Master_metadata
Combined plots for all batches
Gunjan Dixit
February 04, 2025
Last updated: 2025-02-04
Checks: 5 2
Knit directory: paed-airway-allTissues/
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Load libraries
suppressPackageStartupMessages({
library(BiocStyle)
library(tidyverse)
library(here)
library(dplyr)
library(Seurat)
library(clustree)
library(paletteer)
library(viridis)
library(ggforce)
library(ggridges)
library(kableExtra)
library(RColorBrewer)
library(data.table)
library(dplyr)
library(cowplot)
library(ggplot2)
library(paletteer)
library(patchwork)
library(harmony)
library(BiocParallel)
library(circlize)
})data_path <- here("~/projects/paed-airway-atlas/airway-atlas-allTissues/paed-airway-allTissues/output/RDS/AllBatches_Annotation_SEUs_v2")
tissue_list <- list.files(data_path, pattern = "\\.rds$", full.names = TRUE)
tissue_list[1] "/Users/dixitgunjan/projects/paed-airway-atlas/airway-atlas-allTissues/paed-airway-allTissues/output/RDS/AllBatches_Annotation_SEUs_v2/G000231_Neeland_Adenoids.annotated_clusters.SEU.rds"
[2] "/Users/dixitgunjan/projects/paed-airway-atlas/airway-atlas-allTissues/paed-airway-allTissues/output/RDS/AllBatches_Annotation_SEUs_v2/G000231_Neeland_BAL.annotated_clusters.SEU.rds"
[3] "/Users/dixitgunjan/projects/paed-airway-atlas/airway-atlas-allTissues/paed-airway-allTissues/output/RDS/AllBatches_Annotation_SEUs_v2/G000231_Neeland_Bronchial_brushings.annotated_clusters.SEU.rds"
[4] "/Users/dixitgunjan/projects/paed-airway-atlas/airway-atlas-allTissues/paed-airway-allTissues/output/RDS/AllBatches_Annotation_SEUs_v2/G000231_Neeland_Nasal_brushings.annotated_clusters.SEU.rds"
[5] "/Users/dixitgunjan/projects/paed-airway-atlas/airway-atlas-allTissues/paed-airway-allTissues/output/RDS/AllBatches_Annotation_SEUs_v2/G000231_Neeland_Tonsils.annotated_clusters.SEU.rds" metadata_list <- list()
for (tissue in tissue_list) {
seu <- readRDS(tissue)
metadata <- seu@meta.data
metadata$source <- basename(tissue)
metadata_list[[tissue]] <- metadata
}
combined_metadata <- bind_rows(metadata_list)sort(table(combined_metadata$cell_labels_v2), decreasing = T)
Naïve B cells Memory B cells
87050 51442
Macrophages Non-ciliated epithelial cells
37383 34992
CD4 TFH Ciliated epithelial cells
25963 23752
CD4 TN DZtoLZ GCB transition
17887 17762
TFH-LZ-GC Naïve B cell-IFN
17170 14607
Plasma B cells DZ G2Mphase
14373 13881
Early MBC DZ GCB
13055 12961
CD8 TRM DZ early Sphase
12317 10212
CD8 TF Monocyte and neutrophil-like
9481 9318
DZ late Sphase CD8 TN
8618 8398
CD4 Treg-eff Early GC-committed NBC
8293 8086
T-IFN Cycling GCB
7501 7288
Naïve B cells activated CD4 TCM
6632 6562
Monocytes/macrophages Neutrophils
5481 5095
Intermediate B cells CD4 TEM
4749 4479
CD8 TEM IFN-activated cells
3909 3687
GC-commited metabolic activation Monocytes
3613 3548
Pre-BCRi II NK cells
3452 3352
Unconventional T cells Plasmacytoid DCs
3331 3316
CD4 effector Follicular dendritic cells
2944 2811
Dendritic cells Macro-CCL
2494 2434
CD4 Treg B activated
2296 2045
Double negative T Early PC precursor
2026 2020
Mast cells Macro-proliferating
1877 1813
Proliferating epithelial cells Gamma delta T
1799 1657
Pre-MBC/BC NK/gamma-delta T
1651 1546
Secretory epithelial cells CD4 T proliferating
1142 1086
Proliferating B cells Proliferating T/NK
1086 1075
csMBC FCRL4/5+ Pre-T cells
960 937
NK-T cells Erythroid cells
832 802
Epithelial cells B cells
779 726
DZ GCB Noproli-memory like MAIT cells
694 603
Ionocytes Cycling T
558 536
CD8 T GCB-IFN
387 324
Basal epithelial cells Melanocyte
206 205
Mesothelial cells
199 unique(combined_metadata$cell_labels_v2) [1] "Naïve B cells" "Memory B cells"
[3] "Plasma B cells" "Naïve B cell-IFN"
[5] "Monocytes/macrophages" "Follicular dendritic cells"
[7] "Pre-BCRi II" "Plasmacytoid DCs"
[9] "Epithelial cells" "Mast cells"
[11] "Neutrophils" "DZtoLZ GCB transition"
[13] "DZ early Sphase" "Early MBC"
[15] "Early GC-committed NBC" "DZ G2Mphase"
[17] "DZ GCB Noproli-memory like" "DZ GCB"
[19] "DZ late Sphase" "GC-commited metabolic activation"
[21] "csMBC FCRL4/5+" "Cycling GCB"
[23] "Early PC precursor" "Pre-T cells"
[25] "CD4 T proliferating" "CD4 TN"
[27] "CD4 TFH" "T-IFN"
[29] "CD4 TCM" "NK-T cells"
[31] "CD4 Treg-eff" "TFH-LZ-GC"
[33] "CD8 TF" "CD8 TN"
[35] "NK/gamma-delta T" "Double negative T"
[37] "B cells" "Basal epithelial cells"
[39] "Macrophages" "Macro-proliferating"
[41] "Secretory epithelial cells" "Dendritic cells"
