | 5.9000000 | M | 5 | Human_HTO_12 | 2021-09-09 |
| 33 | 33 | CF | 3.9232877 | F | 5 | Human_HTO_13 | 2021-09-09 |
| 34 | 34 | CF | 5.2356164 | M | 5 | Human_HTO_14 | 2021-09-09 |
| 35 | 35 | CF | 5.9753425 | F | 5 | Human_HTO_15 | 2021-09-09 |
Setting up the data
sce <- readRDS(here("data", "C133_Neeland_batch5",
"data", "SCEs", "C133_Neeland_batch5.CellRanger.SCE.rds"))
sce$Capture <- factor(sce$Sample)
capture_names <- levels(sce$Capture)
capture_names <- setNames(capture_names, capture_names)
sce$Sample <- NULL
sceclass: SingleCellExperiment
dim: 36601 6971974
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(3): ID Symbol Type
colnames(6971974): 1_AAACCCAAGAAACACT-1 1_AAACCCAAGAAACCAT-1 ...
2_TTTGTTGTCTTTGCTG-1 2_TTTGTTGTCTTTGTCG-1
colData names(2): Barcode Capture
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(1): Antibody CaptureCalling cells from empty droplets
par(mfrow = c(1, 2))
lapply(capture_names, function(cn) {
sce <- sce[, sce$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)$knee, col = "dodgerblue", lty = 2)
})
Total UMI count for each barcode in the dataset, plotted against its rank (in decreasing order of total counts). The inferred locations of the inflection (dark green dashed lines) and knee points (blue dashed lines) are also shown.
Remove empty droplets.
empties <- do.call(rbind, lapply(capture_names, function(cn) {
message(cn)
empties <- readRDS(
here("data",
"C133_Neeland_batch5",
"data",
"emptyDrops", paste0(cn, ".emptyDrops.rds")))
empties$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**.")| x | |
|---|---|
| C133_batch5_1 | 24905 |
| C133_batch5_2 | 25763 |
sce <- sce[, which(empties$FDR <= 0.001)]
sceclass: SingleCellExperiment
dim: 36601 50668
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(3): ID Symbol Type
colnames(50668): 1_AAACCCAAGAAGATCT-1 1_AAACCCAAGATGCAGC-1 ...
2_TTTGTTGTCGGATTAC-1 2_TTTGTTGTCTGAGAGG-1
colData names(2): Barcode Capture
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(1): Antibody CaptureAdding per cell quality control information
sce <- scuttle::addPerCellQC(sce)
head(colData(sce)) %>%
data.frame %>%
knitr::kable()| Barcode | Capture | sum | detected | altexps_Antibody.Capture_sum | altexps_Antibody.Capture_detected | altexps_Antibody.Capture_percent | total | |
|---|---|---|---|---|---|---|---|---|
| 1_AAACCCAAGAAGATCT-1 | AAACCCAAGAAGATCT-1 | C133_batch5_1 | 3556 | 1822 | 3027 | 166 | 45.98208 | 6583 |
| 1_AAACCCAAGATGCAGC-1 | AAACCCAAGATGCAGC-1 | C133_batch5_1 | 1227 | 719 | 2296 | 162 | 65.17173 | 3523 |
| 1_AAACCCAAGCACTAAA-1 | AAACCCAAGCACTAAA-1 | C133_batch5_1 | 11917 | 3278 | 4356 | 166 | 26.76827 | 16273 |
| 1_AAACCCAAGGAGAGGC-1 | AAACCCAAGGAGAGGC-1 | C133_batch5_1 | 5706 | 2020 | 3361 | 167 | 37.06849 | 9067 |
| 1_AAACCCAAGGATACAT-1 | AAACCCAAGGATACAT-1 | C133_batch5_1 | 10001 | 3478 | 4361 | 163 | 30.36485 | 14362 |
| 1_AAACCCAAGGATACCG-1 | AAACCCAAGGATACCG-1 | C133_batch5_1 | 9187 | 2850 | 3285 | 165 | 26.33900 | 12472 |
Demultiplexing with hashtag oligos (HTOs)
is_adt <- grepl("^A[0-9]+", rownames(altExp(sce, "Antibody Capture")))
is_hto <- grepl("^Human_HTO", rownames(altExp(sce, "Antibody Capture")))
altExp(sce, "HTO") <- altExp(sce, "Antibody Capture")[is_hto, ]
altExp(sce, "ADT") <- altExp(sce, "Antibody Capture")[is_adt, ]
altExp(sce, "Antibody Capture") <- NULL
hto_counts <- counts(altExp(sce, "HTO"))
xmax <- ceiling(max(log2(hto_counts + 1)))C133_batch5_1
par(mfrow = c(3, 3))
lapply(rownames(hto_counts), function(i) {
hist(
log2(hto_counts[i, sce$Capture == "C133_batch5_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.
