Analysis of scRNA-seq & CITE-seq Data Combined
Combine and Annotate Data
Jovana Maksimovic
June 16, 2022
Last updated: 2022-06-16
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Knit directory:
paed-cf-cite-seq/
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1 Load libraries
2 Load Data
Load the processed CF_BAL_Pilot and C133_Neeland data sets.
# load preprocessed data
sce1 <- readRDS(here("data", "SCEs", "04_CF_BAL_Pilot.preprocessed.SCE.rds"))
sce2 <- readRDS(here("data", "SCEs", "03_C133_Neeland.preprocessed.SCE.rds"))
# append letter to cell IDs to denote experiment & avoid duplicate IDs
colnames(sce1) <- paste0("A-", colnames(sce1))
colnames(sce2) <- paste0("B-", colnames(sce2))
# identify shared genes
shared_genes <- intersect(rowData(sce1)$ID, rowData(sce2)$ID)
# sort & subset each SCE relative to shared genes
m1 <- match(shared_genes, rowData(sce1)$ID)
all(shared_genes == rowData(sce1)$ID[m1])[1] TRUEsce1 <- sce1[m1,]
# sort & subset each SCE relative to shared genes
m2 <- match(shared_genes, rowData(sce2)$ID)
all(shared_genes == rowData(sce2)$ID[m2])[1] TRUEsce2 <- sce2[m2,]
# create combined matrix of counts
combo_counts <- cbind(counts(sce1), counts(sce2))
# combine cell metadata
combo_data <- DataFrame(
bind_rows(colData(sce1) %>%
data.frame %>%
dplyr::select(Barcode, capture,
nuclear_fraction, cell_status),
colData(sce2) %>%
data.frame %>%
dplyr::select(Barcode, capture,
nuclear_fraction, cell_status)))
combo_data$HTO <- as.factor(c(as.character(sce1$capture),
as.character(sce2$HTO)))
combo_data$donor <- as.factor(c(as.character(sce1$capture),
as.character(sce2$genetic_donor)))
combo_data$experiment <- as.factor(c(rep(1, ncol(sce1)),
rep(2, ncol(sce2))))
sce <- SingleCellExperiment(
list(counts = combo_counts),
colData = combo_data,
rowData = rowData(sce1))
sceclass: SingleCellExperiment
dim: 32732 54106
metadata(0):
assays(1): counts
rownames(32732): MIR1302-2HG FAM138A ... AC213203.1 FAM231C
rowData names(16): ID Symbol ... NCBI.REFSEQ NCBI.SYMBOL
colnames(54106): A-1_AAACCCAAGCTAGTTC-1 A-1_AAACCCACAAGATTGA-1 ...
B-2_TTTGTTGTCATTGGTG-1 B-2_TTTGTTGTCGATGGAG-1
colData names(7): Barcode capture ... donor experiment
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):3 Quality controls
3.1 Identify uninformative genes
Identify genes that are not informative for cell clustering or downstream marker gene identification. For example, genes without gene symbols or duplicated gene symbols, mitochondrial genes, ribosomal genes and sex chromosome genes are not considered informative.
# Some useful gene sets
mito_set <- rownames(sce)[which(rowData(sce)$CHR == "MT")]
ribo_set <- grep("^RP(S|L)", rownames(sce), value = TRUE)
# NOTE: A more curated approach for identifying ribosomal protein genes
# (https://github.com/Bioconductor/OrchestratingSingleCellAnalysis-base/blob/ae201bf26e3e4fa82d9165d8abf4f4dc4b8e5a68/feature-selection.Rmd#L376-L380)
library(msigdbr)
c2_sets <- msigdbr(species = "Homo sapiens", category = "C2")
ribo_set <- union(
ribo_set,
c2_sets[c2_sets$gs_name == "KEGG_RIBOSOME", ]$human_gene_symbol)
is_ribo <- rownames(sce) %in% ribo_set
sex_set <- rownames(sce)[any(rowData(sce)$ENSEMBL.SEQNAME %in% c("X", "Y"))]
pseudogene_set <- rownames(sce)[
any(grepl("pseudogene", rowData(sce)$ENSEMBL.GENEBIOTYPE))]3.2 Calculate quality control metrics
Calculate various quality control metrics for each cell.
is_mito <- rownames(sce) %in% mito_set
is_ribo <- rownames(sce) %in% ribo_set
sce <- addPerCellQC(
sce,
subsets = list(Mito = which(is_mito), Ribo = which(is_ribo)))
head(colData(sce)) %>% knitr::kable()Calculate the percentage of zero counts for each cell.
sce$zero_percent <- colSums(counts(sce) == 0)/nrow(sce)
summary(sce$zero_percent) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.7278 0.8554 0.8830 0.8974 0.9481 0.9994 3.3 Visualise QC metrics
Figure 3.1 shows that the vast majority of samples are good-quality:
- The median library size is around 17,4001.
- The median number of genes detected is around 3,800.