[43] "Ciliated epithelial cells" "Macro-CCL"
[45] "Monocytes" "Pre-MBC/BC"
[47] "Proliferating B cells" "B activated"
[49] "CD4 TEM" "CD4 Treg"
[51] "CD8 TRM" "CD8 TEM"
[53] "NK cells" "CD8 T"
[55] "Proliferating T/NK" "MAIT cells"
[57] "Mesothelial cells" "Monocyte and neutrophil-like"
[59] "Non-ciliated epithelial cells" "Ionocytes"
[61] "Intermediate B cells" "Unconventional T cells"
[63] "Melanocyte" "Proliferating epithelial cells"
[65] "IFN-activated cells" "Erythroid cells"
[67] "Naïve B cells activated" "GCB-IFN"
[69] "CD4 effector" "Gamma delta T"
[71] "Cycling T" unique(combined_metadata$Broad_cell_label_3) [1] "B cells" "Dendritic cells"
[3] "Macrophages" "Doublet query/Other"
[5] "Epithelial lineage" "Granulocytes"
[7] "CD4 T cells" "Gamma delta T cells"
[9] "Double negative T cells" "CD8 T cells"
[11] "Pre B/T cells" "Cycling T cells"
[13] "Innate lymphoid cells" "Natural Killer cells"
[15] "Other" "Monocytes"
[17] "Neuroendocrine" "SMG duct"
[19] "Fibroblast lineage" "Endothelial lineage" Exploratory figures
To see cell counts across all Tissues
cell_type_counts <- sort(table(combined_metadata$cell_labels_v2), decreasing = TRUE) %>%
as.data.frame() %>%
rename(CellType = Var1, Count = Freq)
a <- ggplot(cell_type_counts, aes(x = reorder(CellType, Count), y = Count)) +
geom_bar(stat = "identity", fill = "purple3") +
geom_text(aes(label = Count), hjust = -0.1, size = 3) + # Position the text just outside the bar
coord_flip() +
labs(title = "Cell Type counts in earlyAIR Atlas", x = "Cell Types", y = "Cell Count") +
theme_minimal()
a
cell_type_tissue_counts <- combined_metadata %>%
group_by(cell_labels_v2, tissue) %>%
summarise(Count = n(), .groups = 'drop') %>%
rename(CellType = cell_labels_v2)
total_counts <- cell_type_tissue_counts %>%
group_by(CellType) %>%
summarise(TotalCount = sum(Count)) %>%
arrange(desc(TotalCount))
cell_type_tissue_counts$CellType <- factor(cell_type_tissue_counts$CellType, levels = rev(total_counts$CellType))
ggplot(cell_type_tissue_counts, aes(x = CellType, y = Count, fill = tissue)) +
geom_bar(stat = "identity") +
geom_text(data = total_counts, aes(x = CellType, y = TotalCount, label = TotalCount),
hjust = -0.1, size = 3, inherit.aes = FALSE) +
coord_flip() +
scale_fill_brewer(palette = "Set3") +
labs(title = "Cell Type Distribution by Tissue in earlyAIR Atlas", x = "Cell Types", y = "Cell Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
ggplot(cell_type_tissue_counts, aes(x = CellType, y = Count, fill = tissue)) +
geom_bar(stat = "identity") +
coord_flip() +
geom_text(aes(label = Count), position = position_stack(vjust = 0.5), size = 2.5) +
scale_fill_brewer(palette = "Set3") + # Customize palette
labs(title = "Cell Type Distribution by Tissue", x = "Cell Types", y = "Cell Count", fill = "Tissue") +
theme_minimal() +
theme(axis.text.y = element_text(size = 8))ggplot(combined_metadata, aes(x = cell_labels_v2, fill = tissue)) +
geom_bar(position = "dodge") +
facet_wrap(~tissue, scales = "free_y") +
coord_flip() +
labs(title = "Cell Type Counts by Tissue", x = "Cell Types", y = "Count") +
theme_minimal()
ggplot(combined_metadata, aes(x = tissue, fill = cell_labels_v2)) +
geom_bar(position = "stack") +
geom_text(stat = "count", aes(label = ..count..), position = position_stack(vjust = 0.5), size = 2.5) +
labs(title = "Cell Type Counts per Tissue", x = "Tissue", y = "Count") +
theme_minimal()#set.seed(012025)
#n <- 71
n <- length(unique(combined_metadata$cell_labels_v2))
qual_col_pals <- brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector <- unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
sampled_colors <- sample(col_vector, n, replace = TRUE)
cell_types <- unique(combined_metadata$cell_labels_v2)
#color_palette <- setNames(sampled_colors[1:length(cell_types)], cell_types)
color_palette <- readRDS(here("output/RDS/color_palette_unique.rds"))
proportion_df <- combined_metadata %>%
group_by(tissue, cell_labels_v2) %>%
summarise(Count = n()) %>%
mutate(Proportion = Count / sum(Count))`summarise()` has grouped output by 'tissue'. You can override using the
`.groups` argument.sampled_colors_1 <- c("#5c248b", "#1f57a6", "#ffec34", "#00960f", "#BC80BD", "#f06ab9", "#85d519", "#758dc4", "#89c5df", "#5da3cd", "#ffffba", "#009260", "#ffa037", "#A65628", "#E31A1C", "#377EB8" ,"#FDC086", "#FC8D62" ,"#FDDAEC", "#E78AC3")
proportion_df <- combined_metadata %>%
group_by(tissue, Broad_cell_label_3) %>%
