Preprocessing of Batches: Adenoids_Batch3
Quality Control: Removing ambient RNA, low lib reads, high mito content and doublets
George Howitt, Gunjan Dixit and Jovana Maksimovic
May 02, 2024
Last updated: 2024-05-02
Checks: 6 1
Knit directory: paed-airway-allTissues/
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Introduction
This RMarkdown performs quality control for the earlyAIR batch- Adenoids- Batch3
The steps are: * Load CellRanger counts * Run decontX to determine contamination and correct * Filter cells with low library size and high mitochondrial counts * Identify doublets * Scale, Normalize, Run PCA, UMAP, Azimuth annotation before/after doublet removal * Save Seurat object
suppressPackageStartupMessages({
library(BiocStyle)
library(BiocParallel)
library(tidyverse)
library(here)
library(glue)
library(scran)
library(scater)
library(scuttle)
library(janitor)
library(cowplot)
library(patchwork)
library(scales)
library(Homo.sapiens)
library(msigdbr)
library(EnsDb.Hsapiens.v86)
library(ensembldb)
library(readr)
library(Seurat)
library(celda)
library(decontX)
library(Azimuth)
library(Matrix)
library(scDblFinder)
library(scMerge)
library(googlesheets4)
library(lubridate)
library(ggstats)
})
set.seed(42)Get Batch_info
batch_path <- here("output/RDS/AllBatches_filtered_SCEs/G000231_batch3_Adenoids.CellRanger_filtered.SCE.rds")
batch_info <- str_match(basename(batch_path), "^(G\\d+_batch\\d+)_([A-Za-z_]+)\\.CellRanger_filtered\\.SCE\\.rds$")
batch_name <- batch_info[, 2]
tissue <- batch_info[, 3]sce <- readRDS(batch_path)
sce$tissue <- tissue
sce$batch_name <- batch_name
sceclass: SingleCellExperiment
dim: 18082 96941
metadata(0):
assays(2): counts logcounts
rownames(18082): SAMD11 NOC2L ... MT-ND6 MT-CYB
rowData names(0):
colnames(96941): AAACAAGCAACTTCGTACTTTAGG-1 AAACAAGCACTTTACAACTTTAGG-1
... TTTGTGAGTTGAATCCATTCGGTT-1 TTTGTGAGTTGGCCACATTCGGTT-1
colData names(7): orig.ident nCount_RNA ... tissue batch_name
reducedDimNames(0):
mainExpName: RNA
altExpNames(0):CellRanger calls
Filter cells with zero counts across all genes
sce <- sce[rowSums(counts(sce)) > 0, ]
sceclass: SingleCellExperiment
dim: 17244 96941
metadata(0):
assays(2): counts logcounts
rownames(17244): SAMD11 NOC2L ... MT-ND6 MT-CYB
rowData names(0):
colnames(96941): AAACAAGCAACTTCGTACTTTAGG-1 AAACAAGCACTTTACAACTTTAGG-1
... TTTGTGAGTTGAATCCATTCGGTT-1 TTTGTGAGTTGGCCACATTCGGTT-1
colData names(7): orig.ident nCount_RNA ... tissue batch_name
reducedDimNames(0):
mainExpName: RNA
altExpNames(0):cell_counts <- c()
cell_counts["Post CellRanger Filtering"] <- ncol(sce)Add Barcode metadata
The first 17 characters of the barcodes are the GEM barcode and the last 9 characters are the sample barcode. Create a metadata feature for each of these.
sce$Barcode <- unname(substring(colnames(sce), first = 1, last = 26))
sce$GEM_barcode <- substring(sce$Barcode, first = 1, last = 17)
sce$sample_barcode <- substring(sce$Barcode, first = 18, last = 26)Pre-processing
DecontX
Correcting for ambient RNA with decontX, actually replacing the raw counts with the decontX counts. These can be forced to be integers rather than doubles later if necessary, but so far it doesn’t seem to be an issue.
sce <- decontX(sce)--------------------------------------------------Starting DecontX--------------------------------------------------Thu May 2 13:57:48 2024 .. Analyzing all cellsThu May 2 13:57:49 2024 .... Generating UMAP and estimating cell typesFound more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Thu May 2 14:00:23 2024 .... Estimating contaminationThu May 2 14:00:35 2024 ...... Completed iteration: 10 | converge: 0.03438Thu May 2 14:00:46 2024 ...... Completed iteration: 20 | converge: 0.01265Thu May 2 14:00:56 2024 ...... Completed iteration: 30 | converge: 0.006517Thu May 2 14:01:08 2024 ...... Completed iteration: 40 | converge: 0.004471Thu May 2 14:01:18 2024 ...... Completed iteration: 50 | converge: 0.003294Thu May 2 14:01:29 2024 ...... Completed iteration: 60 | converge: 0.002235Thu May 2 14:01:39 2024 ...... Completed iteration: 70 | converge: 0.001918Thu May 2 14:01:50 2024 ...... Completed iteration: 80 | converge: 0.002093Thu May 2 14:02:00 2024 ...... Completed iteration: 90 | converge: 0.001582Thu May 2 14:02:11 2024 ...... Completed iteration: 100 | converge: 0.001374Thu May 2 14:02:21 2024 ...... Completed iteration: 110 | converge: 0.001152Thu May 2 14:02:25 2024 ...... Completed iteration: 113 | converge: 0.0009538Thu May 2 14:02:25 2024 .. Calculating final decontaminated matrix--------------------------------------------------Completed DecontX. Total time: 4.817199 mins--------------------------------------------------assay(sce, "raw_counts") <- counts(sce)
counts(sce) <- decontXcounts(sce)Filter on library size filter after running decontX
sce <- addPerCellQCMetrics(sce)
sum(sce$sum < 250)[1] 274sce <- sce[, sce$sum >= 250]
cell_counts["Post low-lib Filtering"] <- ncol(sce)Mitochondrial filtering
Filtering out cells with high mitochondrial content.
