Analysis of scRNA-seq & CITE-seq Data Combined
Normalise and Transfer ADT data
Jovana Maksimovic
June 16, 2022
Last updated: 2022-06-16
Checks: 7 0
Knit directory:
paed-cf-cite-seq/
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| Rmd | 8255c24 | Jovana Maksimovic | 2022-06-16 | wflow_publish(c(paste0("analysis/", list.files(path = here::here("analysis"), |
1 Load libraries
loaded dsb package version 1.0.1 please cite DOI: 10.1101/2020.02.24.9636032 Load data
2.1 Load C133_Neeland raw and preprocessed CITE-seq data
sceRaw <- readRDS(here("data/SCEs/C133_Neeland.CellRanger.SCE.rds"))
is_hto <- rownames(altExp(sceRaw, "Antibody Capture")) %in%
paste0("Human_HTO_", 1:8)
altExp(sceRaw, "HTO") <- altExp(sceRaw, "Antibody Capture")[is_hto, ]
altExp(sceRaw, "ADT") <- altExp(sceRaw, "Antibody Capture")[!is_hto, ]
altExp(sceRaw, "Antibody Capture") <- NULL
# Load C133_Neeland ADT data
scePrep <- readRDS(here("data", "SCEs",
"03_C133_Neeland.preprocessed.SCE.rds"))2.1.1 Plot raw background and cell ADT expression
md <- data.frame(
rna.size = log10(Matrix::colSums(counts(sceRaw))),
prot.size = log10(Matrix::colSums(counts(altExp(sceRaw, "ADT")))))
md <- md[md$rna.size > 0 & md$prot.size > 0, ]
md$type <- ifelse(rownames(md) %in% colnames(scePrep), "cell", "background")
ggplot(md[md$type == "cell",], aes(x = prot.size,
y = rna.size)) +
geom_hex() +
ggtitle("cells") +
xlim(c(0, 5)) +
ylim(c(0,5)) +
scale_fill_viridis_c(option = "magma") -> p1
ggplot(md[md$type != "cell",], aes(x = prot.size,
y = rna.size)) +
geom_hex() +
xlim(c(0, 5)) +
ylim(c(0,5)) +
ggtitle("background") +
scale_fill_viridis_c(option = "magma") -> p2
(p1 | p2) & theme(legend.position = "bottom",
legend.text = element_text(size = 6))
2.2 DSB normalise ADT data
# remove low end ADT expression droplets
md <- md[md$prot.size > 1.5 & md$rna.size > 1.5, ]
background.adt.mtx <- counts(altExp(sceRaw, "ADT"))[, colnames(sceRaw) %in% rownames(md[md$type == "background",])]
keep <- grepl("^A0", rownames(background.adt.mtx))
background.adt.mtx <- background.adt.mtx[keep, ]
cell.adt.mtx <- counts(altExp(scePrep, "ADT"))
cell.adt.mtx <- cell.adt.mtx[keep, ]
read_csv(file = here("data/sample_sheets/TotalSeq-A_Universal_Cocktail_v1.0.csv")) %>%
dplyr::filter(grepl("[Ii]sotype", name)) %>%
pull(id) -> isotype.controls
# normalize and denoise with dsb with
out <- here("data/SCEs/04_C133_Neeland.adt_dsb_normalised.rds")
if(!file.exists(out)){
cells.dsb.norm <- DSBNormalizeProtein(cell_protein_matrix = cell.adt.mtx,
empty_drop_matrix = background.adt.mtx,
denoise.counts = TRUE,
use.isotype.control = TRUE,
isotype.control.name.vec = isotype.controls)
saveRDS(cells.dsb.norm, file = out)
} else {
cells.dsb.norm <- readRDS(out)
}2.3 Load processed RNA data
Load integrated, clustered data that has been mapped to Zilionis reference.
