Last updated: 2026-01-27

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Knit directory: paed-airway-allTissues/

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    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cell-IFN/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cell-IFN/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells activated/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells activated/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells activated/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Plasma B cells/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Plasma B cells/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Plasma B cells/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Pre-BCRi II/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Pre-BCRi II/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Pre-BCRi II/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/T-IFN/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/T-IFN/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/T-IFN/up_res.csv
    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 wflow_publish() to start tracking its development.

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)
})

set.seed(2026)

B cells

These are all the B cells across 5 tissues-

paed_sub <- readRDS(here("output/RDS/Lineages_RDS_combined/SEU_B_Cells.rds"))

if (!"umap.bcells" %in% names(paed_sub@reductions)) {
  paed_sub <- paed_sub |>
    FindVariableFeatures() |>
    ScaleData() |>
    RunPCA()
  
  gc()
  paed_sub <- RunUMAP(paed_sub, dims = 1:30, reduction = "pca",
                      reduction.name = "umap.bcells")
  
  paed_sub <- paed_sub |>
    FindNeighbors(dims = 1:30, reduction = "pca") |>
    FindClusters(resolution = 0.2)
  gc()
  
 } else {
  message("skipping processing")
}

skipping processing

table(paed_sub$seurat_clusters)


     0      1      2      3      4      5      6      7 
101604  64033  58159  37003  13824  11181    566    131

DimPlot(paed_sub, reduction = "umap.bcells", raster =F, repel=T, label = T)

paed_sub <- FindSubCluster(paed_sub, cluster = "5", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "cluster")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11181
Number of edges: 336893

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9393
Number of communities: 4
Elapsed time: 0 seconds

DimPlot(paed_sub, raster = F, reduction = "umap.bcells", label = T, group.by = "cluster", repel = T)

Idents(paed_sub) <- factor(paed_sub$cluster,
                           levels = c("0","1","2","3","4","5_0","5_1","5_2","5_3","6","7"))
paed_sub$cluster <- Idents(paed_sub)

FeaturePlot(
  paed_sub,
  features = c("TNFRSF13B", "MEF2B", "BCL6", "AICDA", "MKI67", "TOP2A", "CD83"),
  reduction = "umap.bcells",
  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.

sort(table(paed_sub$harmonised_atlas_label) ,decreasing = T)


            B cells Doublet query/Other     Cycling T cells       Pre B/T cells 
             285264                 930                 132                  89 
    Dendritic cells         CD4 T cells  Epithelial lineage         CD8 T cells 
                 48                  30                   4                   2 
        Macrophages 
                  2

sort(table(paed_sub$predicted.celltype.l1) ,decreasing = T)


              B naive DZtoLZ GCB transition        Cycling DZ GCB 
                98675                 40311                 36944 
    FCRL4/5+ B memory              B memory                    PC 
                24527                 22269                 13913 
               DZ GCB           B activated                LZ GCB 
                10162                  9911                  6871 
LZtoDZ GCB transition        preMBC/doublet                preGCB 
                 2942                   860                   832 
                prePB                preB/T             Cycling T 
                  110                    89                    82 
           PC/doublet                    PB             CD4 naive 
                   70                    61                    15 
              CD4 TCM           Cycling FDC               CD4 TFH 
                    3                     3                     2 
                  aDC 
                    1

table(paed_sub$predicted.celltype.l1, paed_sub$cluster)

                       
                            0     1     2     3     4   5_0   5_1   5_2   5_3
  aDC                       0     0     0     0     0     0     1     0     0
  B activated            9133   624    13     7     0     9   113    11     0
  B memory               1172 18280    71   633    24  1136   114   807    32
  B naive               85528 12425    49     0    72     4    73   487     0
  CD4 naive                 9     2     1     0     0     1     1     1     0
  CD4 TCM                   0     1     0     0     2     0     0     0     0
  CD4 TFH                   1     1     0     0     0     0     0     0     0
  Cycling DZ GCB            0     0  1526 31421     0  3990     5     2     0
  Cycling FDC               0     0     0     0     0     3     0     0     0
  Cycling T                 0     0     0     4     0    78     0     0     0
  DZ GCB                    0     0  9574   537     0    16    14    21     0
  DZtoLZ GCB transition     0     4 38607  1288     0    66   300    39     0
  FCRL4/5+ B memory       898 23023    10     4    10    18   517    38     9
  LZ GCB                    2     2  6015   503     0    69   276     3     0
  LZtoDZ GCB transition     0     0     4  1572     0  1362     4     0     0
  PB                        0     0     0    54     0     3     0     0     0
  PC                        0     1    18    30 13393     0     0     0     0
  PC/doublet               16     6     0     3     2     1     0    41     0
  preB/T                    0     0     6     0     0     0     0     1     0
  preGCB                   12    11     8     0     3    93   689    14     0
  preMBC/doublet            9    29   617    44     0    21    21   111     0
  prePB                     0     0    41     1     1     0     1     0     0
                       
                            6     7
  aDC                       0     0
  B activated               0     1
  B memory                  0     0
  B naive                   0    37
  CD4 naive                 0     0
  CD4 TCM                   0     0
  CD4 TFH                   0     0
  Cycling DZ GCB            0     0
  Cycling FDC               0     0
  Cycling T                 0     0
  DZ GCB                    0     0
  DZtoLZ GCB transition     7     0
  FCRL4/5+ B memory         0     0
  LZ GCB                    1     0
  LZtoDZ GCB transition     0     0
  PB                        4     0
  PC                      471     0
  PC/doublet                1     0
  preB/T                    0    82
  preGCB                    1     1
  preMBC/doublet            0     8
  prePB                    66     0

cell_order <- factor(c(
  # Naive B cells
  "NBC", "NBC early activation", "NBC IFN-activated", "NBC CD229+",
  "Early GC-commited NBC", "GC-commited NBC", "preGC",
  
  # GC Dark Zone
  "DZ early Sphase", "DZ late Sphase", "DZ early G2Mphase", 
  "DZ late G2Mphase", "DZ non proliferative", "DZ migratory PC precursor",
  
  # Transitions
  "DZ_LZ transition", "LZ_DZ transition", "DZ cell cycle exit",
  
  # GC Light Zone  
  "LZ", "LZ proliferative", "LZ_DZ reentry commitment",
  "PC committed Light Zone GCBC",
  
  # Memory B cells
  "ncsMBC", "ncsMBC FCRL4/5+", "csMBC", "csMBC FCRL4/5+",
  "Early MBC", "MBC FCRL5+", "Precursor MBCs", "Reactivated proliferative MBCs",
  
  # Plasma cell precursors & PCs
  "IgG+ PC precursor", "IgM+ PC precursor", "IgD PC precursor", 
  "IgM+ early PC precursor", "preMature IgG+ PC", "preMature IgM+ PC",
  "Mature IgG+ PC", "Mature IgA+ PC", "Mature IgM+ PC",
  "Short lived IgM+ PC", "PB", "PB committed early PC precursor",
  "MBC derived early PC precursor", "MBC derived IgA+ PC", 
  "MBC derived IgG+ PC", "MBC derived PC precursor",
  
