Demultiplexing the C133_Neeland_batch6 data set
Peter Hickey and Jovana Maksimovic
2024-02-26
Last updated: 2024-02-26
Checks: 7 0
Knit directory: paed-inflammation-CITEseq/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20240216) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 025ceae. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Untracked files:
Untracked: .DS_Store
Untracked: analysis/02.0_quality_control.Rmd
Untracked: analysis/03.0_call_doublets.Rmd
Untracked: code/dropletutils.R
Untracked: code/utility.R
Untracked: data/.DS_Store
Untracked: data/C133_Neeland_batch0/
Untracked: data/C133_Neeland_batch1/
Untracked: data/C133_Neeland_batch2/
Untracked: data/C133_Neeland_batch3/
Untracked: data/C133_Neeland_batch4/
Untracked: data/C133_Neeland_batch5/
Untracked: data/C133_Neeland_batch6/
Untracked: renv.lock
Untracked: renv/
Unstaged changes:
Modified: .Rprofile
Modified: .gitignore
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown
(analysis/01.6_preprocess_batch6.Rmd) and HTML
(docs/01.6_preprocess_batch6.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 025ceae | Jovana Maksimovic | 2024-02-26 | wflow_publish(c("analysis/index.Rmd", "analysis/01*")) |
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_batch6/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$Batch == 6, ])| Donor | Sample name | Disease | Age | Sex | Batch | HASHTAG ID | DATE OF CAPTURE |
|---|---|---|---|---|---|---|---|
| 36 | 36 | True control | 1.2246575 | M | 6 | Human_HTO_6 | 2021-09-10 |
| 37 | 37.1 | CF | 0.5232877 | F | 6 | Human_HTO_7 | 2021-09-10 |
| 37 | 37.200000000000003 | CF | 1.0657534 | F | 6 | Human_HTO_9 | 2021-09-10 |
| 37 | 37.299999999999997 | CF | 2.0575342 | F | 6 | Human_HTO_10 | 2021-09-10 |
| 38 | 38.1 | CF | 1.0575342 | M | 6 | Human_HTO_12 | 2021-09-10 |
| 38 | 38.200000000000003 | CF | 1.9917808 | M | 6 | Human_HTO_13 | 2021-09-10 |
| 39 | 39.1 | CF | 0.9616438 | F | 6 | Human_HTO_14 | 2021-09-10 |
| 39 | 39.200000000000003 | CF | 2.2602740 | F | 6 | Human_HTO_15 | 2021-09-10 |
Setting up the data
sce <- readRDS(here("data", "C133_Neeland_batch6",
"data", "SCEs", "C133_Neeland_batch6.CellRanger.SCE.rds"))
sce$Capture <- factor(sce$Sample)
capture_names <- levels(sce$Capture)
capture_names <- setNames(capture_names, capture_names)
sce$Sample <- NULL
sceclass: SingleCellExperiment
dim: 36601 6940875
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(3): ID Symbol Type
colnames(6940875): 1_AAACCCAAGAAACACT-1 1_AAACCCAAGAAACCAT-1 ...
2_TTTGTTGTCTTTGGAG-1 2_TTTGTTGTCTTTGGCT-1
colData names(2): Barcode Capture
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(1): Antibody CaptureCalling cells from empty droplets
par(mfrow = c(1, 2))
lapply(capture_names, function(cn) {
sce <- sce[, sce$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)$knee, col = "dodgerblue", lty = 2)
})
Total UMI count for each barcode in the dataset, plotted against its rank (in decreasing order of total counts). The inferred locations of the inflection (dark green dashed lines) and knee points (blue dashed lines) are also shown.
Remove empty droplets.
empties <- do.call(rbind, lapply(capture_names, function(cn) {
message(cn)
empties <- readRDS(
here("data",
"C133_Neeland_batch6",
"data",
"emptyDrops", paste0(cn, ".emptyDrops.rds")))
empties$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_batch6_1 | 25740 |
| C133_batch6_2 | 25379 |
sce <- sce[, which(empties$FDR <= 0.001)]
sceclass: SingleCellExperiment
dim: 36601 51119
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(3): ID Symbol Type
colnames(51119): 1_AAACCCAAGAAGCGCT-1 1_AAACCCAAGACTCATC-1 ...