summarise(Count = n()) %>%
mutate(Proportion = Count / sum(Count))`summarise()` has grouped output by 'tissue'. You can override using the
`.groups` argument.tissue_order <- c("Tonsils", "Adenoids", "Nasal_brushings", "Bronchial_brushings", "BAL")
proportion_df$tissue <- factor(proportion_df$tissue, levels = tissue_order)
p_stacked <- ggplot(proportion_df, aes(x = tissue, y = Proportion, fill = Broad_cell_label_3)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = sampled_colors_1) +
ylab("Proportion of Cell Labels") +
theme_cowplot(font_size = 10) +
labs(title = "Proportion of Cell Labels per Tissue") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)) # Center the title
print(p_stacked)
combined_metadata <- combined_metadata %>%
mutate(Broad_category = case_when(
# B Cells
cell_labels_v2 %in% c("Naïve B cells", "Memory B cells", "Naïve B cell-IFN",
"Plasma B cells", "DZtoLZ GCB transition", "DZ early Sphase",
"Early MBC", "Early GC-committed NBC", "DZ G2Mphase",
"DZ GCB Noproli-memory like", "DZ GCB", "DZ late Sphase",
"GC-commited metabolic activation", "csMBC FCRL4/5+",
"Cycling GCB", "Early PC precursor", "Pre-MBC/BC",
"Proliferating B cells", "B cells", "Intermediate B cells",
"Naïve B cells activated", "Pre-BCRi II", "IFN-activated cells",
"GCB-IFN", "B activated") ~ "B Cells",
# T Cells
cell_labels_v2 %in% c("CD4 TN", "CD4 TFH", "CD4 TCM", "CD4 Treg",
"CD4 Treg-eff", "TFH-LZ-GC", "CD4 TEM", "CD4 effector",
"CD4 T proliferating", "T-IFN", "CD8 TN", "CD8 TF",
"CD8 T", "CD8 TRM", "CD8 TEM", "Double negative T",
"Unconventional T cells", "Gamma delta T", "Cycling T", "Pre-T cells") ~ "T Cells",
# NK Cells
cell_labels_v2 %in% c("NK cells", "NK-T cells", "NK/gamma-delta T",
"Proliferating T/NK") ~ "NK Cells",
# Monocytes and Macrophages
cell_labels_v2 %in% c("Monocytes", "Monocytes/macrophages", "Macrophages",
"Macro-proliferating", "Macro-CCL", "Monocyte and neutrophil-like") ~ "Monocytes and Macrophages",
# Dendritic Cells
cell_labels_v2 %in% c("Dendritic cells", "Plasmacytoid DCs", "Follicular dendritic cells") ~ "Dendritic Cells",
# Neutrophils
cell_labels_v2 == "Neutrophils" ~ "Neutrophils",
# Innate Lymphoid Cells
cell_labels_v2 %in% c("MAIT cells", "Innate lymphocytes") ~ "Innate Lymphoid Cells",
# Epithelial Cells
cell_labels_v2 %in% c("Epithelial cells", "Basal epithelial cells", "Ciliated epithelial cells",
"Non-ciliated epithelial cells", "Secretory epithelial cells",
"Proliferating epithelial cells") ~ "Epithelial Cells",
# Other
cell_labels_v2 %in% c( "Mast cells", "Erythroid cells", "Ionocytes", "Mesothelial cells",
"Melanocyte",
"Naïve / PC/ doublet") ~ "Other",
TRUE ~ "Unclassified" # Default category for unmatched labels
))
head(combined_metadata) donor_id sample_id age_years sex nCount_RNA
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 eAIR001 s042 3.62 M 2097.863
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 eAIR001 s042 3.62 M 4072.417
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 eAIR001 s042 3.62 M 2848.063
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 eAIR001 s042 3.62 M 434.372
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 eAIR001 s042 3.62 M 21590.274
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 eAIR001 s042 3.62 M 1144.299
nFeature_RNA Barcode
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 1486 AAACAAGCAACTTCGTACTTTAGG-1
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 2506 AAACAAGCATCGTTCGACTTTAGG-1
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 1891 AAACCAATCCTTTAGGACTTTAGG-1
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 443 AAACCGGTCCGTGACTACTTTAGG-1
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 4311 AAACGTTCAGCCCTTAACTTTAGG-1
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 877 AAACGTTCATGGCTAAACTTTAGG-1
GEM_barcode sample_barcode tissue
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 AAACAAGCAACTTCGTA CTTTAGG-1 Adenoids
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 AAACAAGCATCGTTCGA CTTTAGG-1 Adenoids
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 AAACCAATCCTTTAGGA CTTTAGG-1 Adenoids
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 AAACCGGTCCGTGACTA CTTTAGG-1 Adenoids
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 AAACGTTCAGCCCTTAA CTTTAGG-1 Adenoids
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 AAACGTTCATGGCTAAA CTTTAGG-1 Adenoids
batch_name cells_per_GEM.Var1
