Lineages clustering: Myeloid cells
Myeloid subclustering
Gunjan Dixit
January 27, 2026
Last updated: 2026-01-27
Checks: 6 1
Knit directory: paed-airway-allTissues/
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Modified: output/DGE/RUVseq_earlyAIR_Tonsils/TFH-LZ-GC/all_res.csv
Modified: output/DGE/RUVseq_earlyAIR_Tonsils/TFH-LZ-GC/down_res.csv
Modified: output/DGE/RUVseq_earlyAIR_Tonsils/TFH-LZ-GC/up_res.csv
Deleted: test
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
There are no past versions. Publish this analysis with
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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)
library(presto)
library(gtools)
})Myeloid cells
paed_sub <- readRDS(here("output/RDS/Lineages_RDS_combined/SEU_Myeloid_Cells.rds"))
if (!"umap.sub" %in% names(paed_sub@reductions)) {
paed_sub <- paed_sub |>
FindVariableFeatures() |>
ScaleData() |>
RunPCA()
gc()
paed_sub[["RNA"]]
paed_sub[["RNA"]] <- JoinLayers(paed_sub[["RNA"]])
gc()
paed_sub <- RunUMAP(paed_sub, dims = 1:30, reduction = "pca",
reduction.name = "umap.sub")
paed_sub <- paed_sub |>
FindNeighbors(dims = 1:30, reduction = "pca") |>
FindClusters(resolution = 0.1)
gc()
saveRDS(paed_sub, here("output/RDS/Lineages_RDS_combined/SEU_Myeloid_Cells.rds"))
} else {
message("skipping processing")
}skipping processingpaed_sub$tissue <- factor(
paed_sub$tissue,
levels = c("Tonsils", "Adenoids", "Nasal_brushings",
"Bronchial_brushings", "BAL")
)Previous labels
Excluding counts less than 100 to avoid too many labels in the plot
#sort(table(paed_sub$cell_labels_v2), decreasing = T)
k <- names(which(table(paed_sub$cell_labels_v2) > 50))
paed_sub$cell_labels_v22 <- ifelse(
as.character(paed_sub$cell_labels_v2) %in% k,
as.character(paed_sub$cell_labels_v2),
NA
)
DimPlot(
paed_sub,
group.by = "cell_labels_v22",
reduction = "umap.sub",
label = TRUE,
repel = TRUE,
label.size = 4.5,
shuffle = TRUE, raster = F)
#table( paed_sub$cell_labels_v2,paed_sub$RNA_snn_res.0.5)opt_res <- "RNA_snn_res.0.1"
n <- nlevels(paed_sub$RNA_snn_res.0.1)
paed_sub$RNA_snn_res.0.1 <- factor(paed_sub$RNA_snn_res.0.1, levels = seq(0,n-1))
paed_sub$seurat_clusters <- NULL
paed_sub$cluster <- paed_sub$RNA_snn_res.0.1
Idents(paed_sub) <- paed_sub$clusterDimPlot(paed_sub, reduction = "umap.sub", raster =F, repel=T, label = T)
DimPlot(paed_sub, reduction = "umap.sub", raster =F, repel=T, label = T, split.by = "tissue")
Adding CellTypist info
files <- c(
paeds = here("../EarlyAir_paper/output/CellTypist_results/cellTtypist_Myeloid_Paed_Covid19.csv"),
immune = here("../EarlyAir_paper/output/CellTypist_results/cellTtypist_Myeloid_Immune_low.csv"),
lung_airway = here("../EarlyAir_paper/output/CellTypist_results/cellTtypist_Myeloid_Lung_Airway.csv"),
lung_atlas = here("../EarlyAir_paper/output/CellTypist_results/cellTtypist_Myeloid_Lung_atlas.csv"),
tonsil = here("../EarlyAir_paper/output/CellTypist_results/cellTtypist_Myeloid_Human_Tonsil.csv")
)
for (nm in names(files)) {
df <- read.csv(files[nm])
paed_sub[[paste0("celltypist_", nm)]] <- df$predicted_labels
}plot_celltype_heatmap <- function(
seu,
cluster_col = "cluster",
celltype_col,
min_n = 100,
title,
scale = "row",
fontsize = 10
) {
seu@meta.data %>%
dplyr::count(.data[[cluster_col]], .data[[celltype_col]]) %>%