out <- here("data/SCEs/04_COMBO.zilionis_mapped.SEU.rds")
seuInt <- readRDS(out)
## Add Azimuth HCLA v1.0 labels
seuInt <- AddAzimuthResults(seuInt,
filename = here("data/SCEs/03_COMBO.clustered_azimuth.SEU.rds"))
seuInt$predicted.annotation.l1 <- fct_drop(seuInt$predicted.annotation.l1)
## Add Azimuth HCLA v2.0 labels
seuInt <- AddAzimuthResults(seuInt,
filename = here("data/SCEs/03_COMBO.clustered_azimuth_v2.SEU.rds"))
seuInt$predicted.ann_level_1 <- fct_drop(seuInt$predicted.ann_level_1)
seuInt$predicted.ann_level_2 <- fct_drop(seuInt$predicted.ann_level_2)
seuInt$predicted.ann_level_3 <- fct_drop(seuInt$predicted.ann_level_3)
seuInt$predicted.ann_level_4 <- fct_drop(seuInt$predicted.ann_level_4)
seuInt$predicted.ann_finest_level <- fct_drop(seuInt$predicted.ann_finest_level)2.4 Add ADT data to Seurat object
# Extract C133_Neeland RNA data from Seurat object
DefaultAssay(seuInt) <- "RNA"
seuAdt <- DietSeurat(seuInt[, seuInt$experiment == 2], assays = "RNA")
# Create cell ID that matched SCE object
colnames(cells.dsb.norm) <- paste0("B-", colnames(cells.dsb.norm))
colnames(cell.adt.mtx) <- paste0("B-", colnames(cell.adt.mtx))
# Check that all cells in Seurat object are also in SCE object
all(colnames(seuAdt) %in% colnames(cells.dsb.norm))[1] TRUE# Match up and subset Seurat and SCE objects
m <- match(colnames(seuAdt), colnames(cells.dsb.norm))
cells.dsb.norm <- cells.dsb.norm[, m]
cell.adt.mtx <- cell.adt.mtx[, m]
# Check that cell IDs match
all(colnames(seuAdt) == colnames(cells.dsb.norm))[1] TRUE# Add ADT data to Seurat object
# Create a new assay to store ADT information
adt.raw <- CreateAssayObject(counts = cell.adt.mtx)
adt.dsb <- CreateAssayObject(counts = cells.dsb.norm)
# add this assay to the previously created Seurat object
seuAdt[["ADT.raw"]] <- adt.raw
seuAdt[["ADT.dsb"]] <- adt.dsb
# Validate that the object now contains multiple assays
seuAdtAn object of class Seurat
19442 features across 18474 samples within 3 assays
Active assay: RNA (19120 features, 0 variable features)
2 other assays present: ADT.raw, ADT.dsb used (Mb) gc trigger (Mb) max used (Mb)
Ncells 12516199 668.5 38124784 2036.1 47655980 2545.2
Vcells 1699493140 12966.2 2887796642 22032.2 2842229724 21684.52.5 Integrate ADT data
out <- here("data/SCEs/04_C133_Neeland.adt_integrated.rds")
if(!file.exists(out)){
DefaultAssay(seuAdt) <- "ADT.raw"
seuAdt <- intDat(seuAdt, type = "ADT", int.assay.name = "int.adt.raw")
DefaultAssay(seuAdt) <- "ADT.dsb"
seuAdt <- intDat(seuAdt, type = "ADT", adt.norm = "DSB",
int.assay.name = "int.adt.dsb")
saveRDS(seuAdt, file = out)
} else {
seuAdt <- readRDS(out)
}2.6 Cluster ADT
2.6.1 Raw with CLR normalisation
# define proteins to use in clustering (non-isptype controls)
prots <- rownames(cells.dsb.norm)[!rownames(cells.dsb.norm) %in% isotype.controls]
DefaultAssay(seuAdt) <- "int.adt.raw"
VariableFeatures(seuAdt) <- prots
seuAdt <- ScaleData(seuAdt) %>%
RunPCA(verbose = FALSE, dims = 1:30, reduction.name = "int.adt.raw.pca")
seuAdt <- FindNeighbors(object = seuAdt, dims = 1:30, assay = 'int.adt.raw',
k.param = 30, verbose = FALSE,
reduction = "int.adt.raw.pca")
seuAdt <- FindClusters(object = seuAdt, resolution = 1,
algorithm = 3, verbose = FALSE)
seuAdt <- RunUMAP(seuAdt, verbose = FALSE, dims = 1:30,
reduction = "int.adt.raw.pca",
reduction.name = "int.adt.raw.umap")
DimPlot(seuAdt, group.by = "seurat_clusters", label = TRUE,