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 G000231_batch3 <NA>
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 G000231_batch3 <NA>
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 G000231_batch3 <NA>
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 G000231_batch3 <NA>
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 G000231_batch3 <NA>
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 G000231_batch3 <NA>
cells_per_GEM.Freq scDblFinder.class_dbr
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 3 singlet
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 3 singlet
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 2 singlet
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 1 singlet
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 3 singlet
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 1 singlet
scDblFinder.score_dbr predicted.celltype.l1
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 8.364258e-04 B naive
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 3.647992e-04 FCRL4/5+ B memory
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 6.277001e-03 B naive
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 6.357333e-07 B activated
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 3.589588e-02 PC
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 2.285886e-04 B naive
predicted.celltype.l2
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 NBC
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 ncsMBC
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 NBC early activation
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 GC-commited NBC
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 IgG+ PC precursor
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 NBC early activation
predicted.celltype.l1.score
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 0.9986553
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 0.5014055
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 0.9326043
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 0.5793129
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 0.8757269
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 0.9211898
predicted.celltype.l2.score percent.mt
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 0.5650053 0.89363848
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 0.3133930 0.45582175
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 0.4845545 0.45551262
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 0.5580569 0.37925250
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 0.6296915 0.06705518
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 0.8442881 0.60955238
mapping.score Broad_cell_label_1
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 0.8641857 Immune
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 0.7719604 Immune
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 0.7590004 Immune
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 0.8996206 Immune
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 0.8858244 Immune
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 0.8131308 Immune
Broad_cell_label_2 Broad_cell_label_3
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 B cells B cells
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 B cells B cells
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 B cells B cells
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 B cells B cells
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 B cells B cells
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 B cells B cells
unintegrated_clusters harmony_clusters
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 0 0
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 2 2
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 0 0
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 0 0
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 9 9
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 0 0
RNA_snn_res.0.1 RNA_snn_res.0.2
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 0 0
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 0 0
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 0 0
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 0 0
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 5 5
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 0 0