  # Other (T cells, FDC, etc.)
  "cycling T"
 ), ordered = TRUE)

ct1 <- table(paed_sub$predicted.celltype.l2, paed_sub$seurat_clusters)
df_atlas_cluster <- as.data.frame(ct1)
colnames(df_atlas_cluster) <- c("predicted.celltype.l2", "cluster", "n")


df_filtered <- df_atlas_cluster |>
  group_by(predicted.celltype.l2) |>
  mutate(total = sum(n)) |>
  ungroup() |>
  filter(total >= 50) |>
  mutate(predicted.celltype.l2 = factor(
    predicted.celltype.l2, 
    levels = cell_order 
  )) |>
  filter(!is.na(predicted.celltype.l2)) 

ggplot(df_filtered, aes(x = cluster, y = predicted.celltype.l2, fill = n)) +
  geom_tile() +
  scale_fill_viridis_c(option = "plasma")  +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "Seurat cluster", y = "predicted.celltype.l2 (Tonsil Atlas)",
       fill = "Cell count")


ggplot(df_filtered, aes(x = cluster, y = predicted.celltype.l2, fill = n)) +
  geom_tile() +
  scale_fill_viridis_c(option = "plasma", trans = "log10") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "Seurat cluster", y = "predicted.celltype.l2", fill = "Cell count (log10)")

Level 1 labels

new.cluster.ids <- c("Naive B", #0
                     "Memory B", #1
                     "Non-Cycling GCB", #2
                     "Cycling GCB", #3
                     "Plasma B", #4
                     "Cycling GCB", #5_0
                     "Non-Cycling GCB", #5_1 
                     "Memory B", #5_2
                     "Non-Cycling GCB", #5_3 
                     "Plasma B" ,#6
                     "Pre B" #7
                     )
names(new.cluster.ids) <- levels(paed_sub)
paed_sub <- RenameIdents(paed_sub, new.cluster.ids)
bcell_order <- c("Pre B", "Naive B", "Memory B", "Non-Cycling GCB", "Cycling GCB", "Plasma B")
Idents(paed_sub) <- factor(Idents(paed_sub), levels = bcell_order)
paed_sub$cell_labels_l1 <- Idents(paed_sub)

DimPlot(paed_sub, raster = F, reduction = "umap.bcells", label = T, repel = T)

Per tissue composition

df <- paed_sub@meta.data |>
  count(cell_labels_l1, tissue, name = "n")

df |>
  group_by(cell_labels_l1) |>
  mutate(prop = n / sum(n)) |>
  ungroup() |>
  ggplot(aes(cell_labels_l1, prop, fill = tissue)) +
  geom_col(position = "fill") +
  scale_y_continuous(labels = scales::percent_format()) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "B cells L1", y = "Composition by tissue", fill = "Tissue") +
  ggtitle("Proportions at tissue level")

df_totals <- df |>
  group_by(cell_labels_l1) |>
  summarise(total = sum(n), .groups = "drop")

ggplot(df, aes(cell_labels_l1, n, fill = tissue)) +
  geom_col() +
  geom_text(data = df_totals,
            aes(cell_labels_l1, 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 = "B cells L1", y = "Number of cells", fill = "Tissue") +
  ggtitle("Proportions at tissue level and total cell counts")

Naive B cells

naive_b <-  subset(paed_sub, idents = "Naive B")
keep_cells <- Idents(paed_sub) %in% "Naive B"
naive_b@assays$RNA@cells@.Data <- paed_sub@assays$RNA@cells@.Data[keep_cells, ]
naive_b

An object of class Seurat 
18076 features across 101604 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.bcells

out <- here("output/RDS/Lineages_RDS_combined/SEU_NaiveB_Cells.rds")

if (!file.exists(out)) {
  naive_b <- naive_b %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() 
  
  gc() 
  naive_b <- RunUMAP(naive_b, dims = 1:30, reduction = "pca", reduction.name = "umap.naiveB")

  meta_data_columns <- colnames(naive_b@meta.data)
  columns_to_remove <- grep("^RNA_snn_res", meta_data_columns, value = TRUE)
  naive_b@meta.data <- naive_b@meta.data[, !(colnames(naive_b@meta.data) %in% columns_to_remove)]
  resolutions <- seq(0.1, 0.5, by = 0.1)
  naive_b <- FindNeighbors(naive_b, dims = 1:30, reduction = "pca")
  naive_b <- FindClusters(naive_b, resolution = resolutions )
  
  saveRDS(naive_b, out)

} else {
  naive_b <- readRDS(out)
}

clustree(naive_b, prefix = "RNA_snn_res.")

DimPlot(naive_b, group.by = "RNA_snn_res.0.1", reduction = "umap.naiveB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

DimPlot(naive_b, group.by = "RNA_snn_res.0.4", reduction = "umap.naiveB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

DimPlot(naive_b, group.by = "RNA_snn_res.0.5", reduction = "umap.naiveB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

Choosing resolution 0.1 initially and using FindSubCluster function to split the subclusters.

opt_res <- "RNA_snn_res.0.1"  
n <- nlevels(naive_b$RNA_snn_res.0.1)
naive_b$RNA_snn_res.0.1 <- factor(naive_b$RNA_snn_res.0.1, levels = seq(0,n-1))
naive_b$seurat_clusters <- NULL
naive_b$cluster <- naive_b$RNA_snn_res.0.1
Idents(naive_b) <- naive_b$cluster

Inspecting expression of some markers listed in the Tonsil Atlas Glossary (https://figshare.com/articles/book/GLOSSARY_TONSIL_ATLAS_pdf/24885063)

 features = list(
    NBC_core         = c("FCER2", "IGHM", "IGHD"),
    NBC_early_act    = c("CD69", "IL4R"),
    NBC_IFN_act      = c("ISG15", "IFIT1", "IFI44", "MX1"),
    NBC_CD229        = c("LY9", "LILRA4"),
    NBC_GC_committed = c("NFKB1", "CCL3", "CCND2", "MYC")
  )
for (g in features) {
  print(
    FeaturePlot(
      naive_b,
      features  = g,
      reduction = "umap.naiveB",
      raster    = FALSE,
      label     = TRUE,
      split.by  = "tissue"
    ) #+ ggtitle(paste("Naive marker:", g))
  )
}

Correcting previous labels and defining level 1 and level 2

naive_b$cell_labels <- NULL

Warning: Cannot find cell-level meta data named cell_labels

Idents(naive_b) <- "RNA_snn_res.0.1"
new.cluster.ids <- c("Naïve B", 
                     "Naïve B-IFN", 
                     "Naïve B activated")
names(new.cluster.ids) <- levels(naive_b)
naive_b <- RenameIdents(naive_b, new.cluster.ids)
naive_b$cell_labels_l2 <- Idents(naive_b)

Subclustering Naïve B cells (cluster 0)

naive_b <- FindSubCluster(naive_b, cluster = "Naïve B", graph.name = "RNA_snn", resolution = 0.2, subcluster.name = "NBC")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 74456
Number of edges: 1062973

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8406
Number of communities: 14
Elapsed time: 7 seconds

10 singletons identified. 4 final clusters.