label.size = 2.5, reduction = "int.adt.raw.umap") + NoLegend() -> p1
DimPlot(seuAdt, group.by = "predicted.ann_level_3", label = TRUE,
label.size = 2.5, reduction = "int.adt.raw.umap") + NoLegend() -> p2
DimPlot(seuAdt, group.by = "donor", reduction = "int.adt.raw.umap") -> p3
# make results dataframe
d <- cbind(seuAdt@meta.data,
as.data.frame(t(seuAdt@assays$ADT.raw@data)))
# calculate the median protein expression separately for each cluster
raw.adt.plot <- d %>%
dplyr::group_by(seurat_clusters) %>%
dplyr::summarize_at(.vars = prots, .funs = median) %>%
tibble::remove_rownames() %>%
tibble::column_to_rownames("seurat_clusters") 2.6.2 DSB normalised
DefaultAssay(seuAdt) <- "int.adt.dsb"
VariableFeatures(seuAdt) <- prots
seuAdt <- ScaleData(seuAdt) %>%
RunPCA(verbose = FALSE, dims = 1:30, reduction.name = "int.adt.dsb.pca")
seuAdt <- FindNeighbors(object = seuAdt, dims = 1:30, assay = 'int.adt.dsb',
k.param = 30, verbose = FALSE,
reduction = "int.adt.dsb.pca")
seuAdt <- FindClusters(object = seuAdt, resolution = 1,
algorithm = 3, verbose = FALSE)
seuAdt <- RunUMAP(seuAdt, verbose = FALSE, dims = 1:30,
reduction = "int.adt.dsb.pca",
reduction.name = "int.adt.dsb.umap")
DimPlot(seuAdt, group.by = "seurat_clusters", label = TRUE,
label.size = 2.5, reduction = "int.adt.dsb.umap") + NoLegend() -> p4
DimPlot(seuAdt, group.by = "predicted.ann_level_3", label = TRUE,
label.size = 2.5, reduction = "int.adt.dsb.umap") + NoLegend() -> p5
DimPlot(seuAdt, group.by = "donor", reduction = "int.adt.dsb.umap") -> p6
d <- cbind(seuAdt@meta.data,
as.data.frame(t(seuAdt@assays$ADT.dsb@data))) %>%
dplyr::group_by(seurat_clusters) %>%
dplyr::summarize_at(.vars = prots, .funs = median) %>%
tibble::remove_rownames() %>%
tibble::column_to_rownames("seurat_clusters") -> dsb.adt.plot((p1 + scale_color_paletteer_d("miscpalettes::pastel") |
p4 + scale_color_paletteer_d("miscpalettes::pastel")) /
((p2 + scale_color_paletteer_d("miscpalettes::pastel") |
p5 + scale_color_paletteer_d("miscpalettes::pastel")) +
plot_layout(guides = "collect")) /
((p3 | p6) +
plot_layout(guides = "collect"))) &
theme(legend.position = "bottom",
legend.text = element_text(size = 8))
read_csv(file = here("data/sample_sheets/TotalSeq-A_Universal_Cocktail_v1.0.csv")) -> dat
dat <- dat[dat$id %in% prots,]
all(colnames(raw.adt.plot) == dat$id)[1] TRUEpheatmap::pheatmap(t(raw.adt.plot),
color = viridis::viridis(25, option = "B"),
fontsize_row = 8, border_color = NA,
labels_row = gsub("anti-human", "", dat$name))
pheatmap::pheatmap(t(dsb.adt.plot),
color = viridis::viridis(25, option = "B"),
fontsize_row = 8, border_color = NA,
labels_row = gsub("anti-human", "", dat$name))
2.6.3 Visualise RNA
DefaultAssay(seuAdt) <- "RNA"
seuAdt <- NormalizeData(seuAdt) %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA(verbose = FALSE, dims = 1:30, reduction.name = "rna.pca") %>%
RunUMAP(verbose = FALSE, dims = 1:30, reduction = "rna.pca",
reduction.name = "rna.umap")DimPlot(seuAdt, group.by = "donor", combine = FALSE, reduction = "rna.umap")[[1]]
DimPlot(seuAdt, group.by = "predicted.ann_level_3",
combine = FALSE, reduction = "rna.umap")[[1]]
2.7 Integrate RNA data
DefaultAssay(seuAdt) <- "RNA"
out <- here("data/SCEs/04_C133_Neeland.all_integrated.SEU.rds")
if(!file.exists(out)) {
seuAdt <- intDat(seuAdt, type = "RNA")
seuAdt <- RunPCA(seuAdt, verbose = FALSE, dims = 1:30,
reduction.name = "rna.pca") %>%
RunUMAP(verbose = FALSE, dims = 1:30, reduction = "rna.pca",