is.mito <- grepl(pattern = "^MT", rownames(sce))
sce <- addPerCellQCMetrics(sce, subsets = list(mito = is.mito))
mito_outliers <- isOutlier(sce$subsets_mito_percent, type = "higher")
sum(mito_outliers)[1] 4137sce <- sce[, !mito_outliers]
cell_counts["Post Mito Filtering"] <- ncol(sce)Multiplet filtering
We know that there will be some unidentified multiplets in our data, as higher-occupancy GEMs have many ways to include multiple cells from the same samples. Still working on a way to estimate the number of these but the existing doublet-finding tools work ok. Using scDblFinder as that seemed to have the best effect on the GEM-level counts.
sce <- logNormCounts(sce) %>%
runPCA() %>%
runUMAP()Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Run scDblFinder
bp <- MulticoreParam(8, RNGseed=56213)
sce <- scDblFinder(sce, clusters = T,
BPPARAM=bp)Clustering cells...14 clustersCreating ~25000 artificial doublets...Dimensional reductionEvaluating kNN...Training model...iter=0, 32808 cells excluded from training.iter=1, 32938 cells excluded from training.iter=2, 32941 cells excluded from training.Threshold found:0.05633997 (36.7%) doublets calledtable(sce$scDblFinder.class)
singlet doublet
58533 33997 Make Seurat object
seu <- CreateSeuratObject(counts(sce), meta.data = as.data.frame(colData(sce)))Add GEM metadata to the cell-level objects
seu$cells_per_GEM <- table(seu$GEM_barcode)[seu$GEM_barcode]table(seu$cells_per_GEM)
1 2 3 4 5 6
20889 31596 23226 11324 4205 1290 Normalization and Azimuth annotation
seu <- NormalizeData(seu, verbose = F) %>%
FindVariableFeatures(nfeatures = 2000, verbose = F) %>%
ScaleData(verbose = F) %>%
RunPCA(dims = 1:30, verbose = F) %>%
RunUMAP(dims = 1:30, verbose = F) Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'options(timeout = max(1000000, getOption("timeout")))
tmp <- RunAzimuth(seu, reference = "tonsilref") detected inputs from HUMAN with id type Gene.namereference rownames detected HUMAN with id type Gene.nameNormalizing query using reference SCT modelProjecting cell embeddingsFinding query neighborsFinding neighborhoodsFinding anchors Found 36201 anchorsFinding integration vectorsFinding integration vector weightsPredicting cell labels
Predicting cell labels
Integrating dataset 2 with reference datasetFinding integration vectorsIntegrating dataComputing nearest neighborsRunning UMAP projection14:16:05 Read 92530 rows14:16:05 Processing block 1 of 114:16:05 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 20
14:16:05 Initializing by weighted average of neighbor coordinates using 1 thread
14:16:05 Commencing optimization for 67 epochs, with 1850600 positive edges
14:16:12 Finished
Projecting reference PCA onto query
Finding integration vector weights
Projecting back the query cells into original PCA space
Finding integration vector weights
Computing scores:
Finding neighbors of original query cells
Finding neighbors of transformed query cells
Computing query SNN
Determining bandwidth and computing transition probabilities
Total elapsed time: 44.5796339511871seu@meta.data <- tmp@meta.dataAdd Batch specific meta data
f <- c("https://docs.google.com/spreadsheets/d/1FKo-7MweuFDoKBm8DMFcMOuq0LyK_K6GVNAAo_n-ItE/edit#gid=1882418352")
dat <- bind_rows(lapply(1:10, function(sheet) read_sheet(ss = f, sheet = sheet)))
dat# A tibble: 153 × 12
sample_id probe_barcode_id sample_oligo expected cell number …¹ `sample type`
<chr> <chr> <chr> <dbl> <chr>
1 eAIR004 BC001 CTTTAGG-1 8000 nasal brushi…
2 eAIR005 BC002 ACGGGAA-1 1789 nasal brushi…
3 eAIR006 BC003 GTAGGCT-1 8000 nasal brushi…
