Last updated: 2024-11-11
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Knit directory: paed-airway-allTissues/
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| html | bd5ec04 | Gunjan Dixit | 2024-05-03 | Modified index |
This RMarkdown performs quality control for the earlyAIR batch- Nasal_brushings- Batch1
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)batch_path <- here("output/RDS/AllBatches_filtered_SCEs/G000231_batch1_Nasal_brushings.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$batch_name <- batch_name
sceclass: SingleCellExperiment
dim: 18082 43290
metadata(0):
assays(2): counts logcounts
rownames(18082): SAMD11 NOC2L ... MT-ND6 MT-CYB
rowData names(0):
colnames(43290): AAACCAATCATGAGGTACTTTAGG-1 AAACCAGGTGTCCAATACTTTAGG-1
... TTTGCTGAGATTGAGCATTCGGTT-1 TTTGGCGGTAAGGTTGATTCGGTT-1
colData names(7): orig.ident nCount_RNA ... tissue batch_name
reducedDimNames(0):
mainExpName: RNA
altExpNames(0):Filter cells with zero counts across all genes
sce <- sce[rowSums(counts(sce)) > 0, ]
sceclass: SingleCellExperiment
dim: 17474 43290
metadata(0):
assays(2): counts logcounts
rownames(17474): SAMD11 NOC2L ... MT-ND6 MT-CYB
rowData names(0):
colnames(43290): AAACCAATCATGAGGTACTTTAGG-1 AAACCAGGTGTCCAATACTTTAGG-1
... TTTGCTGAGATTGAGCATTCGGTT-1 TTTGGCGGTAAGGTTGATTCGGTT-1
colData names(7): orig.ident nCount_RNA ... tissue batch_name
reducedDimNames(0):
mainExpName: RNA
altExpNames(0):cell_counts <- c()
cell_counts["Post CellRanger Filtering"] <- ncol(sce)The first 17 characters of the barcodes are the GEM barcode and the last 9 characters are the sample barcode. Create a metadata feature for each of these.
sce$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)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--------------------------------------------------Mon Nov 11 15:26:58 2024 .. Analyzing all cellsMon Nov 11 15:26:58 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'Mon Nov 11 15:28:07 2024 .... Estimating contaminationMon Nov 11 15:28:14 2024 ...... Completed iteration: 10 | converge: 0.03919Mon Nov 11 15:28:19 2024 ...... Completed iteration: 20 | converge: 0.01257Mon Nov 11 15:28:25 2024 ...... Completed iteration: 30 | converge: 0.009682Mon Nov 11 15:28:31 2024 ...... Completed iteration: 40 | converge: 0.004789Mon Nov 11 15:28:37 2024 ...... Completed iteration: 50 | converge: 0.003704Mon Nov 11 15:28:43 2024 ...... Completed iteration: 60 | converge: 0.002934Mon Nov 11 15:28:49 2024 ...... Completed iteration: 70 | converge: 0.002333Mon Nov 11 15:28:55 2024 ...... Completed iteration: 80 | converge: 0.001852Mon Nov 11 15:29:00 2024 ...... Completed iteration: 90 | converge: 0.005455Mon Nov 11 15:29:06 2024 ...... Completed iteration: 100 | converge: 0.0021Mon Nov 11 15:29:12 2024 ...... Completed iteration: 110 | converge: 0.001555Mon Nov 11 15:29:18 2024 ...... Completed iteration: 120 | converge: 0.0017Mon Nov 11 15:29:24 2024 ...... Completed iteration: 130 | converge: 0.001892Mon Nov 11 15:29:29 2024 ...... Completed iteration: 140 | converge: 0.002202Mon Nov 11 15:29:35 2024 ...... Completed iteration: 150 | converge: 0.002567Mon Nov 11 15:29:41 2024 ...... Completed iteration: 160 | converge: 0.002954Mon Nov 11 15:29:47 2024 ...... Completed iteration: 170 | converge: 0.003292Mon Nov 11 15:29:53 2024 ...... Completed iteration: 180 | converge: 0.003486Mon Nov 11 15:29:59 2024 ...... Completed iteration: 190 | converge: 0.003451Mon Nov 11 15:30:04 2024 ...... Completed iteration: 200 | converge: 0.003361Mon Nov 11 15:30:10 2024 ...... Completed iteration: 210 | converge: 0.003045Mon Nov 11 15:30:16 2024 ...... Completed iteration: 220 | converge: 0.0025Mon Nov 11 15:30:22 2024 ...... Completed iteration: 230 | converge: 0.001937Mon Nov 11 15:30:28 2024 ...... Completed iteration: 240 | converge: 0.001478Mon Nov 11 15:30:34 2024 ...... Completed iteration: 250 | converge: 0.00108Mon Nov 11 15:30:39 2024 ...... Completed iteration: 260 | converge: 0.0009566Mon Nov 11 15:30:39 2024 .. Calculating final decontaminated matrix--------------------------------------------------Completed DecontX. Total time: 3.809982 mins--------------------------------------------------assay(sce, "raw_counts") <- counts(sce)
counts(sce) <- decontXcounts(sce)Filter on library size filter after running decontX
sce <- addPerCellQCMetrics(sce)
sum(sce$sum < 250)[1] 1986sce <- sce[, sce$sum >= 250]
cell_counts["Post low-lib Filtering"] <- ncol(sce)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$subsets_mito_percent, type = "higher")
sum(mito_outliers)[1] 6626sce <- sce[, !mito_outliers]
cell_counts["Post Mito Filtering"] <- ncol(sce)We know that there will be some unidentified multiplets in our data, as higher-occupancy GEMs have many ways to include multiple cells from the same samples. Still working on a way to estimate the number of these but the existing doublet-finding tools work ok. Using scDblFinder as that seemed to have the best effect on the GEM-level counts.
sce <- logNormCounts(sce) %>%
runPCA() %>%
runUMAP()Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'Also defined by 'spam'Run scDblFinder
bp <- MulticoreParam(8, RNGseed=56213)
#sce <- scDblFinder(sce, clusters = T,BPPARAM=bp)params <- list(
dbr = list(clusters = TRUE, BPPARAM = bp, dbr.sd = 1),
dbr_s = list(clusters = TRUE, BPPARAM = bp, dbr.sd = 1, samples = sce$sample_barcode),
s = list(clusters = TRUE, BPPARAM = bp, samples = sce$sample_barcode),
cl = list(clusters = TRUE, BPPARAM = bp)
)
# Run scDblFinder for each parameter set, rename columns, and merge results
for (suffix in names(params)) {
sce_temp <- do.call(scDblFinder, c(list(sce), params[[suffix]]))
# Loop through the relevant columns and rename them with the suffix
for (colname in c("cluster", "class", "originAmbiguous", "mostLikelyOrigin",
"cxds_score", "difficulty", "weighted", "score")) {
sce[[paste0("scDblFinder.", colname, "_", suffix)]] <- sce_temp[[paste0("scDblFinder.", colname)]]
}
}Warning in (function (sce, clusters = NULL, samples = NULL, clustCor = NULL, :
You are trying to run scDblFinder on a very large number of cells. If these are
from different captures, please specify this using the `samples` argument.TRUEClustering cells...16 clustersCreating ~25000 artificial doublets...Dimensional reductionEvaluating kNN...Training model...iter=0, 2771 cells excluded from training.iter=1, 2834 cells excluded from training.iter=2, 2805 cells excluded from training.Threshold found:0.4033079 (8.9%) doublets calledWarning in (function (sce, clusters = NULL, samples = NULL, clustCor = NULL, :