reduction.name = "rna.umap")
saveRDS(seuAdt, file = out)
} else {
seuAdt <- readRDS(out)
}
DefaultAssay(seuAdt) <- "integrated"
DimPlot(seuAdt, group.by = "donor", combine = FALSE, reduction = "rna.umap")[[1]]
2.8 Map CF_BAL_Pilot to C133_Neeland and transfer protein data
out <- here("data/SCEs/05_CF_BAL_Pilot.transfer_adt.SEU.rds")
DefaultAssay(seuInt) <- "RNA"
seuPilot <- DietSeurat(seuInt[, seuInt$experiment == 1], assays = "RNA")
if(!file.exists(out)) {
seuSct <- SCTransform(seuPilot, method = "glmGamPoi")
anchors <- FindTransferAnchors(reference = seuAdt, query = seuSct,
dims = 1:30, reference.reduction = "rna.pca",
normalization.method = "SCT")
adt.raw <- TransferData(anchorset = anchors,
refdata = GetAssayData(seuAdt[["ADT.raw"]]), dims = 1:30)
adt.dsb <- TransferData(anchorset = anchors,
refdata = GetAssayData(seuAdt[["ADT.dsb"]]), dims = 1:30)
seuPilot[["ADT.raw"]] <- adt.raw
seuPilot[["ADT.dsb"]] <- adt.dsb
saveRDS(seuPilot, file = out)
} else {
seuPilot <- readRDS(out)
}2.9 Visualise transferred ADT data
2.9.1 By donor and HLCA v2.0 level 4 annotations
DefaultAssay(seuPilot) <- "ADT.raw"
VariableFeatures(seuPilot) <- prots
seuPilot <- ScaleData(seuPilot) %>%
RunPCA(verbose = FALSE, dims = 1:30, reduction.name = "adt.pca") %>%
RunUMAP(verbose = FALSE, dims = 1:30, reduction = "adt.pca",
reduction.name = "adt.umap")
DimPlot(seuPilot, group.by = "donor", repel = TRUE,
reduction = "adt.umap", label = TRUE, label.size = 2.5) 
DimPlot(seuPilot, group.by = "predicted.ann_level_4", repel = TRUE,
reduction = "adt.umap", label = TRUE, label.size = 2.5) & NoLegend()
### By various marker genes
markers <- read_csv(
here("data/sample_sheets/TotalSeq_A_Human_Universal_Cocktail_Proteins_of_interest_29.09.21.csv"))DefaultAssay(seuPilot) <- "RNA"
Idents(seuPilot) <- "predicted.ann_level_4"
options(ggrepel.max.overlaps = Inf)
p <- vector("list", nrow(markers))
for(i in 1:nrow(markers)){
if(markers$`Gene Name`[i] %in% rownames(seuPilot[["RNA"]]) &
markers$DNA_ID[i] %in% rownames(seuPilot[["ADT.raw"]])){
DefaultAssay(seuPilot) <- "ADT.raw"
p1 <- FeaturePlot(seuPilot, features = markers$DNA_ID[i], label = TRUE,
repel = TRUE, label.size = 2.5,
reduction = 'adt.umap', keep.scale = "all",
cols = c("lightgrey","darkgreen")) +
ggtitle(markers$`Gene Name`[i], subtitle = "ADT.raw") +
theme(title = element_text(size = 8),
axis.text = element_text(size = 8)) +
NoLegend()
DefaultAssay(seuPilot) <- "ADT.dsb"
p2 <- FeaturePlot(seuPilot, features = markers$DNA_ID[i], label = TRUE,
repel = TRUE, label.size = 2.5,
reduction = 'adt.umap', keep.scale = "all",
cols = c("lightgrey","darkblue")) +
ggtitle(markers$`Gene Name`[i], subtitle = "ADT.dsb") +
theme(title = element_text(size = 8),
axis.text = element_text(size = 8)) +
NoLegend()
DefaultAssay(seuPilot) <- "RNA"
p3 <- FeaturePlot(seuPilot, features = markers$`Gene Name`[i], label = TRUE,
repel = TRUE, label.size = 2.5,
reduction = 'adt.umap', keep.scale = "all",
cols = c("lightgrey","purple")) +
ggtitle(markers$`Gene Name`[i], subtitle = "RNA") +
theme(title = element_text(size = 8),
axis.text = element_text(size = 8)) +
NoLegend()
p[[i]] <- (p1 | p2 | p3)
}
}
p[!sapply(p, is.null)][[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
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2.10 Merge C133_Neeland and CF_BAL_Pilot data with ADTs
DefaultAssay(seuPilot) <- "RNA"
DefaultAssay(seuAdt) <- "RNA"