- The median percentage of UMIs that are mapped to mitochondrial RNA is around 6%
- The percentage of UMIs that are mapped to ribosomal protein genes is 12%.
p1 <- plotColData(
sce,
"sum",
x = "capture",
colour_by = "experiment",
point_size = 0.5) +
scale_y_log10() +
theme(axis.text.x = element_text(size = 6)) +
annotation_logticks(
sides = "l",
short = unit(0.03, "cm"),
mid = unit(0.06, "cm"),
long = unit(0.09, "cm"))
p2 <- plotColData(
sce,
"detected",
x = "capture",
colour_by = "experiment",
point_size = 0.5) +
theme(axis.text.x = element_text(size = 6))
p3 <- plotColData(
sce,
"subsets_Mito_percent",
x = "capture",
colour_by = "experiment",
point_size = 0.5) +
theme(axis.text.x = element_text(size = 6))
p4 <- plotColData(
sce,
"subsets_Ribo_percent",
x = "capture",
colour_by = "experiment",
point_size = 0.5) +
theme(axis.text.x = element_text(size = 6))
p5 <- plotColData(
sce,
x = "capture",
y = "zero_percent",
colour_by = "experiment",
point_size = 0.5) +
theme(axis.text.x = element_text(size = 6))
((p1 | p2) / (p3 | p4) / p5) +
plot_layout(guides = "collect") &
theme(legend.position = "bottom")
Figure 3.1: Distributions of various QC metrics for all cells in the dataset. This includes the library sizes, number of genes detected, and percentage of reads mapped to mitochondrial genes.
p1 <- plotColData(
sce,
"sum",
x = "donor",
colour_by = "experiment",
point_size = 0.5) +
scale_y_log10() +
theme(axis.text.x = element_text(angle = 90)) +
annotation_logticks(
sides = "l",
short = unit(0.03, "cm"),
mid = unit(0.06, "cm"),
long = unit(0.09, "cm"))
p2 <- plotColData(
sce,
"detected",
x = "donor",
colour_by = "experiment",
point_size = 0.5) +
theme(axis.text.x = element_text(angle = 90,
hjust = 0.5,
vjust = 1))
p3 <- plotColData(
sce,
"subsets_Mito_percent",
x = "donor",
colour_by = "experiment",
point_size = 0.5) +
theme(axis.text.x = element_text(angle = 90))
p4 <- plotColData(
sce,
"subsets_Ribo_percent",
x = "donor",
colour_by = "experiment",
point_size = 0.5) +
theme(axis.text.x = element_text(angle = 90))
p5 <- plotColData(
sce,
x = "donor",
y = "zero_percent",
colour_by = "experiment",
point_size = 0.5) +
theme(axis.text.x = element_text(angle = 90))
((p1 | p2) / (p3 | p4) / p5) +
plot_layout(guides = "collect") &
theme(legend.position = "bottom")
Figure 3.2: Distributions of various QC metrics for all cells in the dataset. This includes the library sizes, number of genes detected, and percentage of reads mapped to mitochondrial genes.
plotColData(
sce,
x = "sum",
y = "detected",
other_fields = "capture",
colour_by = "zero_percent",
point_size = 0.25,
point_alpha = 0.25) +
facet_wrap(vars(capture), ncol = 2)
plotColData(
sce,
x = "detected",
y = "subsets_Mito_percent",
other_fields = "capture",
colour_by = "zero_percent",
point_size = 0.25,
point_alpha = 0.25) +
facet_wrap(vars(capture), ncol = 2)
plotColData(
sce,
x = "sum",
y = "subsets_Ribo_percent",
other_fields = "capture",
colour_by = "zero_percent",
point_size = 0.25,
point_alpha = 0.25) +
scale_x_log10() +
facet_wrap(vars(capture), ncol = 2)
plotColData(
sce,
x = "sum",
y = "subsets_Mito_percent",
other_fields = "capture",
colour_by = "cell_status",
point_size = 0.25,
point_alpha = 0.25) +
scale_x_log10() +
facet_wrap(vars(capture), ncol = 2)
colData(sce) %>%
data.frame %>%
ggplot(aes(x = cell_status, y = sum, fill = cell_status)) +
geom_violin(scale = "count", size = 0.25) +
scale_y_log10() +
facet_wrap(~ capture, scales = "free_y")
3.4 Discard uninformative genes & cells
Remove uninformative genes and filter out low quality cells:
(1) cells that are called doublets or unassigned based on genetic assignment,
(2) cells that are called doublets based on HTO assignment and,
(3) cells that were called empty droplets by DropletQC in the scRNA-seq data only.
uninformative <- is_mito | is_ribo | rownames(sce) %in% sex_set | rownames(sce) %in% pseudogene_set
sum(uninformative)[1] 1558junk <- sce$donor %in% c("doublet", "unassigned") | sce$HTO %in% "Doublet" | (sce$cell_status == "empty_droplet" & sce$experiment == 1)
sceFlt <- sce[!uninformative, !junk]
sceFltclass: SingleCellExperiment
dim: 31174 45590
metadata(0):
assays(1): counts
rownames(31174): MIR1302-2HG FAM138A ... AC213203.1 FAM231C
rowData names(16): ID Symbol ... NCBI.REFSEQ NCBI.SYMBOL
colnames(45590): A-1_AAACCCAAGCTAGTTC-1 A-1_AAACCCACAAGATTGA-1 ...
B-2_TTTGTTGCATCGAGCC-1 B-2_TTTGTTGTCGATGGAG-1
colData names(17): Barcode capture ... total zero_percent
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):3.5 Remove low-abundance genes
Keep only genes that are expressed in at least 20 cells. On average, this means that we will only be able to identify clusters with >20 cells.
numCells <- nexprs(sceFlt, byrow = TRUE)
keep <- numCells > 20
sum(keep)[1] 19120Keep the genes that meet the criteria.
sceFlt <- sceFlt[keep,]
sceFltclass: SingleCellExperiment
dim: 19120 45590
metadata(0):
assays(1): counts
rownames(19120): AL627309.1 AL669831.5 ... AC004556.1 AC240274.1
rowData names(16): ID Symbol ... NCBI.REFSEQ NCBI.SYMBOL
colnames(45590): A-1_AAACCCAAGCTAGTTC-1 A-1_AAACCCACAAGATTGA-1 ...