dplyr::filter(n >= min_n) %>%
tidyr::pivot_wider(
names_from = .data[[celltype_col]],
values_from = n,
values_fill = 0
) %>%
as.data.frame() %>%
tibble::column_to_rownames(cluster_col) %>%
t() %>%
pheatmap::pheatmap(
scale = scale,
main = title,
fontsize = fontsize
)
}
plot_celltype_heatmap(paed_sub,celltype_col = "celltypist_paeds",title = "CellTypist Predictions (Paed Covid Study) by Cluster")
plot_celltype_heatmap(paed_sub,celltype_col = "celltypist_immune",title = "CellTypist Predictions (Immune Low) by Cluster")
plot_celltype_heatmap(paed_sub,celltype_col = "celltypist_lung_airway",title = "CellTypist Predictions (Lung Atlas) by Cluster")
plot_celltype_heatmap( paed_sub,celltype_col = "celltypist_lung_atlas",title = "CellTypist Predictions (Lung Atlas) by Cluster")
plot_celltype_heatmap(paed_sub,celltype_col = "celltypist_tonsil", title = "Azimuth Predictions (Tonsil Atlas) by Cluster")
plot_ct <- function(obj, col) {
k <- names(which(table(obj[[col]][,1]) > 100))
DimPlot(
obj,
group.by = col,
reduction = "umap.sub",
cells = colnames(obj)[obj[[col]][,1] %in% k],
label = TRUE, repel = TRUE, label.size = 2.5,
shuffle = TRUE, raster = FALSE
)
}
plot_ct(paed_sub, "celltypist_paeds") + ggtitle("Paed Covid Study")
plot_ct(paed_sub, "celltypist_immune") + ggtitle("Immune low dataset")
plot_ct(paed_sub, "celltypist_lung_airway") + ggtitle("Lung Airway Study")
plot_ct(paed_sub, "celltypist_lung_atlas") + ggtitle("Lung Atlas")
plot_ct(paed_sub, "celltypist_tonsil") + ggtitle("Tonsil Atlas")
plot_ct_bar <- function(obj, col) {
k <- names(which(table(obj[[col]][,1]) > 100))
df <- obj@meta.data %>%
filter(get(col) %in% k) %>%
count(cluster, !!sym(col)) %>%
group_by(cluster) %>%
mutate(frac = n / sum(n)) %>%
ungroup()
ggplot(df, aes(x = cluster, y = frac, fill = !!sym(col))) +
geom_col(position = "stack") +
scale_y_continuous(labels = scales::percent) +
labs(x = "Cluster", y = "Fraction of cells", fill = col) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
}
plot_ct_bar(paed_sub, "celltypist_paeds") + ggtitle("Paed Covid Study")
plot_ct_bar(paed_sub, "celltypist_immune") + ggtitle("Immune low dataset")
plot_ct_bar(paed_sub, "celltypist_lung_airway") + ggtitle("Lung Airway study")
plot_ct_bar(paed_sub, "celltypist_lung_atlas") + ggtitle("Lung Atlas")
plot_ct_bar(paed_sub, "celltypist_tonsil") + ggtitle("Tonsil Atlas")
features <- list(
Myeloid_core = c("LYZ", "CTSD", "LST1"),
Mono_classical = c("CD14", "S100A8", "S100A9", "LGALS3"),
Mono_nonclassical = c("FCGR3A", "LILRB1", "MS4A7"),
Macrophage_core = c("CD68", "CSF1R", "APOE", "CTSB"),
Macro_M2_like = c("CD163", "MRC1", "MSR1"),
Macro_CCL_inflam = c("CCL2", "CCL3", "CCL4", "IL1B"),
Macro_prolif = c("MKI67", "TOP2A", "PCNA"),
cDC2 = c("CD1C", "FCER1A", "CLEC10A"),
pDC = c("IL3RA", "GZMB", "TCF4", "IRF7"),
Neutrophil = c("S100A8", "S100A9", "FCGR3B", "CSF3R"),
Mast = c("TPSAB1", "CPA3", "KIT"),
FDC = c("CXCL13", "CR2", "FDCSP")
)
for (g in features) {
print(
FeaturePlot(
paed_sub,
features = g,
reduction = "umap.sub",
raster = FALSE,
label = TRUE
#split.by = "tissue"
) #
)
}Warning: The `slot` argument of `FetchData()` is deprecated as of SeuratObject 5.0.0.
ℹ Please use the `layer` argument instead.
ℹ The deprecated feature was likely used in the Seurat package.