You are trying to run scDblFinder on a very large number of cells. If these are
from different captures, please specify this using the `samples` argument.TRUEClustering cells...16 clustersCreating ~25000 artificial doublets...Dimensional reductionEvaluating kNN...Training model...iter=0, 6029 cells excluded from training.iter=1, 6055 cells excluded from training.iter=2, 5840 cells excluded from training.Threshold found:0.2446013 (17.3%) doublets calledtable(sce$scDblFinder.class_dbr)
singlet doublet
31599 3079 table(sce$scDblFinder.class_dbr_s)
singlet doublet
32417 2261 table(sce$scDblFinder.class_s)
singlet doublet
32957 1721 table(sce$scDblFinder.class_cl)
singlet doublet
28665 6013 Make Seurat object
seu <- CreateSeuratObject(counts(sce), meta.data = as.data.frame(colData(sce)))seu$cells_per_GEM <- table(seu$GEM_barcode)[seu$GEM_barcode]table(seu$cells_per_GEM)
1 2 3 4
16275 12106 4953 1344 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 = "lungref") 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 26809 anchorsFinding integration vectorsFinding integration vector weightsPredicting cell labels
Predicting cell labels
Predicting cell labels
Predicting cell labels
Predicting cell labels
Predicting cell labels
Integrating dataset 2 with reference datasetFinding integration vectorsIntegrating dataComputing nearest neighborsRunning UMAP projection16:27:45 Read 34678 rows16:27:45 Processing block 1 of 116:27:45 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 20
16:27:46 Initializing by weighted average of neighbor coordinates using 1 thread
16:27:46 Commencing optimization for 67 epochs, with 693560 positive edges
16:27:48 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: 15.759418964386seu@meta.data <- tmp@meta.dataout <- here("output",
"RDS", "AllBatches_scDblFinder_test_SEUs",
paste0(batch_name, "_", tissue, ".CellRanger.decontX.mito.filter.Azimuth.SEU.rds"))
saveRDS(seu, file = out)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)))
datbatch_meta <- dat %>%
dplyr::filter(run == "batch1_1")
#batch_meta$sample_id <- gsub("_", "-", batch_meta$sample_id) #For Batch7
seu$sample_id <- sapply(seu$Sample, function(x) batch_meta$sample_id[batch_meta$donor_id == x])
seu$donor_id <- sapply(seu$Sample, function(x) batch_meta$donor_id[batch_meta$donor_id == x])
seu$sex <- sapply(seu$Sample, function(x) batch_meta$sex[batch_meta$donor_id == x])
seu$age_years <- sapply(seu$Sample, function(x) batch_meta$age_years[batch_meta$donor_id == x])seu@meta.data <- seu@meta.data %>%
dplyr::select(c(donor_id, sample_id, age_years, sex, nCount_RNA, nFeature_RNA,
Barcode, GEM_barcode, sample_barcode,
tissue, batch_name, sum, detected,
cells_per_GEM,
scDblFinder.class, scDblFinder.score,
predicted.ann_level_1, predicted.ann_level_1.score, predicted.ann_level_2, predicted.ann_level_2.score, predicted.ann_level_3, predicted.ann_level_3.score, predicted.ann_level_4, predicted.ann_level_4.score, predicted.ann_level_5, predicted.ann_level_5.score, predicted.ann_finest_level, predicted.ann_finest_level.score))out <- here("output",
"RDS", "AllBatches_Azimuth_SEUs",