Prepare the data.
hto <- as.matrix(counts(altExp(sce[, sce$Capture == "C133_batch5_1"], "HTO")))
detected <- sce$detected[sce$Capture == "C133_batch5_1"]
df <- data.frame(t(hto),
detected = detected,
hto = colSums(hto))
df %>%
pivot_longer(cols = starts_with("Human_HTO")) %>%
mutate(logged = log(value + 1)) %>%
ggplot(aes(x = logged)) +
geom_density(adjust = 5) +
facet_wrap(~name, scales = "free")
df %>%
pivot_longer(cols = starts_with("Human_HTO")) %>%
ggplot(aes(x = detected, y = hto)) +
geom_density_2d() +
facet_wrap(~name) 
Run demultiplexing.
dmm <- demuxmix(hto = hto,
rna = detected,
model = "naive")
summary(dmm) Class NumObs RelFreq MedProb ExpFPs FDR
1 Human_HTO_10 3520 0.14500515 0.8792272 553.3198 0.1571931
2 Human_HTO_12 1147 0.04725026 0.9004420 170.2223 0.1484065
3 Human_HTO_13 508 0.02092688 0.8842605 79.3876 0.1562748
4 Human_HTO_14 3660 0.15077240 0.8997272 513.5337 0.1403098
5 Human_HTO_15 3381 0.13927909 0.8789055 520.9637 0.1540857
6 Human_HTO_6 2354 0.09697219 0.8758755 372.6597 0.1583091
7 Human_HTO_7 871 0.03588054 0.9006736 128.6942 0.1477545
8 Human_HTO_9 1167 0.04807415 0.8807931 187.2453 0.1604502
9 singlet 16608 0.68416066 0.8842431 2526.0263 0.1520970
10 multiplet 5595 0.23048404 0.8404114 1188.3842 0.2124011
11 negative 2072 0.08535530 0.7930126 512.4674 0.2473298
12 uncertain 630 NA NA NA NAExamine results.
p <- vector("list", nrow(hto))
for(i in 1:nrow(hto)){
p[[i]] <- plotDmmHistogram(dmm, hto = i) +
coord_cartesian(ylim = c(0, 0.001),
xlim = c(-50, 1000)) +
theme(axis.title = element_text(size = 8),
axis.text = element_text(size = 6))
}
wrap_plots(p , ncol = 3) 
p <- vector("list", nrow(hto))
for(i in 1:nrow(hto)){
p[[i]] <- plotDmmPosteriorP(dmm, hto = i) +
theme(axis.title = element_text(size = 8),
axis.text = element_text(size = 6))
}
wrap_plots(p , ncol = 3) 
pAcpt(dmm) <- 0
classes1 <- dmmClassify(dmm)
classes1$dmmHTO <- ifelse(classes1$Type == "multiplet", "Doublet",
ifelse(classes1$Type %in% c("negative", "uncertain"),
"Negative", classes1$HTO))
table(classes1$dmmHTO)
Doublet Human_HTO_10 Human_HTO_12 Human_HTO_13 Human_HTO_14 Human_HTO_15
6034 3544 1156 512 3679 3395
Human_HTO_6 Human_HTO_7 Human_HTO_9 Negative
2376 883 1173 2153 C133_batch5_2
par(mfrow = c(3, 3))
lapply(rownames(hto_counts), function(i) {
hist(
log2(hto_counts[i, sce$Capture == "C133_batch5_2"] + 1),
xlab = "log2(UMIs + 1)",
main = paste0("C133_2: ", i),
xlim = c(0, xmax),
breaks = seq(0, xmax, 0.5),
cex.main = 0.8)
})
Number of UMIs for each HTO across all non-empty droplets.
Prepare the data.
hto <- as.matrix(counts(altExp(sce[, sce$Capture == "C133_batch5_2"], "HTO")))
detected <- sce$detected[sce$Capture == "C133_batch5_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_10 3404 0.13705359 0.8676921 576.92511 0.1694845
2 Human_HTO_12 1204 0.04847606 0.8939240 187.78576 0.1559682
3 Human_HTO_13 560 0.02254701 0.8789921 89.94687 0.1606194
4 Human_HTO_14 3666 0.14760237 0.8875104 553.63686 0.1510193
5 Human_HTO_15 3412 0.13737569 0.8672174 560.23655 0.1641959
6 Human_HTO_6 2306 0.09284535 0.8670192 388.23607 0.1683591
7 Human_HTO_7 859 0.03458550 0.8968726 123.42747 0.1436874
8 Human_HTO_9 1153 0.04642268 0.8747345 189.02508 0.1639420
9 singlet 16564 0.66690824 0.8745076 2669.21977 0.1611458
10 multiplet 5991 0.24121271 0.8216820 1368.36594 0.2284036
11 negative 2282 0.09187905 0.7818686 586.50753 0.2570147
12 uncertain 926 NA NA NA NAExamine 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$dmmHTO)
Doublet Human_HTO_10 Human_HTO_12 Human_HTO_13 Human_HTO_14 Human_HTO_15
6590 3442 1211 562 3713 3436
Human_HTO_6 Human_HTO_7 Human_HTO_9 Negative
2338 864 1162 2445 Save HTO assignments
classes <- rbind(classes1, classes2)
all(rownames(classes) == colnames(sce))[1] TRUEsce$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_batch5",
"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_batch5_1: ", rownames(z[[k]])),
labels_col = paste0("C133_batch5_", 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.