B-2_TTTGTTGCATCGAGCC-1 B-2_TTTGTTGTCGATGGAG-1
colData names(17): Barcode capture ... total zero_percent
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):3.6 Convert to Seurat object
Convert SingleCellExperiment object to a SeuratObject.
counts <- counts(sceFlt)
rownames(counts) <- rowData(sceFlt)$Symbol
seu <- CreateSeuratObject(counts = counts,
meta.data = data.frame(colData(sceFlt)))
seuAn object of class Seurat
19120 features across 45590 samples within 1 assay
Active assay: RNA (19120 features, 0 variable features) used (Mb) gc trigger (Mb) max used (Mb)
Ncells 8718371 465.7 14316903 764.7 14316903 764.7
Vcells 536602696 4094.0 1993226295 15207.2 3244341485 24752.43.7 Visualise combined, filtered data
DefaultAssay(seu) <- "RNA"
seu <- NormalizeData(seu) %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA(verbose = FALSE, dims = 1:30) %>%
RunUMAP(verbose = FALSE, dims = 1:30)DimPlot(seu, group.by = "experiment", combine = FALSE)[[1]]
DimPlot(seu, split.by = "experiment", combine = FALSE)[[1]]
4 Integrate data
Normalise the data using SCTransform and integrate across batches/individuals.
out <- here("data/SCEs/04_COMBO.integrated.SEU.rds")
if(!file.exists(out)) {
seuInt <- intDat(seu, split = "donor", type = "RNA",
reference = unique(as.character(seu$capture[seu$experiment == 1])))
saveRDS(seuInt, file = out)
} else {
seuInt <- readRDS(out)
} used (Mb) gc trigger (Mb) max used (Mb)
Ncells 8773844 468.6 14316903 764.7 14316903 764.7
Vcells 2300603946 17552.3 2911065156 22209.7 3244341485 24752.44.1 Visualise integrated data
seuInt <- RunPCA(seuInt, npcs = 30, verbose = FALSE)
seuInt <- RunUMAP(seuInt, verbose = FALSE, dims = 1:30)
DimPlot(seuInt, group.by = "experiment", combine = FALSE)[[1]]
5 Cluster data
5.1 Perform linear dimensional reduction
p1 <- DimPlot(seuInt, reduction = "pca", group.by = "donor")
p2 <- DimPlot(seuInt, reduction = "pca", dims = c(1,3), group.by = "donor")
p3 <- DimPlot(seuInt, reduction = "pca", dims = c(2,3), group.by = "donor")
p4 <- DimPlot(seuInt, reduction = "pca", dims = c(3,4), group.by = "donor")
((p1 | p2) / (p3 | p4)) + plot_layout(guides = "collect") &
theme(legend.text = element_text(size = 8),
plot.title = element_text(size = 10),
axis.title = element_text(size = 9),
axis.text = element_text(size = 8))
DimHeatmap(seuInt, dims = 1:30, cells = 500, balanced = TRUE)
5.2 Determine the dimensionality of the dataset
ElbowPlot(seuInt, ndims = 30)
5.3 Cluster the cells
out <- here("data/SCEs/04_COMBO.clustered.SEU.rds")
if(!file.exists(out)) {
seuInt <- FindNeighbors(seuInt, reduction = "pca", dims = 1:30)
seuInt <- FindClusters(seuInt, algorithm = 3,
resolution = seq(0.1, 1, by = 0.1))
seuInt <- RunUMAP(seuInt, dims = 1:30)
saveRDS(seuInt, file = out)
} else {
seuInt <- readRDS(out)
}5.4 Visualise clustering at default resolution
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE,
label.size = 2.5) + NoLegend()
5.5 Clustree resolution visualisation
clustree(seuInt, prefix = "integrated_snn_res.")