2_TTTGTTGTCGAGAATA-1 2_TTTGTTGTCTACTGAG-1
colData names(2): Barcode Capture
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(1): Antibody CaptureAdding per cell quality control information
sce <- scuttle::addPerCellQC(sce)
head(colData(sce)) %>%
data.frame %>%
knitr::kable()| Barcode | Capture | sum | detected | altexps_Antibody.Capture_sum | altexps_Antibody.Capture_detected | altexps_Antibody.Capture_percent | total | |
|---|---|---|---|---|---|---|---|---|
| 1_AAACCCAAGAAGCGCT-1 | AAACCCAAGAAGCGCT-1 | C133_batch6_1 | 2314 | 1100 | 4141 | 165 | 64.15182 | 6455 |
| 1_AAACCCAAGACTCATC-1 | AAACCCAAGACTCATC-1 | C133_batch6_1 | 4921 | 1853 | 2357 | 165 | 32.38527 | 7278 |
| 1_AAACCCAAGCAGCCCT-1 | AAACCCAAGCAGCCCT-1 | C133_batch6_1 | 959 | 529 | 1919 | 163 | 66.67825 | 2878 |
| 1_AAACCCAAGCCTATCA-1 | AAACCCAAGCCTATCA-1 | C133_batch6_1 | 5476 | 2125 | 2494 | 165 | 31.29235 | 7970 |
| 1_AAACCCAAGTCCCGAC-1 | AAACCCAAGTCCCGAC-1 | C133_batch6_1 | 3422 | 1496 | 2522 | 163 | 42.42934 | 5944 |
| 1_AAACCCAAGTGGACGT-1 | AAACCCAAGTGGACGT-1 | C133_batch6_1 | 5046 | 1704 | 13503 | 160 | 72.79638 | 18549 |
Demultiplexing with hashtag oligos (HTOs)
is_adt <- grepl("^A[0-9]+", rownames(altExp(sce, "Antibody Capture")))
is_hto <- grepl("^Human_HTO", rownames(altExp(sce, "Antibody Capture")))
altExp(sce, "HTO") <- altExp(sce, "Antibody Capture")[is_hto, ]
altExp(sce, "ADT") <- altExp(sce, "Antibody Capture")[is_adt, ]
altExp(sce, "Antibody Capture") <- NULL
hto_counts <- counts(altExp(sce, "HTO"))
xmax <- ceiling(max(log2(hto_counts + 1)))C133_batch6_1
par(mfrow = c(3, 3))
lapply(rownames(hto_counts), function(i) {
hist(
log2(hto_counts[i, sce$Capture == "C133_batch6_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$Capture == "C133_batch6_1"], "HTO")))
detected <- sce$detected[sce$Capture == "C133_batch6_1"]
df <- data.frame(t(hto),
detected = detected,
hto = colSums(hto))
df %>%
pivot_longer(cols = starts_with("Human_HTO")) %>%
mutate(logged = log(value + 1)) %>%
ggplot(aes(x = logged)) +
geom_density(adjust = 5) +
facet_wrap(~name, scales = "free")
df %>%
pivot_longer(cols = starts_with("Human_HTO")) %>%
ggplot(aes(x = detected, y = hto)) +
geom_density_2d() +
facet_wrap(~name) 
Run demultiplexing.
dmm <- demuxmix(hto = hto,
rna = detected,
model = "naive")
summary(dmm) Class NumObs RelFreq MedProb ExpFPs FDR
1 Human_HTO_10 2256 0.08974461 0.8491406 422.3817 0.1872259
2 Human_HTO_12 2689 0.10696953 0.9033467 376.1969 0.1399022
3 Human_HTO_13 2697 0.10728777 0.8725124 448.2888 0.1662176
4 Human_HTO_14 1538 0.06118227 0.8878061 241.1825 0.1568157
5 Human_HTO_15 1868 0.07430981 0.8489714 345.8841 0.1851628
6 Human_HTO_6 3416 0.13588989 0.8535141 626.0646 0.1832742
7 Human_HTO_7 762 0.03031267 0.8435471 147.4005 0.1934390
8 Human_HTO_9 711 0.02828387 0.8486184 135.1278 0.1900531
9 singlet 15937 0.63398043 0.8625798 2742.5269 0.1720855
10 multiplet 7620 0.30312674 0.8438312 1589.2969 0.2085691
11 negative 1581 0.06289283 0.7857089 406.2098 0.2569322
12 uncertain 602 NA NA NA NAExamine results.
p <- vector("list", nrow(hto))
for(i in 1:nrow(hto)){
p[[i]] <- plotDmmHistogram(dmm, hto = i) +
coord_cartesian(ylim = c(0, 0.001),
xlim = c(-50, 1000)) +
theme(axis.title = element_text(size = 8),
axis.text = element_text(size = 6))
}
wrap_plots(p , ncol = 3) 
p <- vector("list", nrow(hto))
for(i in 1:nrow(hto)){
p[[i]] <- plotDmmPosteriorP(dmm, hto = i) +
theme(axis.title = element_text(size = 8),
axis.text = element_text(size = 6))
}
wrap_plots(p , ncol = 3) 
pAcpt(dmm) <- 0
classes1 <- dmmClassify(dmm)
classes1$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
8049 2277 2709 2715 1550 1882
Human_HTO_6 Human_HTO_7 Human_HTO_9 Negative
3439 765 717 1637 C133_batch6_2
par(mfrow = c(3, 3))
lapply(rownames(hto_counts), function(i) {
hist(
log2(hto_counts[i, sce$Capture == "C133_batch6_2"] + 1),
xlab = "log2(UMIs + 1)",
main = paste0("C133_2: ", i),
xlim = c(0, xmax),
breaks = seq(0, xmax, 0.5),
cex.main = 0.8)
})
Number of UMIs for each HTO across all non-empty droplets.