4 eAIR007 BC004 TGTTGAC-1 5368 nasal brushi…
5 eAIR011 BC005 CAGACCT-1 8000 nasal brushi…
6 eAIR013 BC006 TCCCAAC-1 8000 nasal brushi…
7 eAIR016 BC007 AGTAGAG-1 8000 nasal brushi…
8 eAIR017 BC008 GCTGTGA-1 8000 nasal brushi…
9 eAIR019 BC009 CAGTCTG-1 8000 nasal brushi…
10 eAIR020 BC010 GTGAGTG-1 8000 nasal brushi…
# ℹ 143 more rows
# ℹ abbreviated name: ¹`expected cell number recovered`
# ℹ 7 more variables: patient <chr>, sex <chr>, age_years <dbl>, batch <dbl>,
# viability <dbl>, `second pool` <dbl>, run <chr>batch_meta <- dat %>%
dplyr::filter(run == "batch3_1")
#batch_meta$sample_id <- gsub("_", "-", batch_meta$sample_id) #For Batch7
seu$sex <- sapply(seu$Sample, function(x) batch_meta$sex[batch_meta$sample_id == x])
seu$age_years <- sapply(seu$Sample, function(x) batch_meta$age_years[batch_meta$sample_id == x])Clean up no longer-useful metadata
seu@meta.data <- seu@meta.data %>%
dplyr::select(c(Sample, age_years, sex, nCount_RNA, nFeature_RNA,
Barcode, GEM_barcode, sample_barcode,
tissue, batch_name,
cells_per_GEM,
scDblFinder.class, scDblFinder.score,
predicted.celltype.l1,predicted.celltype.l2, predicted.celltype.l1.score,predicted.celltype.l2.score))Save pre-processed objects
out <- here("output",
"RDS", "AllBatches_Azimuth_SEUs",
paste0(batch_name, "_", tissue, ".CellRanger.decontX.mito.filter.Azimuth.SEU.rds"))
saveRDS(seu, file = out)Filter doublets and repeat
seu <- seu[, seu$scDblFinder.class == "singlet"]
cell_counts["Post Doublet Filtering"] <- ncol(sce)Normalization and Azimuth annotation
seu <- NormalizeData(seu, verbose = F) %>%
FindVariableFeatures(nfeatures = 2000, verbose = F) %>%
ScaleData(verbose = F) %>%
RunPCA(dims = 1:30, verbose = F) %>%
RunUMAP(dims = 1:30, verbose = F) Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'options(timeout = max(1000000, getOption("timeout")))
tmp <- RunAzimuth(seu, reference = "tonsilref") detected inputs from HUMAN with id type Gene.namereference rownames detected HUMAN with id type Gene.nameNormalizing query using reference SCT modelProjecting cell embeddingsFinding query neighborsFinding neighborhoodsFinding anchors Found 29164 anchorsFinding integration vectorsFinding integration vector weightsPredicting cell labels
Predicting cell labels
Integrating dataset 2 with reference datasetFinding integration vectorsIntegrating dataComputing nearest neighborsRunning UMAP projection14:26:22 Read 58533 rows14:26:22 Processing block 1 of 114:26:22 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 20
14:26:23 Initializing by weighted average of neighbor coordinates using 1 thread
14:26:23 Commencing optimization for 67 epochs, with 1170660 positive edges
14:26:27 Finished
Projecting reference PCA onto query
Finding integration vector weights
Projecting back the query cells into original PCA space
Finding integration vector weights
Computing scores:
Finding neighbors of original query cells
Finding neighbors of transformed query cells
Computing query SNN
Determining bandwidth and computing transition probabilities
Total elapsed time: 27.1543278694153seu@meta.data <- tmp@meta.datathis figure shows number of cells eliminated at each filtering stage-
counts_df <- data.frame(
Stage = factor(names(cell_counts), levels = c("Post CellRanger Filtering", "Post low-lib Filtering","Post Mito Filtering", "Post Doublet Filtering")),
Cell_Count = as.numeric(cell_counts)
)
a <- ggplot(counts_df, aes(x = Stage, y = Cell_Count, group = 1)) +
geom_line() +
geom_point() +
theme_minimal() +
labs(title = paste0(tissue, " ", batch_name, " :Cell Counts After Each Preprocessing Step"))