DimPlot(naive_b, raster = F, reduction = "umap.naiveB", label = T, group.by = "NBC", repel = T)

Idents(naive_b) <- naive_b$NBC

Subclustering Naïve B cells activated (cluster 1)

naive_b <- FindSubCluster(naive_b, cluster = "Naïve B activated", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "NBC")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12876
Number of edges: 259900

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9179
Number of communities: 2
Elapsed time: 0 seconds

Idents(naive_b) <- naive_b$NBC
DimPlot(naive_b, reduction = "umap.naiveB", label = TRUE, group.by = "NBC", label.size = 3.5, repel = TRUE, raster = FALSE )

Subclustering Naïve B cells-IFN (cluster 2)

naive_b <- FindSubCluster(naive_b, cluster = "Naïve B-IFN", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "NBC")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 14272
Number of edges: 289523

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9046
Number of communities: 2
Elapsed time: 1 seconds

Idents(naive_b) <- naive_b$NBC
DimPlot(naive_b, reduction = "umap.naiveB", label = TRUE, group.by = "NBC", label.size = 3.5, repel = TRUE, raster = FALSE )

Renaming idents

new_levels <- c(
  "Naïve B_0",
  "Naïve B_1",
  "Naïve B_2",
  "Naïve B_3",
  "Naïve B-IFN_0",
  "Naïve B-IFN_1",
  "Naïve B activated_0",
  "Naïve B activated_1"
)

Seurat::Idents(naive_b) <- factor(
  Seurat::Idents(naive_b),
  levels = new_levels
)

naive_b$cell_labels_l3 <- Idents(naive_b)

DE features of Naive B cells

The marker genes for this Naive B cell subclustering can be found here-

NaiveB_population_subclusters

features.no.mt <- rownames(paed_sub)[!grepl("^MT-", rownames(paed_sub))]
naiveB.markers <- FindAllMarkers(naive_b, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25, features = features.no.mt)

Calculating cluster Naïve B_0

Warning: The `slot` argument of `GetAssayData()` 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: `PackageCheck()` was deprecated in SeuratObject 5.0.0.
ℹ Please use `rlang::check_installed()` 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.

Calculating cluster Naïve B_1

Calculating cluster Naïve B_2

Calculating cluster Naïve B_3

Calculating cluster Naïve B-IFN_0

Calculating cluster Naïve B-IFN_1

Calculating cluster Naïve B activated_0

Calculating cluster Naïve B activated_1

naiveB.markers %>%
    group_by(cluster) %>%
    dplyr::filter(avg_log2FC > 1)

# A tibble: 397 × 7
# Groups:   cluster [7]
   p_val avg_log2FC pct.1 pct.2 p_val_adj cluster   gene   
   <dbl>      <dbl> <dbl> <dbl>     <dbl> <fct>     <chr>  
 1     0       1.12 0.344 0.155         0 Naïve B_0 CD1C   
 2     0       1.26 0.61  0.395         0 Naïve B_1 PLPP5  
 3     0       1.02 0.764 0.579         0 Naïve B_1 KLF2   
 4     0       1.06 0.365 0.235         0 Naïve B_1 MTSS1  
 5     0       1.08 0.3   0.182         0 Naïve B_1 CD200  
 6     0       2.20 0.763 0.248         0 Naïve B_3 RHOBTB2
 7     0       1.98 0.562 0.192         0 Naïve B_3 CRIP3  
 8     0       1.42 0.754 0.428         0 Naïve B_3 LY9    
 9     0       1.13 0.758 0.438         0 Naïve B_3 GM2A   
10     0       1.50 0.562 0.26          0 Naïve B_3 OAS1   
# ℹ 387 more rows

top10 <- naiveB.markers |>
     group_by(cluster) |>
     distinct(gene, .keep_all = TRUE) |>
     top_n(n = 10, wt = avg_log2FC)

naive_b <- ScaleData(
  naive_b,
  features = unique(top10$gene),
  assay = "RNA",
  verbose = FALSE
)

Warning: Different features in new layer data than already exists for
scale.data

DoHeatmap(naive_b, features = unique(top10$gene), angle = 90) + NoLegend()

cluster_colors <- paletteer::paletteer_d("pals::glasbey")[factor(top10$cluster)]

DotPlot(naive_b,    
        features = unique(top10$gene),
        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)")

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

best.wilcox.gene.per.cluster <- naiveB.markers |>
  group_by(cluster) |>
  slice_max(avg_log2FC, n = 1) |>
  select(cluster, gene)

best.wilcox.gene.per.cluster

# A tibble: 8 × 2
# Groups:   cluster [8]
  cluster             gene   
  <fct>               <chr>  
1 Naïve B_0           CD1C   
2 Naïve B_1           PLPP5  
3 Naïve B_2           SNRNP70
4 Naïve B_3           ABCC5  
5 Naïve B-IFN_0       EBI3   
6 Naïve B-IFN_1       EGR2   
7 Naïve B activated_0 IFIT1  
8 Naïve B activated_1 GBP1

FeaturePlot(
  naive_b,
  features = best.wilcox.gene.per.cluster$gene,
  reduction = "umap.naiveB",
  raster = FALSE,
  ncol = 1,
  label = TRUE,
  split.by = "tissue"
)

out_markers <- here("output",
            "CSV_v3","B_cell_lineage",
            paste("Marker_genes_Reclustered_NaiveB.",opt_res, sep = ""))

dir.create(out_markers, recursive = TRUE, showWarnings = FALSE)

for (cl in unique(naiveB.markers$cluster)) {
  cluster_data <- naiveB.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)
  }
}

Markers with avg_log2FC > 1 & p_val_adj < 0.05

marker_counts <- naiveB.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       Naïve B-IFN_0       185
2 Naïve B activated_0        71
3       Naïve B-IFN_1        48
4 Naïve B activated_1        45
5           Naïve B_3        43
6           Naïve B_1         4
7           Naïve B_0         1

ggplot(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()

DE markers/gene between Naive B sub-clusters

run_de_and_vln <- function(
  obj, ids, title_prefix,
  top_n = 10, min.pct = 0.25, logfc.threshold = 0.5,
  run_one_vs_all = TRUE, run_pairwise = TRUE,
  print_table = TRUE
) {

  res_list <- list()