Prepare the data.
hto <- as.matrix(counts(altExp(sce[, sce$Capture == "C133_batch6_2"], "HTO")))
detected <- sce$detected[sce$Capture == "C133_batch6_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 2354 0.09452676 0.8940317 331.3091 0.1407430
2 Human_HTO_12 2735 0.10982613 0.9088403 352.7269 0.1289678
3 Human_HTO_13 3049 0.12243505 0.9189852 368.3930 0.1208242
4 Human_HTO_14 1743 0.06999157 0.9339143 186.1802 0.1068159
5 Human_HTO_15 2045 0.08211862 0.8977971 287.0439 0.1403638
6 Human_HTO_6 3610 0.14496245 0.8974667 509.5314 0.1411444
7 Human_HTO_7 874 0.03509617 0.8850167 131.3866 0.1503279
8 Human_HTO_9 832 0.03340963 0.8892994 125.4294 0.1507564
9 singlet 17242 0.69236638 0.9035263 2292.0006 0.1329312
10 multiplet 6089 0.24450869 0.8823802 1086.3614 0.1784138
11 negative 1572 0.06312492 0.8097461 360.9998 0.2296437
12 uncertain 476 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$dmmHTO)
Doublet Human_HTO_10 Human_HTO_12 Human_HTO_13 Human_HTO_14 Human_HTO_15
6450 2362 2747 3063 1748 2057
Human_HTO_6 Human_HTO_7 Human_HTO_9 Negative
3625 877 838 1612 Save HTO assignments
classes <- rbind(classes1, classes2)
all(rownames(classes) == colnames(sce))[1] TRUEsce$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_batch6",
"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", 0:3)
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_batch6_1: ", rownames(z[[k]])),
labels_col = paste0("C133_batch6_", 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$GeneticDonor <- LETTERS[seq_along(donor_names)]
best_match_df <- dplyr::select(best_match_df, GeneticDonor, everything())
knitr::kable(
best_match_df,
caption = "Best match of donors between the scRNA-seq captures.")| GeneticDonor | C133_batch6_1 | C133_batch6_2 |
|---|---|---|
| A | donor0 | donor1 |
| B | donor1 | donor3 |
| C | donor2 | donor0 |
| D | donor3 | donor2 |
Assigning barcodes to donors
vireo_df <- do.call(
rbind,
c(
lapply(capture_names, function(cn) {
# Read data
vireo_df <- read.table(
here("data",
"C133_Neeland_batch6",
"data",
"vireo", cn, "donor_ids.tsv"),
header = TRUE)
# Replace `donor[0-9]+` with `donor_[A-Z]` using `best_match_df`.
best_match <- setNames(
c(best_match_df[["GeneticDonor"]], "Doublet", "Unknown"),
c(best_match_df[[cn]], "doublet", "unassigned"))
vireo_df$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_batch6_", "", 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 | Doublet | Unknown | Total |
|---|---|---|---|---|---|---|---|
| Human_HTO_10 | 4505 | 8 | 4 | 14 | 59 | 49 | 4639 |
| Human_HTO_12 | 8 | 25 | 11 | 5235 | 33 | 144 | 5456 |
| Human_HTO_13 | 8 | 11 | 3 | 5645 | 46 | 65 | 5778 |
| Human_HTO_14 | 5 | 3170 | 6 | 18 | 47 | 52 | 3298 |
| Human_HTO_15 | 3 | 3749 | 3 | 17 | 59 | 108 | 3939 |
| Human_HTO_6 | 5 | 11 | 6777 | 24 | 134 | 113 | 7064 |
| Human_HTO_7 | 1580 | 2 | 5 | 14 | 20 | 21 | 1642 |
| Human_HTO_9 | 1511 | 1 | 1 | 3 | 19 | 20 | 1555 |
| Doublet | 2219 | 1772 | 1526 | 2924 | 5901 | 157 | 14499 |
| Negative | 269 | 479 | 222 | 1357 | 298 | 624 | 3249 |
| Total | 10113 | 9228 | 8558 | 15251 | 6616 | 1353 | 51119 |
Save data
saveRDS(
sce,
here("data",
"C133_Neeland_batch6",
"data",
"SCEs",
"C133_Neeland_batch6.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