RNA_snn_res.0.3 RNA_snn_res.0.4
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 0 0
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 2 1
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 0 0
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 0 0
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 9 9
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 0 0
RNA_snn_res.0.5 RNA_snn_res.0.6
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 0 0
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 1 1
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 0 0
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 8 7
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 12 12
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 0 0
RNA_snn_res.0.7 RNA_snn_res.0.8
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 0 0
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 1 1
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 0 1
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 7 6
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 13 13
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 0 0
RNA_snn_res.0.9 RNA_snn_res.1 cluster
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 0 0 0
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 1 1 1
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 0 0 0
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 6 6 0
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 11 12 9
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 0 0 0
cell_labels cell_labels_v2
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 Naïve B cells Naïve B cells
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 Memory B cells Memory B cells
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 Naïve B cells Naïve B cells
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 Naïve B cells Naïve B cells
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 Plasma B cells Plasma B cells
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 Naïve B cells Naïve B cells
source
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 G000231_Neeland_Adenoids.annotated_clusters.SEU.rds
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 G000231_Neeland_Adenoids.annotated_clusters.SEU.rds
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 G000231_Neeland_Adenoids.annotated_clusters.SEU.rds
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 G000231_Neeland_Adenoids.annotated_clusters.SEU.rds
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 G000231_Neeland_Adenoids.annotated_clusters.SEU.rds
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 G000231_Neeland_Adenoids.annotated_clusters.SEU.rds
predicted.ann_level_1
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 <NA>
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 <NA>
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 <NA>
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 <NA>
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 <NA>
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 <NA>
predicted.ann_level_1.score
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 NA
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 NA
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 NA
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 NA
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 NA
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 NA
predicted.ann_level_2
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 <NA>
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 <NA>
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 <NA>
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 <NA>
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 <NA>
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 <NA>
predicted.ann_level_2.score
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 NA
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 NA
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 NA
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 NA
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 NA
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 NA
predicted.ann_level_3
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 <NA>
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 <NA>
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 <NA>