  # Helper function to run DE, plot, and optionally show table
  de_plot <- function(id1, id2, comp_name) {
    res <- FindMarkers(
      obj,
      ident.1 = id1,
      ident.2 = id2,
      only.pos = TRUE,
      min.pct = min.pct,
      logfc.threshold = logfc.threshold,
      features = features.no.mt
    )

    top_genes <- res |>
      arrange(p_val_adj, desc(avg_log2FC)) |>
      head(top_n) |>
      rownames()

    res_list[[comp_name]] <<- res

    cat("\n\n####", title_prefix, ":", comp_name, "\n\n")
    print(
      VlnPlot(obj, features = top_genes, idents = unique(c(id1, id2)), pt.size = 0) +
        patchwork::plot_annotation(title = comp_name)
    )

    if (print_table) {
      print(
        kable(
          head(res, 50),
          caption = paste("Top 50 markers:", comp_name)
        )
      )
    }
  }

  # ---- One-vs-rest ----
  if (run_one_vs_all) {
    for (id in ids) {
      de_plot(id, setdiff(ids, id), paste(id, "vs_rest"))
    }
  }

  # ---- Pairwise ----
  if (run_pairwise && length(ids) > 1) {
    for (pair in combn(ids, 2, simplify = FALSE)) {
      de_plot(pair[1], pair[2], paste(pair[1], "vs", pair[2]))
    }
  }

  invisible(res_list)
}

Naive_B

naive_ids <- c("Naïve B_0","Naïve B_1","Naïve B_2","Naïve B_3")

res_all <- run_de_and_vln(
  naive_b,
  naive_ids,
  title_prefix = "Naive B",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

Naive B : Naïve B_0 vs_rest

Top 50 markers: Naïve B_0 vs_rest
	avg_log2FC	pct.1	pct.2
CR2	1.0943013	0.739	0.372
CR1	1.0583368	0.790	0.443
VASP	0.5959041	0.691	0.413
ARPC1B	0.7420148	0.554	0.283
SEMA7A	1.3006229	0.415	0.148
VPREB3	0.8893962	0.493	0.237
IFNGR1	0.6768016	0.567	0.316
COTL1	0.6101117	0.532	0.283
CD24	0.6758419	0.617	0.371
SPIB	0.6303678	0.515	0.271
NEIL1	0.9365502	0.418	0.180
CD1C	1.3014866	0.344	0.123
LYL1	0.5032970	0.490	0.273
ACP5	0.7901693	0.398	0.189
PDLIM1	0.5307380	0.466	0.264
ELL3	0.5272859	0.429	0.232
CD82	0.5038303	0.416	0.229
ITM2C	1.0671451	0.329	0.148
SBNO2	0.5658423	0.398	0.221
ABI3	0.9038323	0.281	0.107
GCA	0.5154130	0.409	0.236
CALR	0.6105829	0.367	0.194
PTEN	0.5212366	0.380	0.208
CD1D	0.8121041	0.298	0.127
BCL3	1.0732632	0.281	0.113
PFKFB3	0.7008965	0.327	0.159
ADGRG5	0.5889561	0.319	0.153
HIF1A	0.5134259	0.363	0.200
CCND2	0.6768121	0.302	0.145
SIGLEC14	0.6009125	0.327	0.170
MYBL2	0.9268160	0.250	0.093
ZMIZ1	0.6560552	0.285	0.132
PKM	0.6040379	0.290	0.137
PARP9	0.5055112	0.320	0.173
ARL4C	0.7101558	0.294	0.147
PDIA3	0.5888331	0.295	0.151
PLD4	0.5341550	0.298	0.156
TMEM64	0.5935027	0.278	0.138
CAB39	0.5728383	0.286	0.148
CAP1	0.6583138	0.257	0.123
PMEPA1	0.5264641	0.280	0.147
CBFA2T3	0.5987746	0.259	0.127
NEDD9	0.5129055	0.268	0.139
TBC1D9	0.5311728	0.274	0.147
IGKC	0.5912730	0.700	0.658
FOSB	0.5143897	0.341	0.210

Naive B : Naïve B_1 vs_rest

Top 50 markers: Naïve B_1 vs_rest
	avg_log2FC	pct.1	pct.2
PLPP5	1.2486829	0.610	0.399
ADK	0.8967619	0.509	0.391
KLF2	0.7355450	0.764	0.657
CD200	0.9690545	0.300	0.197
MTSS1	0.8905610	0.365	0.266
PALM2-AKAP2	0.6028253	0.655	0.568
ITPR1	0.7027965	0.554	0.470
SELL	0.6571542	0.869	0.787
TSC22D3	0.6080569	0.733	0.654
TXNIP	0.8331760	0.960	0.888
FCRL1	0.6686159	0.841	0.775
RIPOR2	0.5701995	0.697	0.638
CXCR4	0.6159928	0.980	0.926
FCRL2	0.5416509	0.645	0.594
CEMIP2	0.8890314	0.347	0.251
BACH2	0.7894512	0.389	0.298
NLRP1	0.5570718	0.536	0.462
FCRL3	0.8715940	0.370	0.277
CCR7	0.7733287	0.356	0.259
COL19A1	0.5318673	0.497	0.449
RIN3	0.6393596	0.360	0.291
JUN	0.5387165	0.566	0.517
INPP5F	0.6078117	0.361	0.301
SLC9A7	0.6769005	0.293	0.242
SERTAD2	0.5586703	0.375	0.331
H1FX	0.7407872	0.288	0.229
OSBPL10	0.7168181	0.260	0.204
TAPT1	0.7185724	0.280	0.235
HMCES	0.7275604	0.260	0.219
HECTD4	0.5368649	0.334	0.298
MMP17	0.5077700	0.323	0.288
MARCH3	0.5538011	0.265	0.227
LAIR1	0.5385077	0.288	0.256
TCF3	0.5040986	0.301	0.278

Naive B : Naïve B_2 vs_rest

Top 50 markers: Naïve B_2 vs_rest
	p_val	avg_log2FC	pct.1	pct.2	p_val_adj
TTC7A	0.0000000	0.5165675	0.155	0.268	0.0000000
TRABD	0.0000000	0.5728093	0.145	0.251	0.0000000
DPP7	0.0000000	0.5023260	0.175	0.299	0.0000000
TMEM259	0.0000000	0.5439493	0.224	0.378	0.0000000
DOK3	0.0000000	0.6031688	0.150	0.256	0.0000000
TCIRG1	0.0000000	0.5880969	0.186	0.313	0.0000000
MAP4K2	0.0000000	0.5577238	0.160	0.270	0.0000000
PCSK7	0.0000000	0.5666078	0.213	0.355	0.0000000
LYL1	0.0000000	0.5370731	0.235	0.384	0.0000000
LRCH4	0.0000000	0.5198258	0.268	0.437	0.0000000
SPPL2B	0.0000000	0.5085209	0.288	0.464	0.0000000
BCL2	0.0000000	0.6379024	0.196	0.308	0.0000000
SPIB	0.0000000	0.5603278	0.258	0.394	0.0000000
SNRNP70	0.0000000	0.8237379	0.239	0.355	0.0000129
TNK2	0.0000003	0.7207853	0.284	0.418	0.0061112
TMC8	0.0000013	0.5388008	0.549	0.751	0.0231592
STAT6	0.0055913	0.5082840	0.429	0.646	1.0000000
ARHGAP45	0.0216919	0.5320401	0.421	0.628	1.0000000