Please report the issue at <https://github.com/satijalab/seurat/issues>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
Warning: The following requested variables were not found: TPSAB1
for (g in features) {
print(
FeaturePlot(
paed_sub,
features = g,
reduction = "umap.sub",
raster = FALSE,
label = TRUE,
split.by = "tissue",
min.cutoff = "q10",
max.cutoff = "q90"
)
)
}
Warning: The following requested variables were not found: TPSAB1
paed_sub <- FindSubCluster(paed_sub, cluster = 1, graph.name = "RNA_snn", resolution = 0.2, subcluster.name = "M_sub")Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 15619
Number of edges: 500867
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9065
Number of communities: 7
Elapsed time: 1 secondsIdents(paed_sub) <- paed_sub$M_sub#levels(Idents(paed_sub)) <- mixedsort(levels(Idents(paed_sub)))
#levels(paed_sub$T_sub) <- mixedsort(levels(paed_sub$T_sub))
DimPlot(paed_sub, reduction = "umap.sub", raster =F, repel=T, label = T, group.by = "M_sub")
new_levels <- c(
"0" = "Macrophages",
"1_0" = "Monocytes",
"1_1" = "Macrophages",
"1_2" = "Monocytes",
"1_3" = "DC2",
"1_4" = "Monocytes",
"1_5" = "Activated DCs",
"1_6" = "Monocytes",
"2" = "Macrophages",
"3" = "Macrophages",
"4" = "Neutrophils",
"5" = "Plasmacytoid DCs",
"6" = "Macrophages",
"7" = "Macrophages",
"8" = "Mast cells",
"9" = "Macrophages",
"10" = "Follicular DCs",
"11" = "DC1",
"12" = "Ionocytes",
"13" = "Melanocyte"
)
paed_sub <- RenameIdents(paed_sub, new_levels)
paed_sub$cell_labels <- NULLCorrecting previous labels and defining level 1
paed_sub$cell_labels_l1 <- Idents(paed_sub)
paed_sub$cell_labels_l1 <- factor(
Idents(paed_sub),
levels = c(
"Monocytes",
"Macrophages",
"Neutrophils",
"DC1",
"DC2",
"Activated DCs",
"Plasmacytoid DCs",
"Follicular DCs",
"Mast cells",
"Ionocytes",
"Melanocyte"
)
)
Idents(paed_sub) <- paed_sub$cell_labels_l1DimPlot(
paed_sub,
reduction = "umap.sub",
label = TRUE,
repel = TRUE,
label.size = 2.5, raster = F
)
Macrophages subclustering
macro <- subset(paed_sub, idents = "Macrophages")
keep_cells <- paed_sub$cell_labels_l1 %in% "Macrophages"
macro@assays$RNA@cells@.Data <- paed_sub@assays$RNA@cells@.Data[keep_cells, ]
macroAn object of class Seurat
18076 features across 50832 samples within 1 assay
Active assay: RNA (18076 features, 2000 variable features)
3 layers present: data, counts, scale.data
4 dimensional reductions calculated: pca, umap.unintegrated, umap.l1, umap.subout <- here("output/RDS/Lineages_RDS_combined/SEU_Macrophages_Cells.rds")
if (!file.exists(out)) {
macro <- macro %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA()
gc()
macro <- RunUMAP(macro, dims = 1:30, reduction = "pca", reduction.name = "umap.mm")
meta_data_columns <- colnames(macro@meta.data)
columns_to_remove <- grep("^RNA_snn_res", meta_data_columns, value = TRUE)
macro@meta.data <- macro@meta.data[, !(colnames(macro@meta.data) %in% columns_to_remove)]
resolutions <- seq(0.1, 0.5, by = 0.1)
macro <- FindNeighbors(macro, dims = 1:30, reduction = "pca")
macro <- FindClusters(macro, resolution = resolutions )
saveRDS(macro, out)
} else {
macro <- readRDS(out)
}clustree(macro, prefix = "RNA_snn_res.")