6 Annotate data using Zilionis reference
6.1 Load Zilionis reference data
Sys.setenv("VROOM_CONNECTION_SIZE" = 1000000)
files <- list.files(here::here("data/GSE127465_RAW"),
full.names = TRUE,
pattern = "raw_counts")
library <- limma::strsplit2(limma::strsplit2(files, "human_")[,2],
"_raw")[,1]
out <- here::here("data/GSE127465_RAW/raw_dgCMatrix.rds")
if(!file.exists(out)){
raw <- lapply(files, function(f){
message(f)
vroom::vroom(f) %>%
tibble::column_to_rownames("barcode") %>%
t() %>%
as("dgCMatrix")
})
rawCmp <- purrr::reduce(raw, cbind)
times <- sapply(raw, ncol)
libTagged <- paste0(rep(library, times), "_", colnames(rawCmp))
colnames(rawCmp) <- libTagged
saveRDS(rawCmp, out)
} else {
rawCmp <- readRDS(out)
}
metadata <- vroom::vroom(here::here("data",
"GSE127465_RAW",
"GSE127465_human_cell_metadata_54773x25.tsv.gz")) %>%
dplyr::filter(Library %in% library) %>%
dplyr::mutate(ID = paste0(Library, "_", Barcode))
m <- match(metadata$ID, colnames(rawCmp))
subRaw <- rawCmp[, m]
all(colnames(subRaw) == metadata$ID)[1] TRUEzilionisRaw <- CreateSeuratObject(counts = subRaw,
meta.data = data.frame(metadata,
row.names = metadata$ID))
zilionisRaw <- zilionisRaw[, !grepl("specific", zilionisRaw$Major.cell.type)]
zilionisRawAn object of class Seurat
41861 features across 24495 samples within 1 assay
Active assay: RNA (41861 features, 0 variable features)6.2 Normalise Zilionis reference data using SCTranscorm
out <- here("data/SCEs/02_ZILIONIS.sct_normalised.SEU.rds")
if(!file.exists(out)) {
zilSct <- SCTransform(zilionisRaw, method = "glmGamPoi")
zilSct <- RunPCA(zilSct, verbose = FALSE, dims = 1:30)
zilSct <- FindNeighbors(zilSct, reduction = "pca", dims = 1:20)
zilSct <- FindClusters(zilSct, algorithm = 3)
zilSct <- RunUMAP(zilSct, dims = 1:30, reduction = "pca",
return.model = TRUE)
saveRDS(zilSct, file = out)
} else {
zilSct <- readRDS(out)
} used (Mb) gc trigger (Mb) max used (Mb)
Ncells 9051961 483.5 14316903 764.7 14316903 764.7
Vcells 3728408350 28445.5 6036814145 46057.3 4192106805 31983.36.3 Map data to Zilionis reference
out <- here("data/SCEs/04_COMBO.zilionis_mapped.SEU.rds")
if(!file.exists(out)) {
anchors <- FindTransferAnchors(reference = zilSct, query = seuInt,
dims = 1:30, reference.reduction = "pca",
normalization.method = "SCT")
seuInt <- MapQuery(anchorset = anchors, reference = zilSct,
query = seuInt,
refdata = list(celltype = "Major.cell.type"),
reference.reduction = "pca",
reduction.model = "umap")
saveRDS(seuInt, file = out)
} else {
seuInt <- readRDS(out)
}6.4 Visualise reference mapping
p1 <- DimPlot(zilSct, reduction = "umap", group.by = "Major.cell.type",
label = TRUE, label.size = 2.5, repel = TRUE) +
NoLegend() +
ggtitle("Reference annotations") +
theme(axis.text = element_text(size = 8),
axis.title = element_text(size = 8),
title = element_text(size = 9))
p2 <- DimPlot(seuInt, reduction = "ref.umap",
group.by = "predicted.celltype", label = TRUE,
label.size = 2.5, repel = TRUE) +
NoLegend() +
ggtitle("Query transferred labels") +
theme(axis.text = element_text(size = 8),
axis.title = element_text(size = 8),
title = element_text(size = 9))
p1 + p2
6.5 Explore reference annotations
6.5.1 Plot QC metrics by label
options(scipen=1)
seuInt@meta.data %>%
ggplot(aes(y = predicted.celltype.score,
x = predicted.celltype,
fill = predicted.celltype)) +
geom_violin(scale = "width") +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
NoLegend() -> p1
seuInt@meta.data %>%
ggplot(aes(y = nCount_RNA,
x = predicted.celltype,
fill = predicted.celltype)) +
geom_violin(scale = "area") +
scale_y_log10() +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
NoLegend() -> p2
seuInt@meta.data %>%
ggplot(aes(y = nFeature_RNA,
x = predicted.celltype,
fill = predicted.celltype)) +
geom_violin(scale = "area") +
scale_y_log10() +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
NoLegend() -> p3
(p1 / p2 / p3)
6.5.2 Visualise labels on clustering UMAP
DimPlot(seuInt, reduction = 'umap',
label = TRUE, repel = TRUE,
label.size = 2.5,
group.by = "predicted.celltype") + NoLegend()
7 Annotate data using Azimuth & Human Lung Cell Reference v1.0
7.1 Add Azimuth labels
Save filtered data and upload to Azimuth for annotation with Human Lung Cell reference. Add Azimuth labels to data.
out <- here("data/SCEs/04_COMBO.clustered_diet.SEU.rds")
if(!file.exists(out)){
DefaultAssay(seuInt) <- "RNA"
seuDiet <- DietSeurat(seuInt, assays = "RNA")
saveRDS(seuDiet, out)
} else {
seuInt <- AddAzimuthResults(seuInt,
filename = here("data/SCEs/03_COMBO.clustered_azimuth.SEU.rds"))
seuInt$predicted.annotation.l1 <- fct_drop(seuInt$predicted.annotation.l1)
}
if(any(grepl("predicted.annotation.l1",
colnames(seuInt@meta.data)))) table(seuInt$predicted.annotation.l1)
Basophil/Mast Ciliated
48 384
Alveolar Epithelial Type 2 Bronchial Vessel
19 87
Capillary Intermediate Basal
134 363
Dendritic CD16+ Monocyte
2494 24
CD14+ Monocyte Smooth Muscle
620 1
CD8 T CD4 T
2416 3578
Plasmacytoid Dendritic B
141 1403
Macrophage Vein
32039 37
Artery Mucous
12 10
Lymphatic Proliferating Macrophage
28 758
Proliferating NK/T Natural Killer T
102 257
Natural Killer Plasma
121 33
Neuroendocrine Ionocyte
4 412
Goblet
65 7.2 Visualise reference mapping
DimPlot(seuInt, reduction = 'umap.proj',
label = TRUE, repel = TRUE, label.size = 2.5,
group.by = "predicted.annotation.l1") + NoLegend()
DimPlot(seuInt, reduction = 'umap',
label = TRUE, repel = TRUE, label.size = 2.5,
group.by = "predicted.annotation.l1") + NoLegend()
7.3 Explore reference annotations
7.3.1 Plot QC metrics by label
options(scipen=1)
seuInt@meta.data %>%
ggplot(aes(y = predicted.annotation.l1.score,
x = predicted.annotation.l1,
fill = predicted.annotation.l1)) +
geom_violin(scale = "width") +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
NoLegend() -> p1
seuInt@meta.data %>%
ggplot(aes(y = nCount_RNA,
x = predicted.annotation.l1,
fill = predicted.annotation.l1)) +
geom_violin(scale = "area") +
scale_y_log10() +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
NoLegend() -> p2
seuInt@meta.data %>%
ggplot(aes(y = nFeature_RNA,
x = predicted.annotation.l1,
fill = predicted.annotation.l1)) +
geom_violin(scale = "area") +
scale_y_log10() +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
NoLegend() -> p3
(p1 / p2 / p3)
8 Annotate data using Azimuth & Human Lung Cell Reference v2.0
8.1 Add Azimuth labels
Upload previously saved filtered data to Azimuth for annotation with Human Lung Cell reference version 2.0. Add Azimuth labels to data.