Naive B : Naïve B_3 vs_rest

Top 50 markers: Naïve B_3 vs_rest
	avg_log2FC	pct.1	pct.2
RHOBTB2	2.3109006	0.763	0.237
CRIP3	1.9283371	0.562	0.194
OAS1	2.1910554	0.562	0.205
GM2A	1.2693532	0.758	0.416
LY9	1.4801828	0.754	0.427
SGPP1	2.0826507	0.456	0.141
DNMT3A	1.4092259	0.563	0.255
UAP1L1	2.4645679	0.379	0.080
SLC23A2	1.6120254	0.487	0.190
NPC2	1.3303654	0.559	0.268
FAM102A	1.5552622	0.524	0.237
OAS3	2.6790336	0.364	0.081
HEXA	1.3140281	0.635	0.353
KCNAB2	1.3725448	0.547	0.267
BHLHE40	1.7426915	0.423	0.145
LRMP	1.4484893	0.758	0.483
GCNT1	1.5382785	0.451	0.177
DOK3	1.3641399	0.509	0.236
ABCC5	2.5868630	0.332	0.066
MFHAS1	2.2742988	0.340	0.077
BRI3	1.4996667	0.422	0.161
CTSA	1.5648923	0.416	0.164
AMDHD2	1.8446157	0.341	0.109
PARP15	1.0104668	0.924	0.698
RENBP	2.1782014	0.285	0.068
FLCN	1.7401721	0.309	0.096
FCMR	0.7032365	0.983	0.909
CD22	0.9194995	0.999	0.955
MS4A1	0.5966725	0.999	0.985
HSD17B4	1.6206934	0.427	0.182
VEGFB	1.1430810	0.624	0.342
FCRL5	1.2379382	0.712	0.452
TPP1	1.0529034	0.684	0.406
SETDB2	1.3875072	0.464	0.224
DTX1	1.0211738	0.699	0.443
GPX4	1.1703884	0.513	0.262
BCL2	1.1117057	0.546	0.289
IRF4	1.2792891	0.467	0.227
SCIMP	1.3176978	0.404	0.177
FCRL3	1.2822809	0.549	0.306
ASAH1	1.7898017	0.251	0.086
SLC35B2	1.5068829	0.313	0.123
FCER2	0.7351614	0.876	0.701
MTHFR	1.1330061	0.478	0.247
RGS1	1.6333301	0.310	0.127
DNPH1	1.4166163	0.279	0.110
FNIP1	1.2094622	0.352	0.158
NEK6	1.1815398	0.374	0.179
DUSP10	1.2605966	0.294	0.124
TMC8	0.5592879	0.884	0.730

Naive B : Naïve B_0 vs Naïve B_1

Top 50 markers: Naïve B_0 vs Naïve B_1
	avg_log2FC	pct.1	pct.2
CR2	1.1582211	0.739	0.378
CR1	1.0642484	0.790	0.462
ARPC1B	0.9409977	0.554	0.276
SEMA7A	1.5043452	0.415	0.143
VASP	0.6514463	0.691	0.424
IFI30	0.6768547	0.667	0.413
COTL1	0.7785790	0.532	0.281
VPREB3	0.9542619	0.493	0.244
SPIB	0.7877113	0.515	0.272
NEIL1	1.0901721	0.418	0.179
RASSF2	0.5121450	0.648	0.415
ACP5	1.1765006	0.398	0.167
IFNGR1	0.6457537	0.567	0.336
CD1C	1.5341167	0.344	0.117
CD24	0.6404124	0.617	0.397
LYL1	0.6701760	0.490	0.271
HSPA8	0.5059171	0.607	0.392
DTX1	0.5685935	0.557	0.347
GM2A	0.5294989	0.524	0.320
BOD1L1	0.5214217	0.475	0.280
ELL3	0.6094612	0.429	0.238
MIDN	0.5436064	0.451	0.262
PDLIM1	0.5158785	0.466	0.278
DTX3L	0.5000250	0.468	0.283
ITM2C	1.2661539	0.329	0.146
DOK3	0.8334576	0.330	0.148
EIF4A1	0.5873231	0.404	0.223
CD82	0.5801494	0.416	0.236
ABI3	1.1049660	0.281	0.102
SBNO2	0.6937245	0.398	0.220
CCDC50	0.5264787	0.418	0.241
ACTG1	0.5391583	0.922	0.748
CALR	0.7026195	0.367	0.193
ENO1	0.5838005	0.383	0.211
ECE1	0.5207211	0.398	0.226
STAT1	0.5770873	0.433	0.263
CD1D	0.9211381	0.298	0.130
BCL3	1.1939368	0.281	0.115
CCND2	0.9177097	0.302	0.138
SIGLEC14	0.7810126	0.327	0.164
PFKFB3	0.7266081	0.327	0.168
MYBL2	1.0421172	0.250	0.091
ADGRG5	0.5997705	0.319	0.163
PKM	0.7408592	0.290	0.134
ARL4C	0.9795096	0.294	0.139
PARP9	0.6437346	0.320	0.167
ZMIZ1	0.7430860	0.285	0.132
NLRC5	0.6719252	0.316	0.168
SMG1	0.6206627	0.283	0.143
CBFA2T3	0.7367026	0.259	0.128

Naive B : Naïve B_0 vs Naïve B_2

Top 50 markers: Naïve B_0 vs Naïve B_2
	avg_log2FC	pct.1	pct.2
CR2	0.9115519	0.739	0.246
CD69	1.1709846	0.705	0.212
CR1	0.9249536	0.790	0.307
EIF1	0.7674472	0.657	0.195
FOS	1.6104864	0.641	0.184
DUSP1	1.5773814	0.616	0.160
PLAC8	0.7038282	0.800	0.346
TLR10	0.6051889	0.715	0.263
CD48	0.5262909	0.756	0.309
SMIM14	0.6683164	0.670	0.231
STK17B	0.5420877	0.943	0.509
JUNB	0.8083218	0.689	0.270
CHI3L2	1.0279018	0.610	0.200
IFNGR1	0.8259811	0.567	0.164
JUN	1.2182146	0.572	0.178
SET	0.5260141	0.524	0.159
MIOS	0.6864888	0.464	0.126
FOSB	2.6355781	0.341	0.035
PNRC1	0.5324984	0.934	0.508
ACTR2	0.5474434	0.509	0.153
UBB	0.6121830	0.471	0.133
TMEM123	0.8002998	0.403	0.098
PTEN	0.9028667	0.380	0.086
ETS1	0.5269705	0.855	0.392
KLF6	0.8750126	0.402	0.104
DUSP6	1.0730344	0.361	0.083
PDLIM1	0.6525944	0.466	0.140
EIF4G2	1.0098780	0.331	0.068
UBE2D3	0.5399558	0.430	0.122
VPREB3	0.5163383	0.493	0.162
MOB1A	0.5880795	0.407	0.111
SLC2A3	0.5711888	0.450	0.136
YPEL5	0.6849547	0.401	0.112
RNASE6	0.5177289	0.446	0.137
FAM49B	0.5353488	0.418	0.123
SLC38A2	0.6573852	0.401	0.116
HIST1H1E	0.6355643	0.388	0.108
CD24	0.5106997	0.617	0.257
C1orf162	0.6234566	0.383	0.107
CBX3	0.7166038	0.329	0.078
JUND	0.5035147	0.897	0.497
EIF2S3	0.5550027	0.379	0.106
DDX3X	0.5696992	0.371	0.103
HIF1A	0.6323877	0.363	0.100
PPIA	0.6850323	0.338	0.086
SEMA7A	0.5282684	0.415	0.133
OCIAD1	0.5940909	0.362	0.100
CAND1	0.5229148	0.355	0.098
OTUD1	0.5102905	0.390	0.119
SH3GLB1	0.6337793	0.338	0.090