DimPlot(macro, group.by = "RNA_snn_res.0.2", reduction = "umap.mm", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )
DimPlot(macro, reduction = "umap.mm", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE, split.by = "tissue" )
DimPlot(macro, group.by = "donor", reduction = "umap.mm", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE, split.by = "tissue" )Warning: ggrepel: 30 unlabeled data points (too many overlaps). Consider
increasing max.overlapsWarning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
increasing max.overlapsWarning: ggrepel: 21 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
opt_res <- "RNA_snn_res.0.2"
n <- nlevels(macro$RNA_snn_res.0.2)
macro$RNA_snn_res.0.2 <- factor(macro$RNA_snn_res.0.2, levels = seq(0,n-1))
macro$seurat_clusters <- NULL
macro$cluster <- macro$RNA_snn_res.0.2
Idents(macro) <- macro$clusterDimPlot(macro, group.by = "donor", reduction = "umap.mm", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE, split.by = "tissue" )Warning: ggrepel: 30 unlabeled data points (too many overlaps). Consider
increasing max.overlapsWarning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
increasing max.overlapsWarning: ggrepel: 21 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
k <- names(which(table(macro$celltypist_lung_airway) > 150))
macro$celltypist_lung2 <- ifelse(macro$celltypist_lung_airway %in% k,
macro$celltypist_lung_airway, NA)
DimPlot(
macro,
group.by = "celltypist_lung2",
reduction = "umap.mm",
label = TRUE,
repel = TRUE,
label.size = 2.5,
shuffle = TRUE
)plot_ct_bar(macro, "celltypist_paeds")
plot_ct_bar(macro, "celltypist_lung_airway")
plot_ct_bar(macro, "celltypist_lung_atlas")
plot_ct_bar(macro, "celltypist_immune")
plot_ct_bar(macro, "celltypist_tonsil")
#plot_ct_bar(macro, "predicted.celltype.l2") # Tonsil Atlas level 2
plot_ct_bar(macro, "cell_labels_v2") # previous labels
Checking for known markers-
features <-c("C1QB", "C1QC", "APOE", "FABP4", "MARCO", "INHBA", "MCEMP1")
FeaturePlot(
macro,
features = features,
reduction = "umap.mm",
raster = FALSE,
label = TRUE,
ncol = 1,
split.by = "tissue"
)Warning: All cells have the same value (0) of "FABP4"
All cells have the same value (0) of "FABP4"
Renaming idents
new_levels <- c(
"0" = "Alveolar Macrophages",
"1" = "Alveolar Macrophages",
"2" = "Alveolar Macrophages",
"3" = "Alveolar Macrophages",
"4" = "Macro CCL",
"5" = "Intermediate Macrophages",
"6" ="Macro_tonsils_adenoids",
"7" = "Monocyte-derived Macrophages",
"8" = "Proliferating Macrophages",
"9" = "Alveolar Macrophages",
"10" = "Interstitial Macrophages"
)
macro <- RenameIdents(macro, new_levels)
macro$cell_labels_l2 <- Idents(macro)
macro$cell_labels_l3 <- Idents(macro)table(macro$cell_labels_l2, macro$tissue)
Tonsils Adenoids Nasal_brushings
Alveolar Macrophages 0 0 1
Macro CCL 0 0 3
Intermediate Macrophages 48 31 368
Macro_tonsils_adenoids 1506 801 27
Monocyte-derived Macrophages 0 0 1
Proliferating Macrophages 0 0 27
Interstitial Macrophages 0 0 0
Bronchial_brushings BAL
Alveolar Macrophages 6468 29523
Macro CCL 1739 2092
Intermediate Macrophages 1789 1231
Macro_tonsils_adenoids 13 22
Monocyte-derived Macrophages 11 2138
Proliferating Macrophages 160 1742
Interstitial Macrophages 972 119df_counts <- macro@meta.data |>
count(cell_labels_l2, tissue, name = "n")
df_totals <- df_counts |>
group_by(cell_labels_l2) |>
summarise(total = sum(n), .groups = "drop")
ggplot(df_counts, aes(x = cell_labels_l2, y = n, fill = tissue)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(data = df_totals,