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)
table(seuInt$predicted.ann_level_1) %>% knitr::kable()| Var1 | Freq |
|---|---|
| Endothelial | 322 |
| Epithelial | 1800 |
| Immune | 43468 |
table(seuInt$predicted.ann_level_2) %>% knitr::kable()| Var1 | Freq |
|---|---|
| Airway epithelium | 1488 |
| Alveolar epithelium | 299 |
| Blood vessels | 325 |
| Lymphatic EC | 2 |
| Lymphoid | 7955 |
| Myeloid | 35521 |
table(seuInt$predicted.ann_level_3) %>% knitr::kable()| Var1 | Freq |
|---|---|
| AT1 | 369 |
| AT2 | 1 |
| B cell lineage | 1492 |
| Basal | 541 |
| Dendritic cells | 2081 |
| EC arterial | 27 |
| EC capillary | 121 |
| EC venous | 187 |
| Innate lymphoid cell NK | 120 |
| Lymphatic EC mature | 11 |
| Macrophages | 33161 |
| Mast cells | 51 |
| Monocytes | 204 |
| Multiciliated lineage | 386 |
| Rare | 99 |
| Secretory | 397 |
| T cell lineage | 6342 |
table(seuInt$predicted.ann_level_4) %>% knitr::kable()| Var1 | Freq |
|---|---|
| Alveolar macrophages | 28722 |
| B cells | 1471 |
| Basal resting | 244 |
| CD4 T cells | 3020 |
| CD8 T cells | 3237 |
| Classical monocytes | 122 |
| Club | 258 |
| DC1 | 63 |
| DC2 | 1789 |
| Deuterosomal | 2 |
| EC general capillary | 135 |
| EC venous pulmonary | 10 |
| EC venous systemic | 171 |
| Goblet | 120 |
| Interstitial macrophages | 4403 |
| Ionocyte | 1 |
| Migratory DCs | 120 |
| Multiciliated | 384 |
| NK cells | 120 |
| Non-classical monocytes | 62 |
| None | 505 |
| Plasma cells | 37 |
| Plasmacytoid DCs | 143 |
| SMG duct | 2 |
| Suprabasal | 276 |
| T cells proliferating | 74 |
| Tuft | 99 |
table(seuInt$predicted.ann_finest_level) %>% knitr::kable()| Var1 | Freq |
|---|---|
| Alveolar macrophages | 27390 |
| Alveolar Mφ CCL3+ | 573 |
| Alveolar Mφ proliferating | 686 |
| AT1 | 400 |
| AT2 | 1 |
| B cells | 1471 |
| Basal resting | 245 |
| CD4 T cells | 3020 |
| CD8 T cells | 3237 |
| Classical monocytes | 133 |
| Club (nasal) | 230 |
| Club (non-nasal) | 32 |
| DC1 | 63 |
| DC2 | 1909 |
| Deuterosomal | 2 |
| EC arterial | 27 |
| EC general capillary | 145 |
| EC venous pulmonary | 10 |
| EC venous systemic | 171 |
| Goblet (bronchial) | 1 |
| Goblet (nasal) | 116 |
| Interstitial Mφ perivascular | 267 |
| Ionocyte | 2 |
| Lymphatic EC mature | 12 |
| Mast cells | 51 |
| Migratory DCs | 120 |
| Monocyte-derived Mφ | 4054 |
| Multiciliated (nasal) | 1 |
| Multiciliated (non-nasal) | 383 |
| NK cells | 120 |
| Non-classical monocytes | 86 |
| Plasma cells | 37 |
| Plasmacytoid DCs | 143 |
| SMG duct | 2 |
| Suprabasal | 276 |
| T cells proliferating | 74 |
| Tuft | 100 |
8.2 Visualise reference mapping
azimuth_results <- readRDS(here("data/SCEs/03_COMBO.clustered_azimuth.SEU.rds"))
seuInt[["umap.proj.v1"]] <- azimuth_results$umap
DimPlot(seuInt, reduction = 'umap.proj.v1',
label = TRUE, repel = TRUE, label.size = 2.5,
group.by = "predicted.annotation.l1") + NoLegend()
DimPlot(seuInt, reduction = 'umap.proj',
label = TRUE, repel = TRUE, label.size = 2.5,
group.by = "predicted.ann_level_1") + NoLegend()
DimPlot(seuInt, reduction = 'umap.proj',
label = TRUE, repel = TRUE, label.size = 2.5,
group.by = "predicted.ann_level_2") + NoLegend()
DimPlot(seuInt, reduction = 'umap.proj',
label = TRUE, repel = TRUE, label.size = 2.5,
group.by = "predicted.ann_level_3") + NoLegend()
DimPlot(seuInt, reduction = 'umap.proj',
label = TRUE, repel = TRUE, label.size = 2.5,
group.by = "predicted.ann_level_4") + NoLegend()
DimPlot(seuInt, reduction = 'umap.proj',
label = TRUE, repel = TRUE, label.size = 2.5,
group.by = "predicted.ann_finest_level") + NoLegend()
## Visualise cell type abundance
labels <- colnames(seuInt@meta.data)[grepl("(?!.*score$)predicted.ann",
colnames(seuInt@meta.data),
perl = TRUE)]
p <- vector("list",length(labels))
for(label in labels){