#ggsave(a, file=paste0(tissue, " ", batch_name, " :Cells_after_filtering.pdf"), width = 10)
a
Save pre-processed objects
out <- here("output",
"RDS", "AllBatches_Azimuth_noDoublets_SEUs",
paste0(batch_name, "_", tissue, ".CellRanger.decontX.mito.doublet.filter.Azimuth.SEU.rds"))
saveRDS(seu, file = out)Session Info
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.3.2 (2023-10-31)
os macOS Sonoma 14.4.1
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Australia/Melbourne
date 2024-05-02
pandoc 3.1.1 @ /Users/dixitgunjan/Desktop/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
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fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.0)
fastmatch 1.1-4 2023-08-18 [1] CRAN (R 4.3.0)
filelock 1.0.3 2023-12-11 [1] CRAN (R 4.3.1)
fitdistrplus 1.1-11 2023-04-25 [1] CRAN (R 4.3.0)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.3.0)
foreach 1.5.2 2022-02-02 [1] CRAN (R 4.3.0)
foreign 0.8-86 2023-11-28 [1] CRAN (R 4.3.1)
Formula 1.2-5 2023-02-24 [1] CRAN (R 4.3.0)
fs 1.6.3 2023-07-20 [1] CRAN (R 4.3.0)
future 1.33.1 2023-12-22 [1] CRAN (R 4.3.1)
future.apply 1.11.1 2023-12-21 [1] CRAN (R 4.3.1)
gargle 1.5.2 2023-07-20 [1] CRAN (R 4.3.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.0)
GenomeInfoDb * 1.38.6 2024-02-10 [1] Bioconductor 3.18 (R 4.3.2)
GenomeInfoDbData 1.2.11 2024-02-27 [1] Bioconductor
GenomicAlignments 1.38.2 2024-01-20 [1] Bioconductor 3.18 (R 4.3.2)
GenomicFeatures * 1.54.3 2024-02-03 [1] Bioconductor 3.18 (R 4.3.2)
GenomicRanges * 1.54.1 2023-10-30 [1] Bioconductor
getPass 0.2-4 2023-12-10 [1] CRAN (R 4.3.1)
ggbeeswarm 0.7.2 2023-04-29 [1] CRAN (R 4.3.0)
ggplot2 * 3.5.0 2024-02-23 [1] CRAN (R 4.3.1)
ggrepel 0.9.5 2024-01-10 [1] CRAN (R 4.3.1)
ggridges 0.5.6 2024-01-23 [1] CRAN (R 4.3.1)
ggstats * 0.5.1 2023-11-21 [1] CRAN (R 4.3.1)
git2r 0.33.0 2023-11-26 [1] CRAN (R 4.3.1)
globals 0.16.2 2022-11-21 [1] CRAN (R 4.3.0)
glue * 1.7.0 2024-01-09 [1] CRAN (R 4.3.1)
GO.db * 3.18.0 2024-02-27 [1] Bioconductor
goftest 1.2-3 2021-10-07 [1] CRAN (R 4.3.0)
googledrive 2.1.1 2023-06-11 [1] CRAN (R 4.3.0)
googlesheets4 * 1.1.1 2023-06-11 [1] CRAN (R 4.3.0)
gplots 3.1.3.1 2024-02-02 [1] CRAN (R 4.3.1)
graph 1.80.0 2023-10-26 [1] Bioconductor
gridExtra 2.3 2017-09-09 [1] CRAN (R 4.3.0)
gtable 0.3.4 2023-08-21 [1] CRAN (R 4.3.0)
gtools 3.9.5 2023-11-20 [1] CRAN (R 4.3.1)
hdf5r 1.3.9 2024-01-14 [1] CRAN (R 4.3.1)
here * 1.0.1 2020-12-13 [1] CRAN (R 4.3.0)
highr 0.10 2022-12-22 [1] CRAN (R 4.3.0)
Hmisc 5.1-1 2023-09-12 [1] CRAN (R 4.3.0)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.3.0)
Homo.sapiens * 1.3.1 2024-02-27 [1] Bioconductor
htmlTable 2.4.2 2023-10-29 [1] CRAN (R 4.3.1)
htmltools 0.5.7 2023-11-03 [1] CRAN (R 4.3.1)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.3.1)
httpuv 1.6.14 2024-01-26 [1] CRAN (R 4.3.1)
httr 1.4.7 2023-08-15 [1] CRAN (R 4.3.0)
ica 1.0-3 2022-07-08 [1] CRAN (R 4.3.0)
igraph 2.0.2 2024-02-17 [1] CRAN (R 4.3.1)
inline 0.3.19 2021-05-31 [1] CRAN (R 4.3.0)
IRanges * 2.36.0 2023-10-26 [1] Bioconductor
irlba 2.3.5.1 2022-10-03 [1] CRAN (R 4.3.2)
iterators 1.0.14 2022-02-05 [1] CRAN (R 4.3.0)
janitor * 2.2.0 2023-02-02 [1] CRAN (R 4.3.0)
JASPAR2020 0.99.10 2024-02-27 [1] Bioconductor
jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.3.0)
jsonlite 1.8.8 2023-12-04 [1] CRAN (R 4.3.1)
KEGGREST 1.42.0 2023-10-26 [1] Bioconductor
KernSmooth 2.23-22 2023-07-10 [1] CRAN (R 4.3.2)
knitr 1.45 2023-10-30 [1] CRAN (R 4.3.1)
labeling 0.4.3 2023-08-29 [1] CRAN (R 4.3.0)