paste0(batch_name, "_", tissue, ".CellRanger.decontX.mito.filter.Azimuth.SEU.rds"))
saveRDS(seu, file = out)seu <- seu[, seu$scDblFinder.class == "singlet"]
cell_counts["Post Doublet Filtering"] <- ncol(sce)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) options(timeout = max(1000000, getOption("timeout")))
tmp <- RunAzimuth(seu, reference = "lungref")
seu@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# Function to map cell types to broad cell label
map_to_broad_cell_label <- function(cell_type, broad_cell_labels_df, label_column) {
label <- broad_cell_labels_df[[label_column]][broad_cell_labels_df$`Cell Types` == cell_type]
if (length(label) == 0) {
return("Unknown") # Assign to "Unknown" if not found in mapping
} else {
return(label)
}
}
broad_cell_labels <- readxl::read_excel(here("data/celltypes_Mel_v2_MN.xlsx")) #modified cell types based on Tonsils ref v2
seu$Broad_cell_label_1 <- sapply(seu$predicted.ann_level_4, map_to_broad_cell_label, broad_cell_labels_df = broad_cell_labels, label_column = "Broad cell label level 1")
# Apply mapping to Seurat object for Broad Cell Label 2
seu$Broad_cell_label_2 <- sapply(seu$predicted.ann_level_4, map_to_broad_cell_label, broad_cell_labels_df = broad_cell_labels, label_column = "Broad cell label level 2")
# Apply mapping to Seurat object for Broad Cell Label 3
seu$Broad_cell_label_3 <- sapply(seu$predicted.ann_level_4, map_to_broad_cell_label, broad_cell_labels_df = broad_cell_labels, label_column = "Broad cell label level 3")
table(seu$Broad_cell_label_2 == "Unknown")
table(seu$Broad_cell_label_2 == "NA")out <- here("output",
"RDS", "AllBatches_Azimuth_noDoublets_SEUs",
paste0(batch_name, "_", tissue, ".CellRanger.decontX.mito.doublet.filter.Azimuth.SEU.rds"))
saveRDS(seu, file = out)sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.3.2 (2023-10-31)
os macOS 15.0.1
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Australia/Melbourne
date 2024-11-11
pandoc 3.1.1 @ /Users/dixitgunjan/Desktop/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-5 2016-07-21 [1] CRAN (R 4.3.0)
annotate 1.80.0 2023-10-26 [1] Bioconductor
AnnotationDbi * 1.64.1 2023-11-02 [1] Bioconductor
AnnotationFilter * 1.26.0 2023-10-26 [1] Bioconductor
Azimuth * 0.5.0 2024-02-27 [1] Github (satijalab/azimuth@c3ad1bc)
babelgene 22.9 2022-09-29 [1] CRAN (R 4.3.0)
backports 1.4.1 2021-12-13 [1] CRAN (R 4.3.0)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.3.0)
batchelor 1.18.1 2023-12-30 [1] Bioconductor 3.18 (R 4.3.2)
bbmle 1.0.25.1 2023-12-09 [1] CRAN (R 4.3.1)
bdsmatrix 1.3-6 2022-06-03 [1] CRAN (R 4.3.0)
beachmat 2.18.1 2024-02-17 [1] Bioconductor 3.18 (R 4.3.2)
beeswarm 0.4.0 2021-06-01 [1] CRAN (R 4.3.0)
Biobase * 2.62.0 2023-10-26 [1] Bioconductor
BiocFileCache 2.10.1 2023-10-26 [1] Bioconductor
BiocGenerics * 0.48.1 2023-11-02 [1] Bioconductor
BiocIO 1.12.0 2023-10-26 [1] Bioconductor
BiocManager 1.30.22 2023-08-08 [1] CRAN (R 4.3.0)
BiocNeighbors 1.20.2 2024-01-13 [1] Bioconductor 3.18 (R 4.3.2)
BiocParallel * 1.36.0 2023-10-26 [1] Bioconductor
BiocSingular 1.18.0 2023-11-06 [1] Bioconductor
BiocStyle * 2.30.0 2023-10-26 [1] Bioconductor
biomaRt 2.58.2 2024-02-03 [1] Bioconductor 3.18 (R 4.3.2)
Biostrings 2.70.2 2024-01-30 [1] Bioconductor 3.18 (R 4.3.2)