seuInt@meta.data %>%
ggplot(aes(x = !!sym(label), fill = !!sym(label))) +
geom_bar() +
geom_text(aes(label = ..count..), stat = "count",
vjust = -0.5, colour = "black", size = 2) +
scale_y_log10() +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank()) +
NoLegend() +
labs(y = "No. Cells (log scale)") -> p1
seuInt@meta.data %>%
dplyr::select(!!sym(label), experiment) %>%
group_by(!!sym(label), experiment) %>%
summarise(num = n()) %>%
mutate(prop = num / sum(num)) %>%
ggplot(aes(x = !!sym(label), y = prop * 100,
fill = experiment)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1)) +
labs(y = "% Cells", fill = "Experiment") -> p2
p1 / p2 -> p[[label]]
}
p[[1]]
NULL
[[2]]
NULL
[[3]]
NULL
[[4]]
NULL
[[5]]
NULL
[[6]]
NULL
[[7]]
NULL
$predicted.annotation.l1
$predicted.ann_level_1
$predicted.ann_level_2
$predicted.ann_level_3
$predicted.ann_level_4
$predicted.ann_level_5
$predicted.ann_finest_level
## Visualise annotation relasionships and quality
check <- levels(seuInt$predicted.annotation.l1)
p <- vector("list", length(check))
for(i in 1:length(p)){
seuInt@meta.data %>%
dplyr::filter(predicted.annotation.l1 == check[i]) %>%
ggplot(aes(x = predicted.ann_level_3.score,
colour = predicted.ann_level_3)) +
geom_density() +
ggtitle(paste0("Cluster ", check[i])) +
theme(legend.key.size = unit(4, "pt")) -> p1
seuInt@meta.data %>%
dplyr::filter(predicted.annotation.l1 == check[i]) %>%
ggplot(aes(x = predicted.ann_level_3,
fill = predicted.ann_level_3)) +
geom_bar() +
geom_text(aes(label = ..count..), stat = "count",
vjust = -0.5, colour = "black", size = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
NoLegend() -> p2
seuInt@meta.data %>%
dplyr::filter(predicted.annotation.l1 == check[i]) %>%
ggplot(aes(x = predicted.ann_finest_level.score,
colour = predicted.ann_finest_level)) +
geom_density() +
ggtitle(paste0("Cluster ", check[i])) +
theme(legend.key.size = unit(4, "pt")) -> p3
seuInt@meta.data %>%
dplyr::filter(predicted.annotation.l1 == check[i]) %>%
ggplot(aes(x = predicted.ann_finest_level,
fill = predicted.ann_finest_level)) +
geom_bar() +
geom_text(aes(label = ..count..), stat = "count",
vjust = -0.5, colour = "black", size = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
NoLegend() -> p4
seuInt@meta.data %>%
dplyr::filter(predicted.annotation.l1 == check[i]) %>%
ggplot(aes(x = predicted.ann_level_4.score,
colour = predicted.ann_level_4)) +
geom_density() +
ggtitle(paste0("Cluster ", check[i])) +
theme(legend.key.size = unit(4, "pt")) -> p5
seuInt@meta.data %>%
dplyr::filter(predicted.annotation.l1 == check[i]) %>%
ggplot(aes(x = predicted.ann_level_4,
fill = predicted.ann_level_4)) +
geom_bar() +
geom_text(aes(label = ..count..), stat = "count",
vjust = -0.5, colour = "black", size = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
NoLegend() -> p6
((p1 | p2) / (p5 | p6) / (p3 | p4)) +
plot_annotation(title = check[i]) &
theme(text = element_text(size = 8)) -> p[[i]]
}
p[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
[[19]]
[[20]]
[[21]]
[[22]]
[[23]]
[[24]]
[[25]]
[[26]]
[[27]]
9 Save data
labels <- levels(factor(seuInt$predicted.ann_level_3))
macrophages <- c("Macrophages")
tcells <- c("T cell lineage", "Innate lymphoid cell NK")
lung <- c("AT1", "EC arterial", "Rare", "Secretory", "Basal",
"EC venous", "Multiciliated lineage", "EC capillary",
"Lymphatic EC mature", "AT2")
subList <- list(macrophages = macrophages,
tcells = tcells,
lung = lung,
others = labels[!labels %in% c(macrophages, tcells, lung)])
Idents(seuInt) <- "predicted.ann_level_3"
for(sub in names(subList)){
out <- here(glue("data/SCEs/05_COMBO.clustered_annotated_{sub}_diet.SEU.rds"))
message(sub)
if(!file.exists(out)){
saveRDS(DietSeurat(subset(seuInt,
idents = subList[[sub]]),
assays = "RNA",
dimreducs = NULL,