Naive B : Naïve B_0 vs Naïve B_3

Top 50 markers: Naïve B_0 vs Naïve B_3
	avg_log2FC	pct.1	pct.2
LTB	1.5953676	0.911	0.582
CR1	1.2332642	0.790	0.470
P2RX5	1.1443669	0.791	0.503
ALOX5	1.3033383	0.664	0.342
TCL1A	0.8905755	0.888	0.752
CD24	1.4758905	0.617	0.303
PLAC8	0.9926042	0.800	0.568
ATP2A3	0.6752292	0.908	0.754
CR2	0.8364307	0.739	0.514
STK17B	0.5058952	0.943	0.840
VASP	0.8582783	0.691	0.463
DENND3	0.8968926	0.655	0.411
ANXA6	0.6081402	0.858	0.686
PPP1R18	0.6736898	0.782	0.591
SPIB	1.0873282	0.515	0.282
PFKFB3	1.5059805	0.327	0.138
VPREB3	0.9646585	0.493	0.287
ETS1	0.5198162	0.855	0.709
BCL3	1.7234795	0.281	0.102
SEMA7A	1.1310157	0.415	0.218
RCSD1	0.5362745	0.810	0.654
ADGRG5	1.2487217	0.319	0.137
PXK	0.6071736	0.689	0.506
CLCF1	1.1641898	0.332	0.150
CD1D	1.3654574	0.298	0.125
IFNGR1	0.7505052	0.567	0.375
EBF1	1.1058777	0.328	0.161
IER2	0.6169448	0.678	0.510
CD82	0.8637836	0.416	0.241
PTPN1	0.7667460	0.564	0.396
CXXC5	0.8026839	0.437	0.263
CD38	1.2675128	0.260	0.114
CLEC17A	0.5206579	0.697	0.538
NEIL1	0.7853540	0.418	0.249
ELL3	0.7618904	0.429	0.258
DUSP6	0.8919754	0.361	0.199
ECE1	0.8095040	0.398	0.230
TSPAN3	0.5031073	0.662	0.502
MKNK2	0.5880295	0.571	0.407
TBC1D9	1.0924371	0.274	0.133
CD1C	0.9139222	0.344	0.194
IER5	0.8017481	0.393	0.238
ZNF101	0.7452499	0.408	0.253
GCA	0.7571714	0.409	0.251
ITGB2	0.5860140	0.552	0.394
EMP3	0.6930291	0.425	0.270
PRKD2	0.5186749	0.613	0.465
RGS19	0.5934777	0.517	0.366
PMEPA1	0.9734707	0.280	0.147
TRABD	0.8935245	0.315	0.179

Naive B : Naïve B_1 vs Naïve B_2

Top 50 markers: Naïve B_1 vs Naïve B_2
	avg_log2FC	pct.1	pct.2
TSC22D3	1.0148861	0.733	0.311
DUSP1	1.8215569	0.560	0.160
EIF1	0.9835989	0.586	0.195
SELL	0.7850341	0.869	0.481
JUN	1.6284117	0.566	0.178
PLPP5	0.9210075	0.610	0.242
TXNIP	1.0896800	0.960	0.625
FOS	1.3880894	0.512	0.184
CXCR4	0.6204619	0.980	0.768
CD69	0.9226586	0.558	0.212
BTG1	0.5297167	0.953	0.699
ITPR1	0.7438921	0.554	0.214
PNRC1	0.5624450	0.872	0.508
EVI2B	0.7207148	0.585	0.241
JUNB	0.8791057	0.592	0.270
ADK	0.7633672	0.509	0.191
REL	0.6018724	0.654	0.290
PLEKHF2	0.5456735	0.738	0.361
CTSS	0.5697195	0.574	0.246
SLAMF6	0.5710293	0.545	0.227
KLF2	0.5061848	0.764	0.435
FAM3C	0.7829737	0.406	0.145
MEF2C	0.5190375	0.661	0.323
RHOQ	0.8253160	0.361	0.119
PLAC8	0.5040379	0.687	0.346
RNASE6	0.7320620	0.389	0.137
ZNF318	0.5077038	0.526	0.220
SMIM14	0.5297983	0.532	0.231
JMJD1C	0.5222708	0.511	0.212
CEMIP2	0.7848807	0.347	0.114
KLF6	0.9598259	0.323	0.104
YPEL5	0.8158754	0.338	0.112
MAP3K8	0.7431690	0.374	0.136
SERPINB9	0.5189576	0.480	0.200
CD200	0.8279360	0.300	0.092
GLIPR1	0.5483092	0.426	0.173
MTSS1	0.6003816	0.365	0.135
NUP88	0.5159242	0.412	0.162
IFNAR1	0.6279978	0.355	0.128
PHF3	0.5223699	0.385	0.148
CHI3L2	0.5354775	0.447	0.200
NSD3	0.5591283	0.342	0.126
GNG7	0.5703604	0.349	0.131
PDE4B	0.6038920	0.327	0.120
OTUD1	0.5959323	0.323	0.119
CLIC4	0.5714387	0.324	0.121
DUSP6	0.9328568	0.257	0.083
TP53INP1	0.6667915	0.313	0.117
CENPM	0.6016182	0.346	0.140
LRBA	0.5116398	0.327	0.123