later 1.3.2 2023-12-06 [1] CRAN (R 4.3.1)
lattice 0.22-5 2023-10-24 [1] CRAN (R 4.3.1)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.3.0)
leiden 0.4.3.1 2023-11-17 [1] CRAN (R 4.3.1)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.1)
limma 3.58.1 2023-11-02 [1] Bioconductor
listenv 0.9.1 2024-01-29 [1] CRAN (R 4.3.1)
lmtest 0.9-40 2022-03-21 [1] CRAN (R 4.3.0)
locfit 1.5-9.8 2023-06-11 [1] CRAN (R 4.3.0)
loo 2.7.0 2024-02-24 [1] CRAN (R 4.3.1)
lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.3.1)
lungref.SeuratData 2.0.0 2024-02-29 [1] local
M3Drop 1.28.0 2023-10-26 [1] Bioconductor
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.0)
MASS 7.3-60.0.1 2024-01-13 [1] CRAN (R 4.3.1)
Matrix * 1.6-5 2024-01-11 [1] CRAN (R 4.3.1)
MatrixGenerics * 1.14.0 2023-10-26 [1] Bioconductor
matrixStats * 1.2.0 2023-12-11 [1] CRAN (R 4.3.1)
MCMCprecision 0.4.0 2019-12-05 [1] CRAN (R 4.3.0)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.3.0)
metapod 1.10.1 2023-12-23 [1] Bioconductor 3.18 (R 4.3.2)
mgcv 1.9-1 2023-12-21 [1] CRAN (R 4.3.1)
mime 0.12 2021-09-28 [1] CRAN (R 4.3.0)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.3.0)
msigdbr * 7.5.1 2022-03-30 [1] CRAN (R 4.3.0)
munsell 0.5.0 2018-06-12 [1] CRAN (R 4.3.0)
mvtnorm 1.2-4 2023-11-27 [1] CRAN (R 4.3.1)
nlme 3.1-164 2023-11-27 [1] CRAN (R 4.3.1)
nnet 7.3-19 2023-05-03 [1] CRAN (R 4.3.2)
numDeriv 2016.8-1.1 2019-06-06 [1] CRAN (R 4.3.0)
openssl 2.1.1 2023-09-25 [1] CRAN (R 4.3.1)
org.Hs.eg.db * 3.18.0 2024-02-27 [1] Bioconductor
OrganismDbi * 1.44.0 2023-10-26 [1] Bioconductor
parallelly 1.37.0 2024-02-14 [1] CRAN (R 4.3.1)
patchwork * 1.2.0 2024-01-08 [1] CRAN (R 4.3.1)
pbapply 1.7-2 2023-06-27 [1] CRAN (R 4.3.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.0)
pkgbuild 1.4.3 2023-12-10 [1] CRAN (R 4.3.1)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.0)
plotly 4.10.4 2024-01-13 [1] CRAN (R 4.3.1)
plyr 1.8.9 2023-10-02 [1] CRAN (R 4.3.1)
png 0.1-8 2022-11-29 [1] CRAN (R 4.3.0)
polyclip 1.10-6 2023-09-27 [1] CRAN (R 4.3.1)
poweRlaw 0.80.0 2024-01-25 [1] CRAN (R 4.3.1)
pracma 2.4.4 2023-11-10 [1] CRAN (R 4.3.1)
presto 1.0.0 2024-02-27 [1] Github (immunogenomics/presto@31dc97f)
prettyunits 1.2.0 2023-09-24 [1] CRAN (R 4.3.1)
processx 3.8.3 2023-12-10 [1] CRAN (R 4.3.1)
progress 1.2.3 2023-12-06 [1] CRAN (R 4.3.1)
progressr 0.14.0 2023-08-10 [1] CRAN (R 4.3.0)
promises 1.2.1 2023-08-10 [1] CRAN (R 4.3.0)
ProtGenerics 1.34.0 2023-10-26 [1] Bioconductor
proxyC 0.3.4 2023-10-25 [1] CRAN (R 4.3.1)
ps 1.7.6 2024-01-18 [1] CRAN (R 4.3.1)
purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.3.0)
QuickJSR 1.1.3 2024-01-31 [1] CRAN (R 4.3.1)
R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.3.0)
R.oo 1.26.0 2024-01-24 [1] CRAN (R 4.3.1)
R.utils 2.12.3 2023-11-18 [1] CRAN (R 4.3.1)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.0)
RANN 2.6.1 2019-01-08 [1] CRAN (R 4.3.0)
rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.3.0)
RBGL 1.78.0 2023-10-26 [1] Bioconductor
RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.3.0)
Rcpp 1.0.12 2024-01-09 [1] CRAN (R 4.3.1)
RcppAnnoy 0.0.22 2024-01-23 [1] CRAN (R 4.3.1)
RcppEigen 0.3.3.9.4 2023-11-02 [1] CRAN (R 4.3.1)
RcppHNSW 0.6.0 2024-02-04 [1] CRAN (R 4.3.1)
RcppParallel 5.1.7 2023-02-27 [1] CRAN (R 4.3.0)
RcppRoll 0.3.0 2018-06-05 [1] CRAN (R 4.3.0)
RCurl 1.98-1.14 2024-01-09 [1] CRAN (R 4.3.1)
readr * 2.1.5 2024-01-10 [1] CRAN (R 4.3.1)
reldist 1.7-2 2023-02-17 [1] CRAN (R 4.3.0)
reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.3.0)
ResidualMatrix 1.12.0 2023-11-06 [1] Bioconductor