graphs = NULL), out)
}
}10 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 babelgene 21.4 2021-04-26 [?] 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 checkmate 2.0.0 2020-02-06 [?] CRAN (R 4.0.2)
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 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)
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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
P globals 0.14.0 2020-11-22 [?] CRAN (R 4.0.2)
P glue * 1.6.0 2021-12-17 [?] CRAN (R 4.1.0)
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
P here * 1.0.1 2020-12-13 [?] CRAN (R 4.0.2)
P highr 0.9 2021-04-16 [?] CRAN (R 4.1.0)
P hms 1.1.1 2021-09-26 [?] CRAN (R 4.1.0)
P htmltools 0.5.2 2021-08-25 [?] CRAN (R 4.1.0)
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)
P ica 1.0-2 2018-05-24 [?] CRAN (R 4.1.0)
P igraph 1.2.11 2022-01-04 [?] CRAN (R 4.1.0)
P IRanges * 2.28.0 2021-10-26 [?] Bioconductor
P irlba 2.3.5 2021-12-06 [?] CRAN (R 4.1.0)
P jquerylib 0.1.4 2021-04-26 [?] CRAN (R 4.1.0)
P jsonlite 1.7.2 2020-12-09 [?] CRAN (R 4.0.2)
P KEGGREST 1.34.0 2021-10-26 [?] Bioconductor
P KernSmooth 2.23-20 2021-05-03 [?] CRAN (R 4.1.0)
P knitr 1.37 2021-12-16 [?] CRAN (R 4.1.0)
P labeling 0.4.2 2020-10-20 [?] CRAN (R 4.0.2)
P later 1.3.0 2021-08-18 [?] CRAN (R 4.1.0)
P lattice 0.20-45 2021-09-22 [?] CRAN (R 4.1.0)
P lazyeval 0.2.2 2019-03-15 [?] CRAN (R 4.1.0)
P leiden 0.3.9 2021-07-27 [?] CRAN (R 4.1.0)
P lifecycle 1.0.1 2021-09-24 [?] CRAN (R 4.1.0)
P limma 3.50.0 2021-10-26 [?] Bioconductor
P listenv 0.8.0 2019-12-05 [?] CRAN (R 4.1.0)
P lmtest 0.9-39 2021-11-07 [?] CRAN (R 4.1.0)
P locfit 1.5-9.4 2020-03-25 [?] CRAN (R 4.1.0)
P lubridate 1.8.0 2021-10-07 [?] CRAN (R 4.1.0)
P magrittr 2.0.1 2020-11-17 [?] CRAN (R 4.0.2)
P MASS 7.3-53.1 2021-02-12 [?] CRAN (R 4.0.2)
P Matrix 1.4-0 2021-12-08 [?] CRAN (R 4.1.0)
P MatrixGenerics * 1.6.0 2021-10-26 [?] Bioconductor
P matrixStats * 0.61.0 2021-09-17 [?] CRAN (R 4.1.0)
P memoise 2.0.1 2021-11-26 [?] CRAN (R 4.1.0)
P metapod 1.2.0 2021-10-26 [?] Bioconductor
P mgcv 1.8-38 2021-10-06 [?] CRAN (R 4.1.0)
P mime 0.12 2021-09-28 [?] CRAN (R 4.1.0)
P miniUI 0.1.1.1 2018-05-18 [?] CRAN (R 4.1.0)
P modelr 0.1.8 2020-05-19 [?] CRAN (R 4.0.2)
P msigdbr * 7.4.1 2021-05-05 [?] CRAN (R 4.1.0)
P munsell 0.5.0 2018-06-12 [?] CRAN (R 4.1.0)
P nlme 3.1-153 2021-09-07 [?] CRAN (R 4.1.0)
P parallelly 1.30.0 2021-12-17 [?] CRAN (R 4.1.0)
P patchwork * 1.1.1 2020-12-17 [?] CRAN (R 4.0.2)
P pbapply 1.5-0 2021-09-16 [?] CRAN (R 4.1.0)
P pillar 1.6.4 2021-10-18 [?] CRAN (R 4.1.0)
P pkgconfig 2.0.3 2019-09-22 [?] CRAN (R 4.1.0)
P plotly 4.10.0 2021-10-09 [?] CRAN (R 4.1.0)
P plyr 1.8.6 2020-03-03 [?] CRAN (R 4.0.2)
P png 0.1-7 2013-12-03 [?] CRAN (R 4.1.0)
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 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)
P ProtGenerics 1.26.0 2021-10-26 [?] Bioconductor
P ps 1.6.0 2021-02-28 [?] CRAN (R 4.1.0)
P purrr * 0.3.4 2020-04-17 [?] CRAN (R 4.0.2)
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 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
P RSpectra 0.16-0 2019-12-01 [?] CRAN (R 4.1.0)
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)
P scater * 1.22.0 2021-10-26 [?] Bioconductor
P scattermore 0.7 2020-11-24 [?] CRAN (R 4.1.0)
P scran * 1.22.1 2021-11-14 [?] Bioconductor
P sctransform 0.3.3 2022-01-13 [?] CRAN (R 4.1.0)
P scuttle * 1.4.0 2021-10-26 [?] Bioconductor
P sessioninfo 1.2.2 2021-12-06 [?] CRAN (R 4.1.0)
P Seurat * 4.0.6 2021-12-16 [?] CRAN (R 4.1.0)
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)
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P tensor 1.5 2012-05-05 [?] CRAN (R 4.1.0)
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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)