bit 4.0.5 2022-11-15 [1] CRAN (R 4.3.0)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.3.0)
bitops 1.0-7 2021-04-24 [1] CRAN (R 4.3.0)
blob 1.2.4 2023-03-17 [1] CRAN (R 4.3.0)
bluster 1.12.0 2023-12-19 [1] Bioconductor 3.18 (R 4.3.2)
BSgenome 1.70.2 2024-02-10 [1] Bioconductor 3.18 (R 4.3.2)
BSgenome.Hsapiens.UCSC.hg38 1.4.5 2024-02-27 [1] Bioconductor
bslib 0.6.1 2023-11-28 [1] CRAN (R 4.3.1)
cachem 1.0.8 2023-05-01 [1] CRAN (R 4.3.0)
caTools 1.18.2 2021-03-28 [1] CRAN (R 4.3.0)
celda * 1.18.1 2023-12-23 [1] Bioconductor 3.18 (R 4.3.2)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.3.0)
checkmate 2.3.1 2023-12-04 [1] CRAN (R 4.3.1)
cli 3.6.2 2023-12-11 [1] CRAN (R 4.3.1)
cluster 2.1.6 2023-12-01 [1] CRAN (R 4.3.1)
CNEr 1.38.0 2023-10-24 [1] Bioconductor
codetools 0.2-19 2023-02-01 [1] CRAN (R 4.3.2)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.3.0)
combinat 0.0-8 2012-10-29 [1] CRAN (R 4.3.0)
cowplot * 1.1.3 2024-01-22 [1] CRAN (R 4.3.1)
crayon 1.5.2 2022-09-29 [1] CRAN (R 4.3.0)
curl 5.2.0 2023-12-08 [1] CRAN (R 4.3.1)
cvTools 0.3.2 2012-05-14 [1] CRAN (R 4.3.0)
data.table 1.15.0 2024-01-30 [1] CRAN (R 4.3.1)
DBI 1.2.2 2024-02-16 [1] CRAN (R 4.3.1)
dbplyr 2.4.0 2023-10-26 [1] CRAN (R 4.3.1)
dbscan 1.1-12 2023-11-28 [1] CRAN (R 4.3.1)
decontX * 1.0.0 2023-12-23 [1] Bioconductor 3.18 (R 4.3.2)
DelayedArray 0.28.0 2023-11-06 [1] Bioconductor
DelayedMatrixStats 1.24.0 2023-11-06 [1] Bioconductor
deldir 2.0-2 2023-11-23 [1] CRAN (R 4.3.1)
densEstBayes 1.0-2.2 2023-03-31 [1] CRAN (R 4.3.0)
DEoptimR 1.1-3 2023-10-07 [1] CRAN (R 4.3.1)
digest 0.6.34 2024-01-11 [1] CRAN (R 4.3.1)
DirichletMultinomial 1.44.0 2023-10-26 [1] Bioconductor
distr 2.9.3 2024-01-29 [1] CRAN (R 4.3.1)
doParallel 1.0.17 2022-02-07 [1] CRAN (R 4.3.0)
dotCall64 1.1-1 2023-11-28 [1] CRAN (R 4.3.1)
dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.3.1)
dqrng 0.3.2 2023-11-29 [1] CRAN (R 4.3.1)
DT 0.32 2024-02-19 [1] CRAN (R 4.3.1)
edgeR 4.0.16 2024-02-20 [1] Bioconductor 3.18 (R 4.3.2)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.3.0)
enrichR 3.2 2023-04-14 [1] CRAN (R 4.3.0)
EnsDb.Hsapiens.v86 * 2.99.0 2024-02-27 [1] Bioconductor
ensembldb * 2.26.0 2023-10-26 [1] Bioconductor
evaluate 0.23 2023-11-01 [1] CRAN (R 4.3.1)
fansi 1.0.6 2023-12-08 [1] CRAN (R 4.3.1)
fastDummies 1.7.3 2023-07-06 [1] CRAN (R 4.3.0)
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)
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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)
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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
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)
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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)
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)
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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
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janitor * 2.2.0 2023-02-02 [1] CRAN (R 4.3.0)
JASPAR2020 0.99.10 2024-02-27 [1] Bioconductor
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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)
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)