seuMerge <- merge(DietSeurat(seuPilot, assays = c("RNA", "ADT.dsb", "ADT.raw")),
y = DietSeurat(seuAdt, assays = c("RNA","ADT.dsb", "ADT.raw")))
seuMergeAn object of class Seurat
19442 features across 45590 samples within 3 assays
Active assay: RNA (19120 features, 0 variable features)
2 other assays present: ADT.raw, ADT.dsb3 Save data
out <- here(glue("data/SCEs/05_COMBO.clustered_annotated_adt_diet.SEU.rds"))
if(!file.exists(out)) {
saveRDS(seuMerge, file = out)
} 4 Session info
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.1.0 (2021-05-18)
os CentOS Linux 7 (Core)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Melbourne
date 2022-06-16
pandoc 2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
! package * version date (UTC) lib source
P abind 1.4-5 2016-07-21 [?] CRAN (R 4.1.0)
P AnnotationDbi 1.56.2 2021-11-09 [?] Bioconductor
P AnnotationFilter 1.18.0 2021-10-26 [?] Bioconductor
P assertthat 0.2.1 2019-03-21 [?] CRAN (R 4.1.0)
P backports 1.4.1 2021-12-13 [?] CRAN (R 4.1.0)
P beachmat 2.10.0 2021-10-26 [?] Bioconductor
P beeswarm 0.4.0 2021-06-01 [?] CRAN (R 4.1.0)
P Biobase * 2.54.0 2021-10-26 [?] Bioconductor
P BiocFileCache 2.2.0 2021-10-26 [?] Bioconductor
P BiocGenerics * 0.40.0 2021-10-26 [?] Bioconductor
P BiocIO 1.4.0 2021-10-26 [?] Bioconductor
P BiocManager 1.30.16 2021-06-15 [?] CRAN (R 4.1.0)
P BiocNeighbors 1.12.0 2021-10-26 [?] Bioconductor
P BiocParallel * 1.28.3 2021-12-09 [?] Bioconductor
P BiocSingular 1.10.0 2021-10-26 [?] Bioconductor
P BiocStyle * 2.22.0 2021-10-26 [?] Bioconductor
P biomaRt 2.50.1 2021-11-21 [?] Bioconductor
P Biostrings 2.62.0 2021-10-26 [?] Bioconductor
P bit 4.0.4 2020-08-04 [?] CRAN (R 4.1.0)
P bit64 4.0.5 2020-08-30 [?] CRAN (R 4.0.2)
P bitops 1.0-7 2021-04-24 [?] CRAN (R 4.0.2)
P blob 1.2.2 2021-07-23 [?] CRAN (R 4.1.0)
P bluster 1.4.0 2021-10-26 [?] Bioconductor
P bookdown 0.24 2021-09-02 [?] CRAN (R 4.1.0)
P broom 0.7.11 2022-01-03 [?] CRAN (R 4.1.0)
P bslib 0.3.1 2021-10-06 [?] CRAN (R 4.1.0)
P cachem 1.0.6 2021-08-19 [?] CRAN (R 4.1.0)
P callr 3.7.0 2021-04-20 [?] CRAN (R 4.1.0)
P cellranger 1.1.0 2016-07-27 [?] CRAN (R 4.1.0)
P cli 3.1.0 2021-10-27 [?] CRAN (R 4.1.0)
P cluster 2.1.2 2021-04-17 [?] CRAN (R 4.1.0)
P clustree * 0.4.4 2021-11-08 [?] CRAN (R 4.1.0)
P codetools 0.2-18 2020-11-04 [?] CRAN (R 4.1.0)
P colorspace 2.0-2 2021-06-24 [?] CRAN (R 4.0.2)
P cowplot 1.1.1 2020-12-30 [?] CRAN (R 4.0.2)
P crayon 1.4.2 2021-10-29 [?] CRAN (R 4.1.0)
P curl 4.3.2 2021-06-23 [?] CRAN (R 4.1.0)
P data.table 1.14.2 2021-09-27 [?] CRAN (R 4.1.0)
P DBI 1.1.2 2021-12-20 [?] CRAN (R 4.1.0)
P dbplyr 2.1.1 2021-04-06 [?] CRAN (R 4.1.0)
P DelayedArray 0.20.0 2021-10-26 [?] Bioconductor
P DelayedMatrixStats 1.16.0 2021-10-26 [?] Bioconductor
P deldir 1.0-6 2021-10-23 [?] CRAN (R 4.1.0)
P digest 0.6.29 2021-12-01 [?] CRAN (R 4.1.0)
P dplyr * 1.0.7 2021-06-18 [?] CRAN (R 4.1.0)
P dqrng 0.3.0 2021-05-01 [?] CRAN (R 4.1.0)
P DropletUtils * 1.14.1 2021-11-08 [?] Bioconductor
P dsb * 1.0.1 2022-03-14 [?] CRAN (R 4.1.0)
P edgeR 3.36.0 2021-10-26 [?] Bioconductor
P ellipsis 0.3.2 2021-04-29 [?] CRAN (R 4.0.2)
P ensembldb 2.18.2 2021-11-08 [?] Bioconductor
P evaluate 0.14 2019-05-28 [?] CRAN (R 4.0.2)
P fansi 1.0.0 2022-01-10 [?] CRAN (R 4.1.0)
P farver 2.1.0 2021-02-28 [?] CRAN (R 4.0.2)
P fastmap 1.1.0 2021-01-25 [?] CRAN (R 4.1.0)
P filelock 1.0.2 2018-10-05 [?] CRAN (R 4.1.0)
P fitdistrplus 1.1-6 2021-09-28 [?] CRAN (R 4.1.0)