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 <NA>
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 <NA>
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 <NA>
predicted.ann_level_3.score
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 NA
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 NA
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 NA
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 NA
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 NA
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 NA
predicted.ann_level_4
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 <NA>
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 <NA>
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 <NA>
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 <NA>
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 <NA>
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 <NA>
predicted.ann_level_4.score
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 NA
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 NA
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 NA
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 NA
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 NA
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 NA
predicted.ann_level_5
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 <NA>
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 <NA>
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 <NA>
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 <NA>
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 <NA>
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 <NA>
predicted.ann_level_5.score
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 NA
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 NA
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 NA
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 NA
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 NA
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 NA
predicted.ann_finest_level
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 <NA>
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 <NA>
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 <NA>
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 <NA>
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 <NA>
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 <NA>
predicted.ann_finest_level.score donor sum
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 NA <NA> NA
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 NA <NA> NA
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 NA <NA> NA
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 NA <NA> NA
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 NA <NA> NA
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 NA <NA> NA
detected scDblFinder.class_dbr_s
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 NA <NA>
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 NA <NA>
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 NA <NA>
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 NA <NA>
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 NA <NA>
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 NA <NA>
scDblFinder.score_dbr_s Broad_category
Batch1_AAACAAGCAACTTCGTACTTTAGG-1 NA B Cells
Batch1_AAACAAGCATCGTTCGACTTTAGG-1 NA B Cells
Batch1_AAACCAATCCTTTAGGACTTTAGG-1 NA B Cells
Batch1_AAACCGGTCCGTGACTACTTTAGG-1 NA B Cells
Batch1_AAACGTTCAGCCCTTAACTTTAGG-1 NA B Cells
Batch1_AAACGTTCATGGCTAAACTTTAGG-1 NA B Cellscombined_metadata$Broad_category <- factor(combined_metadata$Broad_category,
levels = c("B Cells", "T Cells", "NK Cells", "Monocytes and Macrophages",
"Dendritic Cells", "Neutrophils", "Innate Lymphoid Cells",
"Epithelial Cells", "Other", "Unclassified"))
combined_metadata$tissue <- factor(combined_metadata$tissue, levels = tissue_order)
ggplot(combined_metadata, aes(x = tissue, fill = Broad_category)) +
geom_bar(position = "fill") +
scale_fill_manual(values = sampled_colors_1) +
labs(y = "Proportion", x = "Tissue") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
theme_minimal() +
theme(legend.title = element_blank()) 
combined_metadata$cell_labels_v2 <- factor(combined_metadata$cell_labels_v2,
levels = unique(combined_metadata$cell_labels_v2))
ggplot(combined_metadata, aes(x = tissue, fill = cell_labels_v2)) +
geom_bar(position = "fill") +