restfulr 0.0.15 2022-06-16 [1] CRAN (R 4.3.0)
reticulate 1.35.0 2024-01-31 [1] CRAN (R 4.3.1)
rhdf5 2.46.1 2023-12-02 [1] Bioconductor 3.18 (R 4.3.2)
rhdf5filters 1.14.1 2023-12-16 [1] Bioconductor 3.18 (R 4.3.2)
Rhdf5lib 1.24.2 2024-02-10 [1] Bioconductor 3.18 (R 4.3.2)
rjson 0.2.21 2022-01-09 [1] CRAN (R 4.3.0)
rlang 1.1.3 2024-01-10 [1] CRAN (R 4.3.1)
rmarkdown 2.25 2023-09-18 [1] CRAN (R 4.3.1)
robustbase 0.99-2 2024-01-27 [1] CRAN (R 4.3.1)
ROCR 1.0-11 2020-05-02 [1] CRAN (R 4.3.0)
rpart 4.1.23 2023-12-05 [1] CRAN (R 4.3.1)
rprojroot 2.0.4 2023-11-05 [1] CRAN (R 4.3.1)
Rsamtools 2.18.0 2023-10-26 [1] Bioconductor
RSpectra 0.16-1 2022-04-24 [1] CRAN (R 4.3.0)
RSQLite 2.3.5 2024-01-21 [1] CRAN (R 4.3.1)
rstan 2.32.5 2024-01-10 [1] CRAN (R 4.3.1)
rstantools 2.4.0 2024-01-31 [1] CRAN (R 4.3.1)
rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.3.0)
rsvd 1.0.5 2021-04-16 [1] CRAN (R 4.3.0)
rtracklayer 1.62.0 2023-10-26 [1] Bioconductor
Rtsne 0.17 2023-12-07 [1] CRAN (R 4.3.1)
ruv 0.9.7.1 2019-08-30 [1] CRAN (R 4.3.0)
S4Arrays 1.2.0 2023-10-26 [1] Bioconductor
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scales * 1.3.0 2023-11-28 [1] CRAN (R 4.3.1)
scater * 1.30.1 2023-11-16 [1] Bioconductor
scattermore 1.2 2023-06-12 [1] CRAN (R 4.3.0)
scDblFinder * 1.16.0 2023-12-23 [1] Bioconductor 3.18 (R 4.3.2)
scMerge * 1.18.0 2023-12-30 [1] Bioconductor 3.18 (R 4.3.2)
scran * 1.30.2 2024-01-23 [1] Bioconductor 3.18 (R 4.3.2)
sctransform 0.4.1 2023-10-19 [1] CRAN (R 4.3.1)
scuttle * 1.12.0 2023-11-06 [1] Bioconductor
seqLogo 1.68.0 2023-10-26 [1] Bioconductor
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.0)
Seurat * 5.0.1.9009 2024-02-28 [1] Github (satijalab/seurat@6a3ef5e)
SeuratData 0.2.2.9001 2024-02-28 [1] Github (satijalab/seurat-data@0cce240)
SeuratDisk 0.0.0.9021 2024-02-27 [1] Github (mojaveazure/seurat-disk@877d4e1)
SeuratObject * 5.0.1 2023-11-17 [1] CRAN (R 4.3.1)
sfsmisc 1.1-17 2024-02-01 [1] CRAN (R 4.3.1)
shiny 1.8.0 2023-11-17 [1] CRAN (R 4.3.1)
shinyBS * 0.61.1 2022-04-17 [1] CRAN (R 4.3.0)
shinydashboard 0.7.2 2021-09-30 [1] CRAN (R 4.3.0)
shinyjs 2.1.0 2021-12-23 [1] CRAN (R 4.3.0)
Signac 1.12.0 2023-11-08 [1] CRAN (R 4.3.1)
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sp * 2.1-3 2024-01-30 [1] CRAN (R 4.3.1)
spam 2.10-0 2023-10-23 [1] CRAN (R 4.3.1)
SparseArray 1.2.4 2024-02-10 [1] Bioconductor 3.18 (R 4.3.2)
sparseMatrixStats 1.14.0 2023-10-26 [1] Bioconductor
spatstat.data 3.0-4 2024-01-15 [1] CRAN (R 4.3.1)
spatstat.explore 3.2-6 2024-02-01 [1] CRAN (R 4.3.1)
spatstat.geom 3.2-8 2024-01-26 [1] CRAN (R 4.3.1)
spatstat.random 3.2-2 2023-11-29 [1] CRAN (R 4.3.1)
spatstat.sparse 3.0-3 2023-10-24 [1] CRAN (R 4.3.1)
spatstat.utils 3.0-4 2023-10-24 [1] CRAN (R 4.3.1)
StanHeaders 2.32.5 2024-01-10 [1] CRAN (R 4.3.1)
startupmsg 0.9.6.1 2024-02-12 [1] CRAN (R 4.3.1)
statmod 1.5.0 2023-01-06 [1] CRAN (R 4.3.0)
stringi 1.8.3 2023-12-11 [1] CRAN (R 4.3.1)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.3.1)
SummarizedExperiment * 1.32.0 2023-11-06 [1] Bioconductor
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tensor 1.5 2012-05-05 [1] CRAN (R 4.3.0)
TFBSTools 1.40.0 2023-10-24 [1] Bioconductor
TFMPvalue 0.0.9 2022-10-21 [1] CRAN (R 4.3.0)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.3.0)
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tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.3.0)
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tonsilref.SeuratData 2.0.0 2024-02-29 [1] local
TxDb.Hsapiens.UCSC.hg19.knownGene * 3.2.2 2024-02-27 [1] Bioconductor