P forcats * 0.5.1 2021-01-27 [?] CRAN (R 4.1.0)
P fs 1.5.2 2021-12-08 [?] CRAN (R 4.1.0)
P future 1.23.0 2021-10-31 [?] CRAN (R 4.1.0)
P future.apply 1.8.1 2021-08-10 [?] CRAN (R 4.1.0)
P generics 0.1.1 2021-10-25 [?] CRAN (R 4.1.0)
GenomeInfoDb * 1.30.1 2022-01-30 [1] Bioconductor
P GenomeInfoDbData 1.2.7 2021-12-21 [?] Bioconductor
P GenomicAlignments 1.30.0 2021-10-26 [?] Bioconductor
P GenomicFeatures 1.46.3 2021-12-30 [?] Bioconductor
P GenomicRanges * 1.46.1 2021-11-18 [?] Bioconductor
P getPass 0.2-2 2017-07-21 [?] CRAN (R 4.0.2)
P ggbeeswarm 0.6.0 2017-08-07 [?] CRAN (R 4.1.0)
P ggforce 0.3.3 2021-03-05 [?] CRAN (R 4.1.0)
P ggplot2 * 3.3.5 2021-06-25 [?] CRAN (R 4.0.2)
P ggraph * 2.0.5 2021-02-23 [?] CRAN (R 4.1.0)
P ggrepel 0.9.1 2021-01-15 [?] CRAN (R 4.1.0)
P ggridges 0.5.3 2021-01-08 [?] CRAN (R 4.1.0)
P git2r 0.29.0 2021-11-22 [?] CRAN (R 4.1.0)
P glmGamPoi * 1.6.0 2021-10-26 [?] Bioconductor
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P goftest 1.2-3 2021-10-07 [?] CRAN (R 4.1.0)
P graphlayouts 0.8.0 2022-01-03 [?] CRAN (R 4.1.0)
P gridExtra 2.3 2017-09-09 [?] CRAN (R 4.1.0)
P gtable 0.3.0 2019-03-25 [?] CRAN (R 4.1.0)
P haven 2.4.3 2021-08-04 [?] CRAN (R 4.1.0)
P HDF5Array 1.22.1 2021-11-14 [?] Bioconductor
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hexbin 1.28.2 2021-01-08 [1] CRAN (R 4.1.0)
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P htmlwidgets 1.5.4 2021-09-08 [?] CRAN (R 4.1.0)
P httpuv 1.6.5 2022-01-05 [?] CRAN (R 4.1.0)
P httr 1.4.2 2020-07-20 [?] CRAN (R 4.1.0)
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P igraph 1.2.11 2022-01-04 [?] CRAN (R 4.1.0)
P IRanges * 2.28.0 2021-10-26 [?] Bioconductor
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P KEGGREST 1.34.0 2021-10-26 [?] Bioconductor
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P knitr 1.37 2021-12-16 [?] CRAN (R 4.1.0)
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P later 1.3.0 2021-08-18 [?] CRAN (R 4.1.0)
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P lubridate 1.8.0 2021-10-07 [?] CRAN (R 4.1.0)
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P parallelly 1.30.0 2021-12-17 [?] CRAN (R 4.1.0)
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P pheatmap 1.0.12 2019-01-04 [?] CRAN (R 4.1.0)
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P polyclip 1.10-0 2019-03-14 [?] CRAN (R 4.1.0)
P prettyunits 1.1.1 2020-01-24 [?] CRAN (R 4.0.2)
P prismatic 1.1.0 2021-10-17 [?] CRAN (R 4.1.0)
P processx 3.5.2 2021-04-30 [?] CRAN (R 4.1.0)
P progress 1.2.2 2019-05-16 [?] CRAN (R 4.1.0)
P promises 1.2.0.1 2021-02-11 [?] CRAN (R 4.0.2)
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P ps 1.6.0 2021-02-28 [?] CRAN (R 4.1.0)
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P R.methodsS3 1.8.1 2020-08-26 [?] CRAN (R 4.0.2)
P R.oo 1.24.0 2020-08-26 [?] CRAN (R 4.0.2)
P R.utils 2.11.0 2021-09-26 [?] CRAN (R 4.1.0)
P R6 2.5.1 2021-08-19 [?] CRAN (R 4.1.0)
P RANN 2.6.1 2019-01-08 [?] CRAN (R 4.1.0)
P rappdirs 0.3.3 2021-01-31 [?] CRAN (R 4.0.2)
P RColorBrewer 1.1-2 2014-12-07 [?] CRAN (R 4.0.2)
P Rcpp 1.0.7 2021-07-07 [?] CRAN (R 4.1.0)
P RcppAnnoy 0.0.19 2021-07-30 [?] CRAN (R 4.1.0)
RCurl 1.98-1.6 2022-02-08 [1] CRAN (R 4.1.0)
P readr * 2.1.1 2021-11-30 [?] CRAN (R 4.1.0)
P readxl 1.3.1 2019-03-13 [?] CRAN (R 4.1.0)
P rematch2 2.1.2 2020-05-01 [?] CRAN (R 4.1.0)
P renv 0.15.0-14 2022-01-10 [?] Github (rstudio/renv@a3b90eb)