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limma 3.58.1 2023-11-02 [1] Bioconductor
listenv 0.9.1 2024-01-29 [1] CRAN (R 4.3.1)
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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)
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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)
org.Hs.eg.db * 3.18.0 2024-02-27 [1] Bioconductor
OrganismDbi * 1.44.0 2023-10-26 [1] Bioconductor
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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)
pbmcref.SeuratData 1.0.0 2024-10-04 [1] local
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)
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
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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)
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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)
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sass 0.4.8 2023-12-06 [1] CRAN (R 4.3.1)
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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)
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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)
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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)
SingleCellExperiment * 1.24.0 2023-11-06 [1] Bioconductor
snakecase 0.11.1 2023-08-27 [1] CRAN (R 4.3.0)
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
survival 3.5-8 2024-02-14 [1] CRAN (R 4.3.1)
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)
tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.3.1)
tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.3.0)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.3.0)
timechange 0.3.0 2024-01-18 [1] CRAN (R 4.3.1)
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)
whisker 0.4.1 2022-12-05 [1] CRAN (R 4.3.0)
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)
WriteXLS 6.5.0 2024-01-09 [1] CRAN (R 4.3.1)
xfun 0.42 2024-02-08 [1] CRAN (R 4.3.1)
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 15.0.1
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
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 ProtGenerics_1.34.0
[21] goftest_1.2-3 gargle_1.5.2
[23] BiocIO_1.12.0 bluster_1.12.0
[25] densEstBayes_1.0-2.2 BiocNeighbors_1.20.2
[27] Signac_1.12.0 uwot_0.1.16
[29] curl_5.2.0 mime_0.12
[31] evaluate_0.23 leiden_0.4.3.1
[33] stringi_1.8.3 backports_1.4.1
[35] XML_3.99-0.16.1 httpuv_1.6.14
[37] magrittr_2.0.3 rappdirs_0.3.3
[39] splines_4.3.2 RcppRoll_0.3.0
[41] DT_0.32 sctransform_0.4.1
[43] ggbeeswarm_0.7.2 sessioninfo_1.2.2
[45] DBI_1.2.2 jquerylib_0.1.4
[47] withr_3.0.0 git2r_0.33.0
[49] rprojroot_2.0.4 xgboost_1.7.7.1
[51] lmtest_0.9-40 RBGL_1.78.0
[53] bdsmatrix_1.3-6 rtracklayer_1.62.0
[55] BiocManager_1.30.22 htmlwidgets_1.6.4
[57] fs_1.6.3 biomaRt_2.58.2
[59] ggrepel_0.9.5 SparseArray_1.2.4
[61] DEoptimR_1.1-3 cellranger_1.1.0
[63] annotate_1.80.0 reticulate_1.35.0
[65] zoo_1.8-12 JASPAR2020_0.99.10
[67] XVector_0.42.0 knitr_1.45
[69] TFBSTools_1.40.0 TFMPvalue_0.0.9
[71] timechange_0.3.0 foreach_1.5.2
[73] fansi_1.0.6 caTools_1.18.2
[75] grid_4.3.2 data.table_1.15.0