aes(x = cell_labels_l2, y = total, label = total),
vjust = -0.3, size = 3, inherit.aes = FALSE) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(x = "cluster at l2",
y = "Number of cells",
fill = "Tissue")
macro@meta.data %>%
count(cell_labels_l2, donor) %>%
group_by(cell_labels_l2) %>%
mutate(prop = n / sum(n)) %>% # proportion per L2
ungroup() %>%
ggplot(aes(x = cell_labels_l2, y = prop, fill = donor)) +
geom_col(width = 0.9) +
scale_y_continuous(labels = scales::percent) +
scale_fill_viridis_d(option = "turbo", guide = guide_legend(ncol = 2)) +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
x = NULL,
y = "Proportion",
fill = "Donor",
title = "Donor composition per cluster"
)
DE markers
The marker genes for this subclustering can be found here-
Macrophages_population_subclusters
features.no.mt <- rownames(paed_sub)[!grepl("^MT-", rownames(paed_sub))]
gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 4436866 237.0 10214208 545.5 NA 10214208 545.5
Vcells 1272521453 9708.6 2092132540 15961.8 163840 2092111009 15961.6paed_sub.markers <- FindAllMarkers(
macro,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25,
features = features.no.mt
)
gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 4501622 240.5 10214208 545.5 NA 10214208 545.5
Vcells 1272706287 9710.0 3012846857 22986.2 163840 2991650449 22824.5top10 <- paed_sub.markers %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) %>%
ungroup() %>%
distinct(gene, .keep_all = TRUE) %>%
arrange(cluster, desc(avg_log2FC))
best.wilcox.gene.per.cluster <- paed_sub.markers |>
group_by(cluster) |>
slice_max(avg_log2FC, n = 1) |>
select(cluster, gene)
best.wilcox.gene.per.cluster# A tibble: 7 × 2
# Groups: cluster [7]
cluster gene
<fct> <chr>
1 Alveolar Macrophages SLC19A3
2 Macro CCL IL6
3 Intermediate Macrophages SDS
4 Macro_tonsils_adenoids PLA2G2D
5 Monocyte-derived Macrophages CCR2
6 Proliferating Macrophages KIF18B
7 Interstitial Macrophages CXCL10 FeaturePlot(
macro,
features = best.wilcox.gene.per.cluster$gene,
reduction = "umap.mm",
raster = FALSE,
repel = TRUE,
ncol = 1,
label = TRUE,
split.by = "tissue"
) 
## Seurat top markers
cluster_colors <- paletteer::paletteer_d("pals::glasbey")[factor(top10$cluster)]
DotPlot(macro,
features = unique(top10$gene),
group.by = "cell_labels_l3",
cols = c("azure1", "blueviolet"),
dot.scale = 3, assay = "RNA") +
RotatedAxis() +
FontSize(y.text = 8, x.text = 12) +
labs(y = element_blank(), x = element_blank()) +
coord_flip() +
theme(axis.text.y = element_text(color = cluster_colors)) +
ggtitle("Top 10 marker genes per cluster (Seurat)")
marker_counts <- paed_sub.markers %>%
filter(avg_log2FC > 1 & p_val_adj < 0.05) %>%
count(cluster, name = "n_markers") %>%
arrange(desc(n_markers))
print(marker_counts) cluster n_markers
1 Intermediate Macrophages 443
2 Proliferating Macrophages 395
3 Macro_tonsils_adenoids 321
4 Interstitial Macrophages 261
5 Monocyte-derived Macrophages 152
6 Alveolar Macrophages 151
7 Macro CCL 130ggplot(marker_counts, aes(x = reorder(cluster, n_markers), y = n_markers)) +
geom_col(fill = "steelblue", alpha = 0.8) +
coord_flip() +
labs(title = "High-confidence markers per subcluster (logFC > 1)",
x = "Subcluster", y = "Number of markers") +
theme_minimal()
out_markers <- here("output",
"CSV_v3","Myeloid_lineage",
paste("Marker_genes_Reclustered_Macrophages.",opt_res, sep = ""))
dir.create(out_markers, recursive = TRUE, showWarnings = FALSE)
for (cl in unique(paed_sub.markers$cluster)) {
cluster_data <- paed_sub.markers %>% dplyr::filter(cluster == cl)