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.3.0)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.3.1)
uwot 0.1.16 2023-06-29 [1] CRAN (R 4.3.0)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.1)
vipor 0.4.7 2023-12-18 [1] CRAN (R 4.3.1)
viridis 0.6.5 2024-01-29 [1] CRAN (R 4.3.1)
viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.3.0)
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withr 3.0.0 2024-01-16 [1] CRAN (R 4.3.1)
workflowr * 1.7.1 2023-08-23 [1] CRAN (R 4.3.0)
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xgboost 1.7.7.1 2024-01-25 [1] CRAN (R 4.3.1)
XML 3.99-0.16.1 2024-01-22 [1] CRAN (R 4.3.1)
xml2 1.3.6 2023-12-04 [1] CRAN (R 4.3.1)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.3.0)
XVector 0.42.0 2023-10-26 [1] Bioconductor
yaml 2.3.8 2023-12-11 [1] CRAN (R 4.3.1)
zlibbioc 1.48.0 2023-10-26 [1] Bioconductor
zoo 1.8-12 2023-04-13 [1] CRAN (R 4.3.0)
[1] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library
──────────────────────────────────────────────────────────────────────────────
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.4.1
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 utils datasets methods
[8] base
other attached packages:
[1] ggstats_0.5.1
[2] googlesheets4_1.1.1
[3] scMerge_1.18.0
[4] scDblFinder_1.16.0
[5] Azimuth_0.5.0
[6] shinyBS_0.61.1
[7] decontX_1.0.0
[8] celda_1.18.1
[9] Matrix_1.6-5
[10] Seurat_5.0.1.9009
[11] SeuratObject_5.0.1
[12] sp_2.1-3
[13] EnsDb.Hsapiens.v86_2.99.0
[14] ensembldb_2.26.0
[15] AnnotationFilter_1.26.0
[16] msigdbr_7.5.1
[17] Homo.sapiens_1.3.1
[18] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[19] org.Hs.eg.db_3.18.0
[20] GO.db_3.18.0
[21] OrganismDbi_1.44.0
[22] GenomicFeatures_1.54.3
[23] AnnotationDbi_1.64.1
[24] scales_1.3.0
[25] patchwork_1.2.0
[26] cowplot_1.1.3
[27] janitor_2.2.0
[28] scater_1.30.1
[29] scran_1.30.2
[30] scuttle_1.12.0
[31] SingleCellExperiment_1.24.0
[32] SummarizedExperiment_1.32.0
[33] Biobase_2.62.0
[34] GenomicRanges_1.54.1
[35] GenomeInfoDb_1.38.6
[36] IRanges_2.36.0
[37] S4Vectors_0.40.2
[38] BiocGenerics_0.48.1
[39] MatrixGenerics_1.14.0
[40] matrixStats_1.2.0
[41] glue_1.7.0
[42] here_1.0.1
[43] lubridate_1.9.3
[44] forcats_1.0.0
[45] stringr_1.5.1
[46] dplyr_1.1.4
[47] purrr_1.0.2
[48] readr_2.1.5
[49] tidyr_1.3.1
[50] tibble_3.2.1
[51] ggplot2_3.5.0
[52] tidyverse_2.0.0
[53] BiocParallel_1.36.0
[54] BiocStyle_2.30.0
[55] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] igraph_2.0.2 graph_1.80.0
[3] Formula_1.2-5 ica_1.0-3
[5] plotly_4.10.4 zlibbioc_1.48.0
[7] tidyselect_1.2.0 bit_4.0.5
[9] doParallel_1.0.17 lattice_0.22-5
[11] rjson_0.2.21 M3Drop_1.28.0
[13] blob_1.2.4 S4Arrays_1.2.0
[15] parallel_4.3.2 seqLogo_1.68.0
[17] png_0.1-8 ResidualMatrix_1.12.0
[19] cli_3.6.2 askpass_1.2.0
[21] ProtGenerics_1.34.0 openssl_2.1.1
[23] goftest_1.2-3 gargle_1.5.2
[25] BiocIO_1.12.0 bluster_1.12.0
[27] densEstBayes_1.0-2.2 BiocNeighbors_1.20.2
[29] Signac_1.12.0 uwot_0.1.16
[31] curl_5.2.0 mime_0.12
[33] evaluate_0.23 leiden_0.4.3.1
[35] stringi_1.8.3 backports_1.4.1
[37] XML_3.99-0.16.1 httpuv_1.6.14
[39] magrittr_2.0.3 rappdirs_0.3.3
[41] splines_4.3.2 RcppRoll_0.3.0
[43] DT_0.32 sctransform_0.4.1
[45] ggbeeswarm_0.7.2 sessioninfo_1.2.2
[47] DBI_1.2.2 jquerylib_0.1.4
[49] withr_3.0.0 git2r_0.33.0
[51] rprojroot_2.0.4 xgboost_1.7.7.1
[53] lmtest_0.9-40 RBGL_1.78.0
[55] bdsmatrix_1.3-6 rtracklayer_1.62.0
[57] BiocManager_1.30.22 htmlwidgets_1.6.4
[59] fs_1.6.3 biomaRt_2.58.2
[61] ggrepel_0.9.5 labeling_0.4.3