[77] rhdf5_2.46.1 ruv_0.9.7.1
[79] R.oo_1.26.0 poweRlaw_0.80.0
[81] RSpectra_0.16-1 irlba_2.3.5.1
[83] fastDummies_1.7.3 ellipsis_0.3.2
[85] lazyeval_0.2.2 yaml_2.3.8
[87] survival_3.5-8 scattermore_1.2
[89] crayon_1.5.2 RcppAnnoy_0.0.22
[91] RColorBrewer_1.1-3 progressr_0.14.0
[93] later_1.3.2 base64enc_0.1-3
[95] ggridges_0.5.6 codetools_0.2-19
[97] KEGGREST_1.42.0 bbmle_1.0.25.1
[99] Rtsne_0.17 startupmsg_0.9.6.1
[101] limma_3.58.1 Rsamtools_2.18.0
[103] filelock_1.0.3 foreign_0.8-86
[105] pkgconfig_2.0.3 xml2_1.3.6
[107] sfsmisc_1.1-17 GenomicAlignments_1.38.2
[109] spatstat.sparse_3.0-3 BSgenome_1.70.2
[111] viridisLite_0.4.2 xtable_1.8-4
[113] plyr_1.8.9 httr_1.4.7
[115] tools_4.3.2 globals_0.16.2
[117] pkgbuild_1.4.3 checkmate_2.3.1
[119] htmlTable_2.4.2 beeswarm_0.4.0
[121] nlme_3.1-164 loo_2.7.0
[123] dbplyr_2.4.0 hdf5r_1.3.9
[125] shinyjs_2.1.0 digest_0.6.34
[127] numDeriv_2016.8-1.1 tzdb_0.4.0
[129] reshape2_1.4.4 cvTools_0.3.2
[131] WriteXLS_6.5.0 viridis_0.6.5
[133] rpart_4.1.23 DirichletMultinomial_1.44.0
[135] cachem_1.0.8 BiocFileCache_2.10.1
[137] polyclip_1.10-6 proxyC_0.3.4
[139] Hmisc_5.1-1 generics_0.1.3
[141] Biostrings_2.70.2 mvtnorm_1.2-4
[143] googledrive_2.1.1 presto_1.0.0
[145] parallelly_1.37.0 statmod_1.5.0
[147] RcppHNSW_0.6.0 ScaledMatrix_1.10.0
[149] pbapply_1.7-2 spam_2.10-0
[151] dqrng_0.3.2 utf8_1.2.4
[153] pbmcref.SeuratData_1.0.0 StanHeaders_2.32.5
[155] gtools_3.9.5 RcppEigen_0.3.3.9.4
[157] gridExtra_2.3 shiny_1.8.0
[159] GenomeInfoDbData_1.2.11 R.utils_2.12.3
[161] rhdf5filters_1.14.1 RCurl_1.98-1.14
[163] memoise_2.0.1 rmarkdown_2.25
[165] R.methodsS3_1.8.2 future_1.33.1
[167] RANN_2.6.1 spatstat.data_3.0-4
[169] rstudioapi_0.15.0 cluster_2.1.6
[171] QuickJSR_1.1.3 whisker_0.4.1
[173] rstantools_2.4.0 spatstat.utils_3.0-4
[175] hms_1.1.3 fitdistrplus_1.1-11
[177] munsell_0.5.0 colorspace_2.1-0
[179] rlang_1.1.3 DelayedMatrixStats_1.24.0
[181] sparseMatrixStats_1.14.0 dotCall64_1.1-1
[183] shinydashboard_0.7.2 dbscan_1.1-12
[185] mgcv_1.9-1 xfun_0.42
[187] CNEr_1.38.0 iterators_1.0.14
[189] reldist_1.7-2 abind_1.4-5
[191] MCMCprecision_0.4.0 rstan_2.32.5
[193] Rhdf5lib_1.24.2 bitops_1.0-7
[195] promises_1.2.1 inline_0.3.19
[197] RSQLite_2.3.5 DelayedArray_0.28.0
[199] compiler_4.3.2 prettyunits_1.2.0
[201] beachmat_2.18.1 listenv_0.9.1
[203] BSgenome.Hsapiens.UCSC.hg38_1.4.5 Rcpp_1.0.12
[205] tonsilref.SeuratData_2.0.0 enrichR_3.2
[207] edgeR_4.0.16 workflowr_1.7.1
[209] BiocSingular_1.18.0 tensor_1.5
[211] MASS_7.3-60.0.1 progress_1.2.3
[213] babelgene_22.9 spatstat.random_3.2-2
[215] R6_2.5.1 fastmap_1.1.1
[217] fastmatch_1.1-4 distr_2.9.3
[219] vipor_0.4.7 ROCR_1.0-11
[221] SeuratDisk_0.0.0.9021 nnet_7.3-19
[223] rsvd_1.0.5 gtable_0.3.4
[225] KernSmooth_2.23-22 lungref.SeuratData_2.0.0
[227] miniUI_0.1.1.1 deldir_2.0-2
[229] htmltools_0.5.7 RcppParallel_5.1.7
[231] bit64_4.0.5 spatstat.explore_3.2-6
[233] lifecycle_1.0.4 restfulr_0.0.15
[235] sass_0.4.8 vctrs_0.6.5
[237] robustbase_0.99-2 spatstat.geom_3.2-8
[239] snakecase_0.11.1 SeuratData_0.2.2.9001
[241] future.apply_1.11.1 pracma_2.4.4
[243] batchelor_1.18.1 bslib_0.6.1
[245] pillar_1.9.0 gplots_3.1.3.1
[247] metapod_1.10.1 locfit_1.5-9.8
[249] combinat_0.0-8 jsonlite_1.8.8