file_name <- here(out_markers, paste0("G000231_Neeland_", cl, ".csv"))
if (!file.exists(file_name)) {
write.csv(cluster_data, file = file_name)
}
}Pairwise markers within Macrophages
ids <- unique(macro$cell_labels_l3)
res_list <- list()
for (i in 1:(length(ids) - 1)) {
for (j in (i + 1):length(ids)) {
id1 <- ids[i]
id2 <- ids[j]
res <- FindMarkers(
macro,
ident.1 = id1,
ident.2 = id2,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.5,
features = features.no.mt
)
top_genes <- res %>%
filter(pct.1 > 0.1 & pct.2 > 0.1) %>%
arrange(p_val_adj, desc(avg_log2FC)) %>%
head(10) %>%
rownames()
res_list[[paste(id1, "vs", id2, sep = "_")]] <- res
cat("\n\n###", paste(id1, "vs", id2), "\n\n")
print(
VlnPlot(
macro,
features = top_genes,
idents = c(id1, id2),
pt.size = 0
) + patchwork::plot_annotation(title = paste(id1, "vs", id2))
)
kable(
head(res, 50),
caption = paste("Top 50 markers for", id1, "vs", id2)
)
}
}Intermediate Macrophages vs Proliferating Macrophages
Intermediate Macrophages vs Macro_tonsils_adenoids
Intermediate Macrophages vs Macro CCL
Intermediate Macrophages vs Alveolar Macrophages
Intermediate Macrophages vs Monocyte-derived Macrophages
Intermediate Macrophages vs Interstitial Macrophages
Proliferating Macrophages vs Macro_tonsils_adenoids
Proliferating Macrophages vs Macro CCL
Proliferating Macrophages vs Alveolar Macrophages
Proliferating Macrophages vs Monocyte-derived Macrophages
Proliferating Macrophages vs Interstitial Macrophages
Macro_tonsils_adenoids vs Macro CCL
Macro_tonsils_adenoids vs Alveolar Macrophages
Macro_tonsils_adenoids vs Monocyte-derived Macrophages
Macro_tonsils_adenoids vs Interstitial Macrophages
Macro CCL vs Alveolar Macrophages
Macro CCL vs Monocyte-derived Macrophages
Macro CCL vs Interstitial Macrophages
Alveolar Macrophages vs Monocyte-derived Macrophages
Alveolar Macrophages vs Interstitial Macrophages
Monocyte-derived Macrophages vs Interstitial Macrophages
Overview of l1, l2 and l3
paed_sub$cell_labels_l2 <- as.character(paed_sub$cell_labels_l1)
paed_sub$cell_labels_l3 <- as.character(paed_sub$cell_labels_l2)
for (obj in list(macro)) {
cells <- intersect(colnames(paed_sub), colnames(obj))
paed_sub$cell_labels_l2[cells] <- as.character(obj$cell_labels_l2[cells])
paed_sub$cell_labels_l3[cells] <- as.character(obj$cell_labels_l3[cells])
}
paed_sub$cell_labels_l2 <- factor(paed_sub$cell_labels_l2)
paed_sub$cell_labels_l3 <- factor(paed_sub$cell_labels_l3)DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "lineage")
DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "cell_labels_l1")
paed_sub$cell_labels_l2 <- factor(
paed_sub$cell_labels_l2,
levels = c(
"Monocytes",
"Monocyte-derived Macrophages",
"Intermediate Macrophages",
"Proliferating Macrophages",
"Macro CCL",
"Macro_tonsils_adenoids",
"Interstitial Macrophages",
"Alveolar Macrophages",
"DC1",
"DC2",
"Activated DCs",
"Plasmacytoid DCs",
"Follicular DCs",
"Neutrophils",
"Mast cells",
"Ionocytes",
"Melanocyte"
)
)
Idents(paed_sub) <- paed_sub$cell_labels_l2
DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "cell_labels_l2")
DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "cell_labels_l2", split.by = "tissue")
paed_sub$cell_labels_l3 <- paed_sub$cell_labels_l2#DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "cell_labels_l3", split.by = "tissue")Save RDS
#saveRDS(paed_sub, "../EarlyAir_paper/output/RDS/Annotated_lineages/SEU_Myeloid_annotated.rds")paed_sub@meta.data %>%
count(cell_labels_l3, tissue) %>%
group_by(cell_labels_l3) %>%
mutate(total = sum(n)) %>%
ungroup() %>%