scale_fill_manual(values = color_palette) +
labs(y = "Proportion", x = "Tissue") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
theme_minimal() +
theme(legend.title = element_blank()) 
set.seed(2024)
proportions <- combined_metadata %>%
group_by(Broad_category, cell_labels_v2) %>%
summarise(count = n()) %>%
arrange(Broad_category, desc(count))`summarise()` has grouped output by 'Broad_category'. You can override using
the `.groups` argument.combined_metadata <- combined_metadata %>%
mutate(cell_labels_v2 = factor(cell_labels_v2,
levels = proportions$cell_labels_v2[order(proportions$Broad_category, -proportions$count)]))broad_colors <- c("#5c248b", "#ffec34", "#00960f", "#FBB4AE", "#BC80BD", "#f06ab9", "#85d519", "#758dc4" )
cell_contingency <- table(combined_metadata$Broad_category, combined_metadata$cell_labels_v2)
chordDiagram(cell_contingency,
transparency = 0.5,
annotationTrack = "grid",
preAllocateTracks = 1,
grid.col = c(broad_colors, color_palette))
circos.track(track.index = 1, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter, CELL_META$ylim[1], CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))
}, bg.border = NA)
Updated UMAPs
for (tissue in tissue_list) {
seu <- readRDS(tissue)
tissue <- seu$tissue
p4 <- DimPlot(seu, reduction = "umap.merged", raster = FALSE, repel = TRUE, label = TRUE, label.size = 3.5, pt.size = 0.2) +
ggtitle(paste0(basename(tissue), ": UMAP (Final clusters)")) +
scale_color_manual(values = color_palette) +
NoLegend()
print(p4)
}
Boxplot of cell populations
cell_type_proportions <- combined_metadata %>%
group_by(tissue, sample_id, cell_labels_v2) %>%
summarise(cell_count = n(), .groups = 'drop') %>%
group_by(tissue, sample_id) %>%
mutate(total_cells = sum(cell_count)) %>%
mutate(proportion = cell_count / total_cells) %>%
ungroup()
tissues <- unique(cell_type_proportions$tissue)
for (tissue in tissues) {
tissue_data <- cell_type_proportions %>% filter(tissue == !!tissue)
plot <- ggplot(tissue_data, aes(x = cell_labels_v2, y = proportion, fill = cell_labels_v2)) +
geom_boxplot() +
scale_fill_manual(values = color_palette) +
labs(x = "Cell Type", y = "Proportion", title = paste("Median Proportion of Each Cell Type in", tissue)) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "none",
plot.margin = margin(20, 20, 20, 20)
)
#ggsave(filename = paste0("boxplot_proportions_", tissue, ".pdf"), plot = plot, width = 12, height = 8, units = "in")
print(plot)
}
for (tissue in tissue_list) {
seu <- readRDS(tissue)
metadata_df <- data.frame(
sample = seu$sample_id,
donor = seu$donor_id,
age_years = as.character(seu$age_years),
cell_type = seu$cell_labels_v2
)
metadata_df$age_years <- as.numeric(metadata_df$age_years)
barplot_data <- metadata_df %>%
group_by(donor, age_years, cell_type) %>%
summarise(n_cells = n(), .groups = 'drop') %>%
arrange(donor, age_years)
p <- ggplot(barplot_data, aes(x = reorder(paste(donor, age_years, sep = ":"), age_years),
y = n_cells, fill = cell_type)) +
geom_bar(stat = "identity") +
ggtitle(paste0("Cell Type Counts: ", basename(tissue))) +
labs(x = "Donor:Age (Years)", y = "Count", fill = "Cell Type") +
scale_fill_manual(values = color_palette) +
theme_minimal() +
theme(
plot.title = element_text(size = 13, hjust = 0.5, face = "bold"),
legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1)
)
print(p)
}
Session Info
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Australia/Melbourne
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] circlize_0.4.16 BiocParallel_1.36.0 harmony_1.2.0
[4] Rcpp_1.0.12 patchwork_1.2.0 cowplot_1.1.3
[7] data.table_1.15.0 RColorBrewer_1.1-3 kableExtra_1.4.0
[10] ggridges_0.5.6 ggforce_0.4.2 viridis_0.6.5
[13] viridisLite_0.4.2 paletteer_1.6.0 clustree_0.5.1
[16] ggraph_2.1.0 Seurat_5.0.1.9009 SeuratObject_5.0.1
[19] sp_2.1-3 here_1.0.1 lubridate_1.9.3
[22] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[25] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[28] tibble_3.2.1 ggplot2_3.5.0 tidyverse_2.0.0
[31] BiocStyle_2.30.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] shape_1.4.6.1 rstudioapi_0.15.0 jsonlite_1.8.8
[4] magrittr_2.0.3 spatstat.utils_3.0-4 farver_2.1.1
[7] rmarkdown_2.25 GlobalOptions_0.1.2 fs_1.6.3
[10] vctrs_0.6.5 ROCR_1.0-11 spatstat.explore_3.2-6
[13] htmltools_0.5.7 sass_0.4.8 sctransform_0.4.1
[16] parallelly_1.37.0 KernSmooth_2.23-22 bslib_0.6.1
[19] htmlwidgets_1.6.4 ica_1.0-3 plyr_1.8.9
[22] plotly_4.10.4 zoo_1.8-12 cachem_1.0.8