Naive B : Naïve B_1 vs Naïve B_3

Top 50 markers: Naïve B_1 vs Naïve B_3
	avg_log2FC	pct.1	pct.2
SELL	1.0606605	0.869	0.704
LTB	1.1083240	0.781	0.582
TXNIP	0.7038492	0.960	0.929
TCL1A	0.7623090	0.841	0.752
LBH	0.8588684	0.717	0.571
KLF2	0.9041929	0.764	0.656
RIPOR2	0.8342798	0.697	0.595
P2RX5	0.9410847	0.633	0.503
ALOX5	1.0829130	0.512	0.342
PLAC8	0.7928139	0.687	0.568
RASGRP2	0.5998950	0.786	0.709
UCP2	0.5725899	0.831	0.766
PRKCB	0.6673321	0.712	0.639
OSBPL10	1.7654369	0.260	0.107
ADAM19	0.7053315	0.655	0.562
RCSD1	0.6171377	0.723	0.654
NIBAN3	0.5193074	0.751	0.675
SMAP2	0.5766260	0.719	0.673
MTSS1	1.0890539	0.365	0.228
ELOVL5	0.5471132	0.689	0.637
ITPR1	0.7164222	0.554	0.464
EMP3	0.9697333	0.387	0.270
RNF141	0.9516545	0.372	0.267
CDC42SE1	0.5256003	0.647	0.606
TSPAN13	0.6470061	0.514	0.439
DENND3	0.6844167	0.493	0.411
JUN	0.7408514	0.566	0.482
ITGA4	0.5194585	0.603	0.546
GLIPR1	0.7573355	0.426	0.336
TSPAN3	0.5546792	0.565	0.502
DGKD	0.5692243	0.557	0.493
CD24	0.8354781	0.397	0.303
MYO1G	0.7439317	0.389	0.316
PXK	0.5247918	0.544	0.506
SLC2A1	0.8425771	0.315	0.235
SNX22	0.8248075	0.315	0.232
FAM43A	0.8264574	0.271	0.186
BACH2	0.7250198	0.389	0.322
HIP1R	0.8149343	0.310	0.230
TAPT1	0.9330171	0.280	0.205
IQSEC1	0.5262831	0.554	0.520
CD200	0.7559460	0.300	0.217
BIRC3	0.5725970	0.410	0.335
GUCD1	0.6936251	0.387	0.328
KIAA0040	0.6962343	0.383	0.323
LYST	0.6185155	0.379	0.314
TLE5	0.5880644	0.430	0.389
VIM	0.6409758	0.373	0.301
H1FX	0.8763971	0.288	0.217
APLP2	0.6747122	0.361	0.300

Naive B : Naïve B_2 vs Naïve B_3

Top 50 markers: Naïve B_2 vs Naïve B_3
	avg_log2FC	pct.1	pct.2	p_val_adj
LTB	1.5404139	0.660	0.582	0e+00
IGHG3	1.4115692	0.127	0.332	0e+00
CD79A	0.5192859	0.901	0.986	0e+00
ATP2A3	0.9241520	0.638	0.754	0e+00
ARHGEF1	0.6943000	0.630	0.799	0e+00
ALOX5	1.4375879	0.362	0.342	0e+00
SPSB3	0.5056510	0.134	0.251	0e+00
RASGRP2	0.7895625	0.566	0.709	0e+00
CRTC2	0.5093117	0.149	0.269	0e+00
CARS2	0.5728696	0.145	0.261	0e+00
FUS	0.5059866	0.202	0.356	0e+00
NCF4	0.5165838	0.142	0.255	0e+00
AKNA	0.7041323	0.586	0.758	0e+00
MYO15B	0.5133032	0.193	0.334	0e+00
RGL2	0.5245360	0.178	0.311	0e+00
CYBA	0.5381552	0.654	0.846	0e+00
DHX38	0.5290994	0.147	0.258	0e+00
NIBAN3	0.8077615	0.550	0.675	0e+00
LPCAT1	0.5160810	0.166	0.287	0e+00
AC119396.1	0.5546765	0.154	0.268	0e+00
TMEM259	0.5631994	0.224	0.385	0e+00
CSNK1G2	0.5433208	0.164	0.283	0e+00
ITPR3	0.5382995	0.172	0.293	0e+00
GRK6	0.5588730	0.171	0.293	0e+00
ARHGAP27	0.5782511	0.154	0.266	0e+00
C16orf54	0.5534955	0.161	0.275	0e+00
ATG16L2	0.6010646	0.160	0.274	0e+00
PATZ1	0.5409211	0.150	0.257	0e+00
MAPKAPK2	0.5623337	0.165	0.282	0e+00
SBNO2	0.5516534	0.176	0.297	0e+00
TCL1A	0.6860900	0.596	0.752	0e+00
KDM5C	0.5206639	0.246	0.414	0e+00
TMEM63A	0.5806734	0.174	0.293	0e+00
GMIP	0.5381928	0.247	0.418	0e+00
PAX5	0.5895240	0.611	0.800	0e+00
CARMIL2	0.5089084	0.266	0.448	0e+00
CAPG	0.5509861	0.165	0.273	0e+00
MPRIP	0.5264776	0.228	0.376	0e+00
ANKRD13D	0.5529100	0.202	0.331	0e+00
NOTCH1	0.5133954	0.250	0.412	0e+00
MKNK2	0.5734427	0.246	0.407	0e+00
CYTH4	0.6149256	0.212	0.349	0e+00
MAP4K1	0.6107680	0.227	0.374	0e+00
C11orf24	0.5935320	0.161	0.261	0e+00
MYO1G	0.5666920	0.196	0.316	0e+00
WDFY4	0.7582939	0.523	0.688	0e+00
COTL1	0.6094687	0.232	0.377	0e+00
NLRC5	0.6106786	0.162	0.260	0e+00
ETV6	0.6071557	0.164	0.263	1e-07
R3HDM4	0.5933226	0.190	0.304	1e-07

Naive_B-IFN

naive_ids <- c("Naïve B-IFN_0","Naïve B-IFN_1")

res_all <- run_de_and_vln(
  naive_b,
  naive_ids,
  title_prefix = "Naive B-IFN",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