#mutate(cell_labels_l3 = reorder(cell_labels_l3, total)) %>%
ggplot(aes(x = cell_labels_l3, y = n, fill = tissue)) +
geom_col() +
geom_text(
data = function(df) distinct(df, cell_labels_l3, total),
aes(x = cell_labels_l3, y = total, label = total),
inherit.aes = FALSE,
hjust = -0.1,
size = 3
) +
coord_flip() +
theme_classic() +
labs(
x = NULL,
y = "Cell count",
fill = "Tissue",
title = "Cell counts by L3 cell type (stacked by tissue)"
)
paed_sub@meta.data %>%
count(cell_labels_l3, donor) %>%
group_by(cell_labels_l3) %>%
mutate(prop = n / sum(n)) %>% # proportion per L3
ungroup() %>%
ggplot(aes(x = cell_labels_l3, y = prop, fill = donor)) +
geom_col(width = 0.9) +
scale_y_continuous(labels = scales::percent) +
scale_fill_viridis_d(option = "turbo", guide = guide_legend(ncol = 2)) +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
x = NULL,
y = "Proportion",
fill = "Donor",
title = "Donor composition per L3 cell type"
)
Some notes/observations from Myeloid cell lineage-
- Macrophages mostly present in BAL and Bronchial brushings
- Macrophages found in Tonsils/Adenoids sit in a separate cluster,
I’ve named it as Macro_tonsil_adenoids. It shows SLAN+ (slan-like
signature)
- Macrophages have donor confounding effect
Session Info
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.7.3
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] gtools_3.9.5 presto_1.0.0 circlize_0.4.16
[4] BiocParallel_1.36.0 harmony_1.2.3 Rcpp_1.0.14
[7] patchwork_1.3.0 cowplot_1.1.3 data.table_1.17.2
[10] RColorBrewer_1.1-3 kableExtra_1.4.0 ggridges_0.5.6
[13] ggforce_0.4.2 viridis_0.6.5 viridisLite_0.4.2
[16] paletteer_1.6.0 clustree_0.5.1 ggraph_2.2.1
[19] Seurat_5.0.3 SeuratObject_5.1.0 sp_2.2-0
[22] here_1.0.1 lubridate_1.9.4 forcats_1.0.0
[25] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4
[28] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[31] ggplot2_3.5.2 tidyverse_2.0.0 BiocStyle_2.30.0
[34] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.3.3 later_1.4.2
[4] prismatic_1.1.2 polyclip_1.10-7 fastDummies_1.7.5
[7] lifecycle_1.0.4 rprojroot_2.0.4 globals_0.17.0
[10] processx_3.8.6 lattice_0.22-5 MASS_7.3-60.0.1
[13] backports_1.5.0 magrittr_2.0.3 limma_3.58.1
[16] plotly_4.10.4 sass_0.4.10 rmarkdown_2.29
[19] jquerylib_0.1.4 yaml_2.3.10 httpuv_1.6.16
[22] sctransform_0.4.2 spam_2.11-1 spatstat.sparse_3.1-0
[25] reticulate_1.42.0 pbapply_1.7-2 abind_1.4-8
[28] Rtsne_0.17 tweenr_2.0.3 git2r_0.36.2
[31] ggrepel_0.9.6 irlba_2.3.5.1 listenv_0.9.1
[34] spatstat.utils_3.1-4 pheatmap_1.0.12 goftest_1.2-3
[37] RSpectra_0.16-2 spatstat.random_3.4-1 fitdistrplus_1.2-2
[40] parallelly_1.44.0 svglite_2.2.1 leiden_0.4.3.1
[43] codetools_0.2-19 xml2_1.3.8 tidyselect_1.2.1
[46] shape_1.4.6.1 farver_2.1.2 matrixStats_1.5.0
[49] spatstat.explore_3.4-3 jsonlite_2.0.0 tidygraph_1.3.1
[52] progressr_0.15.1 survival_3.5-8 systemfonts_1.2.3
[55] tools_4.3.3 ica_1.0-3 glue_1.8.0
[58] gridExtra_2.3 xfun_0.52 withr_3.0.2
[61] BiocManager_1.30.25 fastmap_1.2.0 callr_3.7.6
[64] digest_0.6.37 timechange_0.3.0 R6_2.6.1
[67] mime_0.13 textshaping_1.0.1 colorspace_2.1-1
[70] scattermore_1.2 tensor_1.5 spatstat.data_3.1-6
[73] utf8_1.2.5 generics_0.1.4 graphlayouts_1.2.2
[76] httr_1.4.7 htmlwidgets_1.6.4 whisker_0.4.1
[79] uwot_0.2.3 pkgconfig_2.0.3 gtable_0.3.6
[82] lmtest_0.9-40 htmltools_0.5.8.1 dotCall64_1.2
[85] scales_1.4.0 png_0.1-8 spatstat.univar_3.1-3
[88] knitr_1.50 rstudioapi_0.17.1 tzdb_0.5.0
[91] reshape2_1.4.4 checkmate_2.3.2 nlme_3.1-164
[94] cachem_1.1.0 zoo_1.8-14 GlobalOptions_0.1.2