P reprex 2.0.1 2021-08-05 [?] CRAN (R 4.1.0)
P reshape2 1.4.4 2020-04-09 [?] CRAN (R 4.1.0)
P restfulr 0.0.13 2017-08-06 [?] CRAN (R 4.1.0)
P reticulate 1.22 2021-09-17 [?] CRAN (R 4.1.0)
P rhdf5 2.38.0 2021-10-26 [?] Bioconductor
P rhdf5filters 1.6.0 2021-10-26 [?] Bioconductor
P Rhdf5lib 1.16.0 2021-10-26 [?] Bioconductor
P rjson 0.2.21 2022-01-09 [?] CRAN (R 4.1.0)
P rlang 0.4.12 2021-10-18 [?] CRAN (R 4.1.0)
P rmarkdown 2.11 2021-09-14 [?] CRAN (R 4.1.0)
P ROCR 1.0-11 2020-05-02 [?] CRAN (R 4.1.0)
P rpart 4.1-15 2019-04-12 [?] CRAN (R 4.1.0)
P rprojroot 2.0.2 2020-11-15 [?] CRAN (R 4.0.2)
P Rsamtools 2.10.0 2021-10-26 [?] Bioconductor
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P RSQLite 2.2.9 2021-12-06 [?] CRAN (R 4.1.0)
P rstudioapi 0.13 2020-11-12 [?] CRAN (R 4.0.2)
P rsvd 1.0.5 2021-04-16 [?] CRAN (R 4.1.0)
P rtracklayer 1.54.0 2021-10-26 [?] Bioconductor
P Rtsne 0.15 2018-11-10 [?] CRAN (R 4.1.0)
P rvest 1.0.2 2021-10-16 [?] CRAN (R 4.1.0)
P S4Vectors * 0.32.3 2021-11-21 [?] Bioconductor
P sass 0.4.0 2021-05-12 [?] CRAN (R 4.1.0)
P ScaledMatrix 1.2.0 2021-10-26 [?] Bioconductor
P scales 1.1.1 2020-05-11 [?] CRAN (R 4.0.2)
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P scattermore 0.7 2020-11-24 [?] CRAN (R 4.1.0)
P scran * 1.22.1 2021-11-14 [?] Bioconductor
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P SeuratObject * 4.0.4 2021-11-23 [?] CRAN (R 4.1.0)
P shiny 1.7.1 2021-10-02 [?] CRAN (R 4.1.0)
P SingleCellExperiment * 1.16.0 2021-10-26 [?] Bioconductor
P sparseMatrixStats 1.6.0 2021-10-26 [?] Bioconductor
P spatstat.core 2.3-2 2021-11-26 [?] CRAN (R 4.1.0)
P spatstat.data 2.1-2 2021-12-17 [?] CRAN (R 4.1.0)
P spatstat.geom 2.3-1 2021-12-10 [?] CRAN (R 4.1.0)
P spatstat.sparse 2.1-0 2021-12-17 [?] CRAN (R 4.1.0)
P spatstat.utils 2.3-0 2021-12-12 [?] CRAN (R 4.1.0)
P statmod 1.4.36 2021-05-10 [?] CRAN (R 4.1.0)
P stringi 1.7.6 2021-11-29 [?] CRAN (R 4.1.0)
P stringr * 1.4.0 2019-02-10 [?] CRAN (R 4.0.2)
P SummarizedExperiment * 1.24.0 2021-10-26 [?] Bioconductor
P survival 3.2-13 2021-08-24 [?] CRAN (R 4.1.0)
P tensor 1.5 2012-05-05 [?] CRAN (R 4.1.0)
P tibble * 3.1.6 2021-11-07 [?] CRAN (R 4.1.0)
P tidygraph 1.2.0 2020-05-12 [?] CRAN (R 4.0.2)
P tidyr * 1.1.4 2021-09-27 [?] CRAN (R 4.1.0)
P tidyselect 1.1.1 2021-04-30 [?] CRAN (R 4.1.0)
P tidyverse * 1.3.1 2021-04-15 [?] CRAN (R 4.1.0)
P tweenr 1.0.2 2021-03-23 [?] CRAN (R 4.1.0)
P tzdb 0.2.0 2021-10-27 [?] CRAN (R 4.1.0)
P utf8 1.2.2 2021-07-24 [?] CRAN (R 4.1.0)
P uwot 0.1.11 2021-12-02 [?] CRAN (R 4.1.0)
P vctrs 0.3.8 2021-04-29 [?] CRAN (R 4.0.2)
P vipor 0.4.5 2017-03-22 [?] CRAN (R 4.1.0)
P viridis 0.6.2 2021-10-13 [?] CRAN (R 4.1.0)
P viridisLite 0.4.0 2021-04-13 [?] CRAN (R 4.0.2)
P vroom 1.5.7 2021-11-30 [?] CRAN (R 4.1.0)
P whisker 0.4 2019-08-28 [?] CRAN (R 4.0.2)
P withr 2.4.3 2021-11-30 [?] CRAN (R 4.1.0)
P workflowr * 1.7.0 2021-12-21 [?] CRAN (R 4.1.0)
P xfun 0.29 2021-12-14 [?] CRAN (R 4.1.0)
P XML 3.99-0.8 2021-09-17 [?] CRAN (R 4.1.0)
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P yaml 2.2.1 2020-02-01 [?] CRAN (R 4.0.2)
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P zoo 1.8-9 2021-03-09 [?] CRAN (R 4.1.0)
[1] /oshlack_lab/jovana.maksimovic/projects/MCRI/melanie.neeland/paed-cf-cite-seq/renv/library/R-4.1/x86_64-pc-linux-gnu
[2] /config/binaries/R/4.1.0/lib64/R/library
P ── Loaded and on-disk path mismatch.