[25] whisker_0.4.1 igraph_2.0.2 mime_0.12
[28] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.6-5
[31] R6_2.5.1 fastmap_1.1.1 fitdistrplus_1.1-11
[34] future_1.33.1 shiny_1.8.0 digest_0.6.34
[37] colorspace_2.1-0 rematch2_2.1.2 ps_1.7.6
[40] rprojroot_2.0.4 tensor_1.5 RSpectra_0.16-1
[43] irlba_2.3.5.1 labeling_0.4.3 progressr_0.14.0
[46] fansi_1.0.6 spatstat.sparse_3.0-3 timechange_0.3.0
[49] polyclip_1.10-6 httr_1.4.7 abind_1.4-5
[52] compiler_4.3.2 withr_3.0.0 fastDummies_1.7.3
[55] highr_0.10 MASS_7.3-60.0.1 tools_4.3.2
[58] lmtest_0.9-40 httpuv_1.6.14 future.apply_1.11.1
[61] goftest_1.2-3 glue_1.7.0 callr_3.7.5
[64] nlme_3.1-164 promises_1.2.1 grid_4.3.2
[67] Rtsne_0.17 getPass_0.2-4 cluster_2.1.6
[70] reshape2_1.4.4 generics_0.1.3 gtable_0.3.4
[73] spatstat.data_3.0-4 tzdb_0.4.0 hms_1.1.3
[76] xml2_1.3.6 tidygraph_1.3.1 utf8_1.2.4
[79] spatstat.geom_3.2-8 RcppAnnoy_0.0.22 ggrepel_0.9.5
[82] RANN_2.6.1 pillar_1.9.0 spam_2.10-0
[85] RcppHNSW_0.6.0 later_1.3.2 splines_4.3.2
[88] tweenr_2.0.3 lattice_0.22-5 deldir_2.0-2
[91] survival_3.5-8 tidyselect_1.2.0 miniUI_0.1.1.1
[94] pbapply_1.7-2 knitr_1.45 git2r_0.33.0
[97] gridExtra_2.3 svglite_2.1.3 scattermore_1.2
[100] xfun_0.42 graphlayouts_1.1.0 matrixStats_1.2.0
[103] stringi_1.8.3 lazyeval_0.2.2 yaml_2.3.8
[106] evaluate_0.23 codetools_0.2-19 BiocManager_1.30.22
[109] cli_3.6.2 uwot_0.1.16 systemfonts_1.0.5
[112] xtable_1.8-4 reticulate_1.35.0 munsell_0.5.0
[115] processx_3.8.3 jquerylib_0.1.4 spatstat.random_3.2-2
[118] globals_0.16.2 png_0.1-8 parallel_4.3.2
[121] ellipsis_0.3.2 dotCall64_1.1-1 listenv_0.9.1
[124] scales_1.3.0 leiden_0.4.3.1 rlang_1.1.3
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Australia/Melbourne
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] circlize_0.4.16 BiocParallel_1.36.0 harmony_1.2.0
[4] Rcpp_1.0.12 patchwork_1.2.0 cowplot_1.1.3
[7] data.table_1.15.0 RColorBrewer_1.1-3 kableExtra_1.4.0
[10] ggridges_0.5.6 ggforce_0.4.2 viridis_0.6.5
[13] viridisLite_0.4.2 paletteer_1.6.0 clustree_0.5.1
[16] ggraph_2.1.0 Seurat_5.0.1.9009 SeuratObject_5.0.1
[19] sp_2.1-3 here_1.0.1 lubridate_1.9.3
[22] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[25] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[28] tibble_3.2.1 ggplot2_3.5.0 tidyverse_2.0.0
[31] BiocStyle_2.30.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] shape_1.4.6.1 rstudioapi_0.15.0 jsonlite_1.8.8
[4] magrittr_2.0.3 spatstat.utils_3.0-4 farver_2.1.1
[7] rmarkdown_2.25 GlobalOptions_0.1.2 fs_1.6.3
[10] vctrs_0.6.5 ROCR_1.0-11 spatstat.explore_3.2-6
[13] htmltools_0.5.7 sass_0.4.8 sctransform_0.4.1
[16] parallelly_1.37.0 KernSmooth_2.23-22 bslib_0.6.1
[19] htmlwidgets_1.6.4 ica_1.0-3 plyr_1.8.9
[22] plotly_4.10.4 zoo_1.8-12 cachem_1.0.8
[25] whisker_0.4.1 igraph_2.0.2 mime_0.12
[28] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.6-5
[31] R6_2.5.1 fastmap_1.1.1 fitdistrplus_1.1-11
[34] future_1.33.1 shiny_1.8.0 digest_0.6.34
[37] colorspace_2.1-0 rematch2_2.1.2 ps_1.7.6
[40] rprojroot_2.0.4 tensor_1.5 RSpectra_0.16-1
[43] irlba_2.3.5.1 labeling_0.4.3 progressr_0.14.0
[46] fansi_1.0.6 spatstat.sparse_3.0-3 timechange_0.3.0
[49] polyclip_1.10-6 httr_1.4.7 abind_1.4-5
[52] compiler_4.3.2 withr_3.0.0 fastDummies_1.7.3
[55] highr_0.10 MASS_7.3-60.0.1 tools_4.3.2
[58] lmtest_0.9-40 httpuv_1.6.14 future.apply_1.11.1
[61] goftest_1.2-3 glue_1.7.0 callr_3.7.5
[64] nlme_3.1-164 promises_1.2.1 grid_4.3.2
[67] Rtsne_0.17 getPass_0.2-4 cluster_2.1.6
[70] reshape2_1.4.4 generics_0.1.3 gtable_0.3.4
[73] spatstat.data_3.0-4 tzdb_0.4.0 hms_1.1.3
[76] xml2_1.3.6 tidygraph_1.3.1 utf8_1.2.4
[79] spatstat.geom_3.2-8 RcppAnnoy_0.0.22 ggrepel_0.9.5
[82] RANN_2.6.1 pillar_1.9.0 spam_2.10-0
[85] RcppHNSW_0.6.0 later_1.3.2 splines_4.3.2
[88] tweenr_2.0.3 lattice_0.22-5 deldir_2.0-2
[91] survival_3.5-8 tidyselect_1.2.0 miniUI_0.1.1.1
[94] pbapply_1.7-2 knitr_1.45 git2r_0.33.0
[97] gridExtra_2.3 svglite_2.1.3 scattermore_1.2
[100] xfun_0.42 graphlayouts_1.1.0 matrixStats_1.2.0
[103] stringi_1.8.3 lazyeval_0.2.2 yaml_2.3.8
[106] evaluate_0.23 codetools_0.2-19 BiocManager_1.30.22
[109] cli_3.6.2 uwot_0.1.16 systemfonts_1.0.5
[112] xtable_1.8-4 reticulate_1.35.0 munsell_0.5.0
[115] processx_3.8.3 jquerylib_0.1.4 spatstat.random_3.2-2
[118] globals_0.16.2 png_0.1-8 parallel_4.3.2
[121] ellipsis_0.3.2 dotCall64_1.1-1 listenv_0.9.1
[124] scales_1.3.0 leiden_0.4.3.1 rlang_1.1.3