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P yaml 2.2.1 2020-02-01 [?] CRAN (R 4.0.2)
P zlibbioc 1.40.0 2021-10-26 [?] Bioconductor
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] msigdbr_7.4.1 BiocParallel_1.28.3
[3] glmGamPoi_1.6.0 clustree_0.4.4
[5] ggraph_2.0.5 patchwork_1.1.1
[7] scales_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
[23] matrixStats_0.61.0 glue_1.6.0
[25] here_1.0.1 forcats_0.5.1
[27] stringr_1.4.0 dplyr_1.0.7
[29] purrr_0.3.4 readr_2.1.1
[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
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 rtracklayer_1.54.0
[3] scattermore_0.7 R.methodsS3_1.8.1
[5] bit64_4.0.5 knitr_1.37
[7] irlba_2.3.5 DelayedArray_0.20.0
[9] R.utils_2.11.0 data.table_1.14.2
[11] rpart_4.1-15 AnnotationFilter_1.18.0
[13] KEGGREST_1.34.0 RCurl_1.98-1.6
[15] generics_0.1.1 GenomicFeatures_1.46.3
[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 babelgene_21.4
[35] evaluate_0.14 promises_1.2.0.1
[37] progress_1.2.2 restfulr_0.0.13
[39] fansi_1.0.0 dbplyr_2.1.1
[41] readxl_1.3.1 igraph_1.2.11
[43] DBI_1.1.2 htmlwidgets_1.5.4
[45] spatstat.geom_2.3-1 ellipsis_0.3.2
[47] RSpectra_0.16-0 backports_1.4.1
[49] bookdown_0.24 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] checkmate_2.0.0 sctransform_0.3.3
[63] GenomicAlignments_1.30.0 prettyunits_1.1.1
[65] goftest_1.2-3 cluster_2.1.2
[67] lazyeval_0.2.2 crayon_1.4.2
[69] labeling_0.4.2 edgeR_3.36.0
[71] pkgconfig_2.0.3 tweenr_1.0.2
[73] ProtGenerics_1.26.0 nlme_3.1-153
[75] vipor_0.4.5 rlang_0.4.12
[77] globals_0.14.0 lifecycle_1.0.1
[79] miniUI_0.1.1.1 filelock_1.0.2
[81] BiocFileCache_2.2.0 modelr_0.1.8
[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
[91] reprex_2.0.1 beeswarm_0.4.0
[93] whisker_0.4 ggridges_0.5.3
[95] processx_3.5.2 rjson_0.2.21
[97] png_0.1-7 viridisLite_0.4.0
[99] bitops_1.0-7 getPass_0.2-2
[101] R.oo_1.24.0 KernSmooth_2.23-20
[103] rhdf5filters_1.6.0 Biostrings_2.62.0
[105] blob_1.2.2 DelayedMatrixStats_1.16.0
[107] parallelly_1.30.0 beachmat_2.10.0
[109] memoise_2.0.1 magrittr_2.0.1
[111] plyr_1.8.6 ica_1.0-2
[113] zlibbioc_1.40.0 compiler_4.1.0
[115] BiocIO_1.4.0 dqrng_0.3.0
[117] RColorBrewer_1.1-2 fitdistrplus_1.1-6
[119] Rsamtools_2.10.0 cli_3.1.0
[121] XVector_0.34.0 listenv_0.8.0
[123] pbapply_1.5-0 ps_1.6.0
[125] MASS_7.3-53.1 mgcv_1.8-38
[127] tidyselect_1.1.1 stringi_1.7.6
[129] highr_0.9 yaml_2.2.1
[131] BiocSingular_1.10.0 locfit_1.5-9.4
[133] ggrepel_0.9.1 grid_4.1.0
[135] sass_0.4.0 tools_4.1.0
[137] future.apply_1.8.1 parallel_4.1.0
[139] rstudioapi_0.13 bluster_1.4.0
[141] git2r_0.29.0 metapod_1.2.0
[143] gridExtra_2.3 farver_2.1.0
[145] Rtsne_0.15 digest_0.6.29
[147] BiocManager_1.30.16 shiny_1.7.1
[149] Rcpp_1.0.7 broom_0.7.11
[151] later_1.3.0 RcppAnnoy_0.0.19
[153] AnnotationDbi_1.56.2 httr_1.4.2
[155] colorspace_2.0-2 XML_3.99-0.8
[157] rvest_1.0.2 fs_1.5.2
[159] tensor_1.5 reticulate_1.22
[161] splines_4.1.0 uwot_0.1.11
[163] statmod_1.4.36 spatstat.utils_2.3-0
[165] graphlayouts_0.8.0 renv_0.15.0-14
[167] sessioninfo_1.2.2 plotly_4.10.0
[169] xtable_1.8-4 jsonlite_1.7.2
[171] tidygraph_1.2.0 R6_2.5.1
[173] pillar_1.6.4 htmltools_0.5.2
[175] mime_0.12 fastmap_1.1.0
[177] BiocNeighbors_1.12.0 codetools_0.2-18
[179] utf8_1.2.2 lattice_0.20-45
[181] bslib_0.3.1 spatstat.sparse_2.1-0
[183] curl_4.3.2 ggbeeswarm_0.6.0
[185] leiden_0.3.9 survival_3.2-13
[187] limma_3.50.0 rmarkdown_2.11
[189] munsell_0.5.0 rhdf5_2.38.0
[191] GenomeInfoDbData_1.2.7 HDF5Array_1.22.1
[193] haven_2.4.3 reshape2_1.4.4
[195] gtable_0.3.0 spatstat.core_2.3-2 This is consistent with the use of UMI counts rather than read counts, as each transcript molecule can only produce one UMI count but can yield many reads after fragmentation.↩︎