[63] SparseArray_1.2.4 DEoptimR_1.1-3
[65] cellranger_1.1.0 annotate_1.80.0
[67] reticulate_1.35.0 zoo_1.8-12
[69] JASPAR2020_0.99.10 XVector_0.42.0
[71] knitr_1.45 TFBSTools_1.40.0
[73] TFMPvalue_0.0.9 timechange_0.3.0
[75] foreach_1.5.2 fansi_1.0.6
[77] caTools_1.18.2 grid_4.3.2
[79] data.table_1.15.0 rhdf5_2.46.1
[81] ruv_0.9.7.1 R.oo_1.26.0
[83] poweRlaw_0.80.0 RSpectra_0.16-1
[85] irlba_2.3.5.1 fastDummies_1.7.3
[87] ellipsis_0.3.2 lazyeval_0.2.2
[89] yaml_2.3.8 survival_3.5-8
[91] scattermore_1.2 crayon_1.5.2
[93] RcppAnnoy_0.0.22 RColorBrewer_1.1-3
[95] progressr_0.14.0 later_1.3.2
[97] base64enc_0.1-3 ggridges_0.5.6
[99] codetools_0.2-19 KEGGREST_1.42.0
[101] bbmle_1.0.25.1 Rtsne_0.17
[103] startupmsg_0.9.6.1 limma_3.58.1
[105] Rsamtools_2.18.0 filelock_1.0.3
[107] foreign_0.8-86 pkgconfig_2.0.3
[109] xml2_1.3.6 sfsmisc_1.1-17
[111] GenomicAlignments_1.38.2 getPass_0.2-4
[113] spatstat.sparse_3.0-3 BSgenome_1.70.2
[115] viridisLite_0.4.2 xtable_1.8-4
[117] highr_0.10 plyr_1.8.9
[119] httr_1.4.7 tools_4.3.2
[121] globals_0.16.2 pkgbuild_1.4.3
[123] checkmate_2.3.1 htmlTable_2.4.2
[125] beeswarm_0.4.0 nlme_3.1-164
[127] loo_2.7.0 dbplyr_2.4.0
[129] hdf5r_1.3.9 shinyjs_2.1.0
[131] digest_0.6.34 numDeriv_2016.8-1.1
[133] farver_2.1.1 tzdb_0.4.0
[135] reshape2_1.4.4 cvTools_0.3.2
[137] WriteXLS_6.5.0 viridis_0.6.5
[139] rpart_4.1.23 DirichletMultinomial_1.44.0
[141] cachem_1.0.8 BiocFileCache_2.10.1
[143] polyclip_1.10-6 proxyC_0.3.4
[145] Hmisc_5.1-1 generics_0.1.3
[147] Biostrings_2.70.2 mvtnorm_1.2-4
[149] googledrive_2.1.1 presto_1.0.0
[151] parallelly_1.37.0 statmod_1.5.0
[153] RcppHNSW_0.6.0 ScaledMatrix_1.10.0
[155] pbapply_1.7-2 spam_2.10-0
[157] dqrng_0.3.2 utf8_1.2.4
[159] StanHeaders_2.32.5 gtools_3.9.5
[161] RcppEigen_0.3.3.9.4 gridExtra_2.3
[163] shiny_1.8.0 GenomeInfoDbData_1.2.11
[165] R.utils_2.12.3 rhdf5filters_1.14.1
[167] RCurl_1.98-1.14 memoise_2.0.1
[169] rmarkdown_2.25 R.methodsS3_1.8.2
[171] future_1.33.1 RANN_2.6.1
[173] spatstat.data_3.0-4 rstudioapi_0.15.0
[175] cluster_2.1.6 QuickJSR_1.1.3
[177] whisker_0.4.1 rstantools_2.4.0
[179] spatstat.utils_3.0-4 hms_1.1.3
[181] fitdistrplus_1.1-11 munsell_0.5.0
[183] colorspace_2.1-0 rlang_1.1.3
[185] DelayedMatrixStats_1.24.0 sparseMatrixStats_1.14.0
[187] dotCall64_1.1-1 shinydashboard_0.7.2
[189] dbscan_1.1-12 mgcv_1.9-1
[191] xfun_0.42 CNEr_1.38.0
[193] iterators_1.0.14 reldist_1.7-2
[195] abind_1.4-5 MCMCprecision_0.4.0
[197] rstan_2.32.5 Rhdf5lib_1.24.2
[199] bitops_1.0-7 ps_1.7.6
[201] promises_1.2.1 inline_0.3.19
[203] RSQLite_2.3.5 DelayedArray_0.28.0
[205] compiler_4.3.2 prettyunits_1.2.0
[207] beachmat_2.18.1 listenv_0.9.1
[209] BSgenome.Hsapiens.UCSC.hg38_1.4.5 Rcpp_1.0.12
[211] tonsilref.SeuratData_2.0.0 enrichR_3.2
[213] edgeR_4.0.16 BiocSingular_1.18.0
[215] tensor_1.5 MASS_7.3-60.0.1
[217] progress_1.2.3 babelgene_22.9
[219] spatstat.random_3.2-2 R6_2.5.1
[221] fastmap_1.1.1 fastmatch_1.1-4
[223] distr_2.9.3 vipor_0.4.7
[225] ROCR_1.0-11 SeuratDisk_0.0.0.9021
[227] nnet_7.3-19 rsvd_1.0.5
[229] gtable_0.3.4 KernSmooth_2.23-22
[231] lungref.SeuratData_2.0.0 miniUI_0.1.1.1
[233] deldir_2.0-2 htmltools_0.5.7
[235] RcppParallel_5.1.7 bit64_4.0.5
[237] spatstat.explore_3.2-6 lifecycle_1.0.4
[239] processx_3.8.3 callr_3.7.5
[241] restfulr_0.0.15 sass_0.4.8
[243] vctrs_0.6.5 robustbase_0.99-2
[245] spatstat.geom_3.2-8 snakecase_0.11.1
[247] SeuratData_0.2.2.9001 future.apply_1.11.1
[249] pracma_2.4.4 batchelor_1.18.1
[251] bslib_0.6.1 pillar_1.9.0
[253] gplots_3.1.3.1 metapod_1.10.1
[255] locfit_1.5-9.8 combinat_0.0-8
[257] jsonlite_1.8.8