[97] KernSmooth_2.23-22 vipor_0.4.7 parallel_4.3.3
[100] miniUI_0.1.2 ggrastr_1.0.2 pillar_1.10.2
[103] grid_4.3.3 vctrs_0.6.5 RANN_2.6.2
[106] promises_1.3.2 xtable_1.8-4 cluster_2.1.6
[109] beeswarm_0.4.0 evaluate_1.0.3 cli_3.6.5
[112] compiler_4.3.3 rlang_1.1.6 crayon_1.5.3
[115] future.apply_1.11.3 labeling_0.4.3 rematch2_2.1.2
[118] ps_1.9.1 ggbeeswarm_0.7.2 getPass_0.2-4
[121] plyr_1.8.9 fs_1.6.6 stringi_1.8.7
[124] deldir_2.0-4 lazyeval_0.2.2 spatstat.geom_3.4-1
[127] Matrix_1.6-5 RcppHNSW_0.6.0 hms_1.1.3
[130] future_1.40.0 statmod_1.5.0 shiny_1.10.0
[133] ROCR_1.0-11 igraph_2.1.4 memoise_2.0.1
[136] bslib_0.9.0
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.7.3
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] gtools_3.9.5 presto_1.0.0 circlize_0.4.16
[4] BiocParallel_1.36.0 harmony_1.2.3 Rcpp_1.0.14
[7] patchwork_1.3.0 cowplot_1.1.3 data.table_1.17.2
[10] RColorBrewer_1.1-3 kableExtra_1.4.0 ggridges_0.5.6
[13] ggforce_0.4.2 viridis_0.6.5 viridisLite_0.4.2
[16] paletteer_1.6.0 clustree_0.5.1 ggraph_2.2.1
[19] Seurat_5.0.3 SeuratObject_5.1.0 sp_2.2-0
[22] here_1.0.1 lubridate_1.9.4 forcats_1.0.0
[25] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4
[28] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[31] ggplot2_3.5.2 tidyverse_2.0.0 BiocStyle_2.30.0
[34] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.3.3 later_1.4.2
[4] prismatic_1.1.2 polyclip_1.10-7 fastDummies_1.7.5
[7] lifecycle_1.0.4 rprojroot_2.0.4 globals_0.17.0
[10] processx_3.8.6 lattice_0.22-5 MASS_7.3-60.0.1
[13] backports_1.5.0 magrittr_2.0.3 limma_3.58.1
[16] plotly_4.10.4 sass_0.4.10 rmarkdown_2.29
[19] jquerylib_0.1.4 yaml_2.3.10 httpuv_1.6.16
[22] sctransform_0.4.2 spam_2.11-1 spatstat.sparse_3.1-0
[25] reticulate_1.42.0 pbapply_1.7-2 abind_1.4-8
[28] Rtsne_0.17 tweenr_2.0.3 git2r_0.36.2
[31] ggrepel_0.9.6 irlba_2.3.5.1 listenv_0.9.1
[34] spatstat.utils_3.1-4 pheatmap_1.0.12 goftest_1.2-3
[37] RSpectra_0.16-2 spatstat.random_3.4-1 fitdistrplus_1.2-2
[40] parallelly_1.44.0 svglite_2.2.1 leiden_0.4.3.1
[43] codetools_0.2-19 xml2_1.3.8 tidyselect_1.2.1
[46] shape_1.4.6.1 farver_2.1.2 matrixStats_1.5.0
[49] spatstat.explore_3.4-3 jsonlite_2.0.0 tidygraph_1.3.1
[52] progressr_0.15.1 survival_3.5-8 systemfonts_1.2.3
[55] tools_4.3.3 ica_1.0-3 glue_1.8.0
[58] gridExtra_2.3 xfun_0.52 withr_3.0.2
[61] BiocManager_1.30.25 fastmap_1.2.0 callr_3.7.6
[64] digest_0.6.37 timechange_0.3.0 R6_2.6.1
[67] mime_0.13 textshaping_1.0.1 colorspace_2.1-1
[70] scattermore_1.2 tensor_1.5 spatstat.data_3.1-6
[73] utf8_1.2.5 generics_0.1.4 graphlayouts_1.2.2
[76] httr_1.4.7 htmlwidgets_1.6.4 whisker_0.4.1
[79] uwot_0.2.3 pkgconfig_2.0.3 gtable_0.3.6
[82] lmtest_0.9-40 htmltools_0.5.8.1 dotCall64_1.2
[85] scales_1.4.0 png_0.1-8 spatstat.univar_3.1-3
[88] knitr_1.50 rstudioapi_0.17.1 tzdb_0.5.0
[91] reshape2_1.4.4 checkmate_2.3.2 nlme_3.1-164
[94] cachem_1.1.0 zoo_1.8-14 GlobalOptions_0.1.2
[97] KernSmooth_2.23-22 vipor_0.4.7 parallel_4.3.3
[100] miniUI_0.1.2 ggrastr_1.0.2 pillar_1.10.2
[103] grid_4.3.3 vctrs_0.6.5 RANN_2.6.2
[106] promises_1.3.2 xtable_1.8-4 cluster_2.1.6
[109] beeswarm_0.4.0 evaluate_1.0.3 cli_3.6.5
[112] compiler_4.3.3 rlang_1.1.6 crayon_1.5.3
[115] future.apply_1.11.3 labeling_0.4.3 rematch2_2.1.2
[118] ps_1.9.1 ggbeeswarm_0.7.2 getPass_0.2-4
[121] plyr_1.8.9 fs_1.6.6 stringi_1.8.7
[124] deldir_2.0-4 lazyeval_0.2.2 spatstat.geom_3.4-1
[127] Matrix_1.6-5 RcppHNSW_0.6.0 hms_1.1.3
[130] future_1.40.0 statmod_1.5.0 shiny_1.10.0
[133] ROCR_1.0-11 igraph_2.1.4 memoise_2.0.1
[136] bslib_0.9.0