──────────────────────────────────────────────────────────────────────────────
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /config/binaries/R/4.1.0/lib64/R/lib/libRblas.so
LAPACK: /config/binaries/R/4.1.0/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] dsb_1.0.1 paletteer_1.4.0
[3] BiocParallel_1.28.3 glmGamPoi_1.6.0
[5] clustree_0.4.4 ggraph_2.0.5
[7] patchwork_1.1.1 SeuratObject_4.0.4
[9] Seurat_4.0.6 scater_1.22.0
[11] scran_1.22.1 scuttle_1.4.0
[13] DropletUtils_1.14.1 SingleCellExperiment_1.16.0
[15] SummarizedExperiment_1.24.0 Biobase_2.54.0
[17] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[19] IRanges_2.28.0 S4Vectors_0.32.3
[21] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
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[31] tidyr_1.1.4 tibble_3.1.6
[33] ggplot2_3.3.5 tidyverse_1.3.1
[35] BiocStyle_2.22.0 workflowr_1.7.0
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[5] bit64_4.0.5 knitr_1.37
[7] irlba_2.3.5 DelayedArray_0.20.0
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[11] rpart_4.1-15 AnnotationFilter_1.18.0
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[17] ScaledMatrix_1.2.0 callr_3.7.0
[19] cowplot_1.1.1 RSQLite_2.2.9
[21] RANN_2.6.1 future_1.23.0
[23] bit_4.0.4 tzdb_0.2.0
[25] spatstat.data_2.1-2 xml2_1.3.3
[27] lubridate_1.8.0 httpuv_1.6.5
[29] assertthat_0.2.1 viridis_0.6.2
[31] xfun_0.29 hms_1.1.1
[33] jquerylib_0.1.4 evaluate_0.14
[35] promises_1.2.0.1 progress_1.2.2
[37] restfulr_0.0.13 fansi_1.0.0
[39] dbplyr_2.1.1 readxl_1.3.1
[41] igraph_1.2.11 DBI_1.1.2
[43] htmlwidgets_1.5.4 spatstat.geom_2.3-1
[45] ellipsis_0.3.2 RSpectra_0.16-0
[47] backports_1.4.1 bookdown_0.24
[49] prismatic_1.1.0 biomaRt_2.50.1
[51] deldir_1.0-6 sparseMatrixStats_1.6.0
[53] vctrs_0.3.8 ensembldb_2.18.2
[55] ROCR_1.0-11 abind_1.4-5
[57] cachem_1.0.6 withr_2.4.3
[59] ggforce_0.3.3 vroom_1.5.7
[61] sctransform_0.3.3 GenomicAlignments_1.30.0
[63] prettyunits_1.1.1 mclust_5.4.9
[65] goftest_1.2-3 cluster_2.1.2
[67] lazyeval_0.2.2 crayon_1.4.2
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[73] ProtGenerics_1.26.0 nlme_3.1-153
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[77] globals_0.14.0 lifecycle_1.0.1
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[83] rsvd_1.0.5 cellranger_1.1.0
[85] rprojroot_2.0.2 polyclip_1.10-0
[87] lmtest_0.9-39 Matrix_1.4-0
[89] Rhdf5lib_1.16.0 zoo_1.8-9
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[99] viridisLite_0.4.0 bitops_1.0-7
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[103] KernSmooth_2.23-20 rhdf5filters_1.6.0
[105] Biostrings_2.62.0 blob_1.2.2
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[109] beachmat_2.10.0 scales_1.1.1
[111] memoise_2.0.1 hexbin_1.28.2
[113] magrittr_2.0.1 plyr_1.8.6
[115] ica_1.0-2 zlibbioc_1.40.0
[117] compiler_4.1.0 BiocIO_1.4.0
[119] dqrng_0.3.0 RColorBrewer_1.1-2
[121] fitdistrplus_1.1-6 Rsamtools_2.10.0
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[129] mgcv_1.8-38 tidyselect_1.1.1
[131] stringi_1.7.6 highr_0.9
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[145] metapod_1.2.0 gridExtra_2.3
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[149] digest_0.6.29 BiocManager_1.30.16
[151] shiny_1.7.1 Rcpp_1.0.7
[153] broom_0.7.11 later_1.3.0
[155] RcppAnnoy_0.0.19 AnnotationDbi_1.56.2
[157] httr_1.4.2 colorspace_2.0-2
[159] XML_3.99-0.8 rvest_1.0.2
[161] fs_1.5.2 tensor_1.5
[163] reticulate_1.22 splines_4.1.0
[165] uwot_0.1.11 statmod_1.4.36
[167] rematch2_2.1.2 spatstat.utils_2.3-0
[169] graphlayouts_0.8.0 renv_0.15.0-14
[171] sessioninfo_1.2.2 plotly_4.10.0
[173] xtable_1.8-4 jsonlite_1.7.2
[175] tidygraph_1.2.0 R6_2.5.1
[177] pillar_1.6.4 htmltools_0.5.2
[179] mime_0.12 fastmap_1.1.0
[181] BiocNeighbors_1.12.0 codetools_0.2-18
[183] utf8_1.2.2 lattice_0.20-45
[185] bslib_0.3.1 spatstat.sparse_2.1-0
[187] curl_4.3.2 ggbeeswarm_0.6.0
[189] leiden_0.3.9 survival_3.2-13
[191] limma_3.50.0 rmarkdown_2.11
[193] munsell_0.5.0 rhdf5_2.38.0
[195] GenomeInfoDbData_1.2.7 HDF5Array_1.22.1
[197] haven_2.4.3 reshape2_1.4.4
[199] gtable_0.3.0 spatstat.core_2.3-2