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$GeneticDonor <- LETTERS[seq_along(donor_names)]
best_match_df <- dplyr::select(best_match_df, GeneticDonor, everything())
knitr::kable(
best_match_df,
caption = "Best match of donors between the scRNA-seq captures.")| GeneticDonor | C133_batch5_1 | C133_batch5_2 |
|---|---|---|
| A | donor0 | donor3 |
| B | donor1 | donor0 |
| C | donor2 | donor4 |
| D | donor3 | donor7 |
| E | donor4 | donor1 |
| F | donor5 | donor2 |
| G | donor6 | donor5 |
| H | donor7 | donor6 |
Assigning barcodes to donors
vireo_df <- do.call(
rbind,
c(
lapply(capture_names, function(cn) {
# Read data
vireo_df <- read.table(
here("data",
"C133_Neeland_batch5",
"data",
"vireo", cn, "donor_ids.tsv"),
header = TRUE)
# Replace `donor[0-9]+` with `donor_[A-Z]` using `best_match_df`.
best_match <- setNames(
c(best_match_df[["GeneticDonor"]], "Doublet", "Unknown"),
c(best_match_df[[cn]], "doublet", "unassigned"))
vireo_df$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_batch5_", "", 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$Capture)
p1 <- ggplot(tmp_df) +
geom_bar(
aes(x = best_singlet, fill = Confident),
position = position_stack(reverse = TRUE)) +
coord_flip() +
ylab("Number of droplets") +
xlab("Best singlet") +
theme_cowplot(font_size = 7)
p2 <- ggplot(tmp_df) +
geom_bar(
aes(x = best_singlet, fill = Confident),
position = position_fill(reverse = TRUE)) +
coord_flip() +
ylab("Proportion of droplets") +
xlab("Best singlet") +
theme_cowplot(font_size = 7)
(p1 + p1 + facet_grid(~Capture) + plot_layout(widths = c(1, 2))) /
(p2 + p2 + facet_grid(~Capture) + plot_layout(widths = c(1, 2))) +
plot_layout(guides = "collect")
Number (top) and proportion (bottom) of droplets assigned to each donor based on genetics (best singlet), and if these were confidently or not confidently assigned, overall (left) and within each capture (right).
p3 <- ggplot(
data.frame(
GeneticDonor = sce$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).
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)
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.")| dmmHTO/GeneticDonor | A | B | C | D | E | F | G | H | Doublet | Unknown | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Human_HTO_10 | 1 | 6 | 0 | 4 | 4 | 6 | 18 | 6760 | 120 | 67 | 6986 |
| Human_HTO_12 | 1 | 0 | 0 | 2053 | 4 | 6 | 14 | 5 | 62 | 222 | 2367 |
| Human_HTO_13 | 0 | 869 | 1 | 4 | 2 | 2 | 4 | 4 | 45 | 143 | 1074 |
| Human_HTO_14 | 2 | 2 | 0 | 9 | 2 | 7 | 7197 | 6 | 85 | 82 | 7392 |
| Human_HTO_15 | 1 | 8 | 6466 | 17 | 1 | 10 | 25 | 3 | 167 | 133 | 6831 |
| Human_HTO_6 | 3 | 2 | 2 | 11 | 4473 | 13 | 24 | 10 | 84 | 92 | 4714 |
| Human_HTO_7 | 1356 | 6 | 2 | 5 | 2 | 7 | 13 | 1 | 62 | 293 | 1747 |
| Human_HTO_9 | 0 | 2 | 2 | 6 | 2 | 2246 | 18 | 2 | 37 | 20 | 2335 |
| Doublet | 216 | 159 | 1328 | 415 | 928 | 493 | 1813 | 1787 | 5238 | 247 | 12624 |
| Negative | 120 | 229 | 119 | 508 | 219 | 275 | 1151 | 324 | 563 | 1090 | 4598 |
| Total | 1700 | 1283 | 7920 | 3032 | 5637 | 3065 | 10277 | 8902 | 6463 | 2389 | 50668 |
Save data
saveRDS(
sce,
here("data",
"C133_Neeland_batch5",
"data",
"SCEs",
"C133_Neeland_batch5.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