Naive B-IFN : Naïve B-IFN_0 vs_rest

Top 50 markers: Naïve B-IFN_0 vs_rest
	avg_log2FC	pct.1	pct.2
SYNGR2	0.8400687	0.976	0.882
CCND2	1.9018919	0.676	0.332
HSP90AB1	0.9426214	0.857	0.651
SRM	1.7970862	0.491	0.178
NME1	2.2343379	0.387	0.093
IFI30	1.0931192	0.793	0.581
ZMIZ2	1.0901037	0.668	0.383
LPCAT1	1.1946794	0.658	0.390
BHLHE40	2.0394444	0.407	0.134
MAT2A	0.9501801	0.787	0.569
NFKB1	1.0715340	0.674	0.400
HIVEP3	1.5213448	0.462	0.182
NOP56	1.1917986	0.567	0.296
MTHFD1L	1.8920547	0.364	0.113
LCP1	0.5261316	0.969	0.912
ENO1	1.0613182	0.600	0.349
MYBBP1A	1.3594652	0.451	0.195
PRDX1	1.0635023	0.564	0.307
SLC7A1	1.7705608	0.339	0.103
EIF3B	0.9107372	0.674	0.443
GPX4	1.0308672	0.590	0.330
PUS7	2.6233616	0.252	0.043
DDX21	0.9606498	0.643	0.402
NHP2	1.1193863	0.539	0.291
EHD1	1.1348376	0.542	0.293
DNMT3A	1.1489276	0.543	0.298
RFTN1	1.0301542	0.655	0.431
DKC1	1.2250995	0.486	0.238
BZW2	1.3847546	0.408	0.168
CD82	1.2377342	0.545	0.316
FASN	1.8394145	0.309	0.089
ATIC	1.1121562	0.492	0.243
WDR43	1.1462778	0.499	0.256
NOP16	0.9571807	0.578	0.332
EBI3	2.5599455	0.268	0.070
GNL3	0.8342726	0.624	0.377
IFRD2	1.4413451	0.362	0.135
TRMT1	1.0444716	0.529	0.292
NME2	1.0674809	0.524	0.294
NCL	0.9214041	0.617	0.393
AP1S3	1.5455003	0.343	0.129
PPAN	1.0530350	0.517	0.283
POLR1A	1.3008786	0.384	0.160
PKM	1.0345286	0.481	0.247
WDR3	1.2499241	0.383	0.162
PRMT1	0.9560981	0.498	0.261
YBX1	0.9002621	0.572	0.346
PPP1R9B	0.6590034	0.776	0.618
CBFA2T3	1.5084947	0.376	0.170
LARP1	1.1637022	0.415	0.195

Naive B-IFN : Naïve B-IFN_1 vs_rest

Top 50 markers: Naïve B-IFN_1 vs_rest
	avg_log2FC	pct.1	pct.2
EGR1	3.8379346	0.737	0.123
EGR2	3.3592990	0.732	0.159
EGR3	2.6011202	0.955	0.482
NR4A1	2.9942441	0.636	0.227
CD69	2.0257112	0.908	0.664
ZFP36L1	1.0142841	0.991	0.956
CD83	0.9631682	0.983	0.949
CCL3	3.2596167	0.321	0.070
BTG2	1.1712066	0.903	0.789
RASGEF1B	1.7338409	0.603	0.301
NFKBIA	1.1032485	0.851	0.705
TAGAP	1.3596902	0.817	0.645
MYC	1.3774759	0.698	0.431
ZFP36	1.1187792	0.797	0.610
IER2	1.0272715	0.851	0.713
JUNB	0.8733896	0.887	0.743
PNRC1	0.7109954	0.913	0.799
OTUD1	1.5478535	0.483	0.250
IRF8	0.5788616	0.957	0.914
RIPOR2	1.2245106	0.578	0.364
NFKBID	0.8493512	0.630	0.417
TNF	1.6832032	0.414	0.218
CEMIP2	1.5173404	0.337	0.147
PLAC8	0.9365129	0.626	0.416
TRIM38	0.8391131	0.692	0.519
PPP1R15A	1.3988433	0.433	0.253
DUSP2	1.2250238	0.571	0.409
TSPAN13	1.1487270	0.450	0.259
GLIPR1	1.2677542	0.363	0.192
SDCBP	1.1735713	0.400	0.228
LBH	0.9360381	0.557	0.386
FAM214A	1.0669376	0.472	0.299
CSRNP1	1.2000974	0.375	0.213
FCRL1	0.7147574	0.753	0.627
EVI2A	1.2096751	0.393	0.239
RASGRP2	0.6469065	0.740	0.626
WIPF1	0.5590138	0.804	0.719
ZC3H12A	1.0218990	0.496	0.343
MGAT5	0.6842794	0.668	0.524
BTN2A2	1.0318534	0.426	0.272
ZBTB10	1.1311422	0.377	0.226
SELL	0.6129518	0.792	0.658
CD55	0.7768873	0.628	0.503
GGA2	0.5839820	0.769	0.693
ACAP1	0.6762395	0.675	0.532
SLAMF1	0.6104978	0.713	0.581
POU2AF1	0.7618670	0.555	0.417
TRIM22	0.6390853	0.712	0.590
ADAM19	0.6931041	0.613	0.468
MARCH1	1.0231559	0.362	0.217

Naive B-IFN : Naïve B-IFN_0 vs Naïve B-IFN_1

Top 50 markers: Naïve B-IFN_0 vs Naïve B-IFN_1
	avg_log2FC	pct.1	pct.2
SYNGR2	0.8400687	0.976	0.882
CCND2	1.9018919	0.676	0.332
HSP90AB1	0.9426214	0.857	0.651
SRM	1.7970862	0.491	0.178
NME1	2.2343379	0.387	0.093
IFI30	1.0931192	0.793	0.581
ZMIZ2	1.0901037	0.668	0.383
LPCAT1	1.1946794	0.658	0.390
BHLHE40	2.0394444	0.407	0.134
MAT2A	0.9501801	0.787	0.569
NFKB1	1.0715340	0.674	0.400
HIVEP3	1.5213448	0.462	0.182
NOP56	1.1917986	0.567	0.296
MTHFD1L	1.8920547	0.364	0.113
LCP1	0.5261316	0.969	0.912
ENO1	1.0613182	0.600	0.349
MYBBP1A	1.3594652	0.451	0.195
PRDX1	1.0635023	0.564	0.307
SLC7A1	1.7705608	0.339	0.103
EIF3B	0.9107372	0.674	0.443
GPX4	1.0308672	0.590	0.330
PUS7	2.6233616	0.252	0.043
DDX21	0.9606498	0.643	0.402
NHP2	1.1193863	0.539	0.291
EHD1	1.1348376	0.542	0.293
DNMT3A	1.1489276	0.543	0.298
RFTN1	1.0301542	0.655	0.431
DKC1	1.2250995	0.486	0.238
BZW2	1.3847546	0.408	0.168
CD82	1.2377342	0.545	0.316
FASN	1.8394145	0.309	0.089
ATIC	1.1121562	0.492	0.243
WDR43	1.1462778	0.499	0.256
NOP16	0.9571807	0.578	0.332
EBI3	2.5599455	0.268	0.070
GNL3	0.8342726	0.624	0.377
IFRD2	1.4413451	0.362	0.135
TRMT1	1.0444716	0.529	0.292
NME2	1.0674809	0.524	0.294
NCL	0.9214041	0.617	0.393
AP1S3	1.5455003	0.343	0.129
PPAN	1.0530350	0.517	0.283
POLR1A	1.3008786	0.384	0.160
PKM	1.0345286	0.481	0.247
WDR3	1.2499241	0.383	0.162
PRMT1	0.9560981	0.498	0.261
YBX1	0.9002621	0.572	0.346
PPP1R9B	0.6590034	0.776	0.618
CBFA2T3	1.5084947	0.376	0.170
LARP1	1.1637022	0.415	0.195

Naive_B-Activated

naive_ids <- c("Naïve B activated_0","Naïve B activated_1")

res_all <- run_de_and_vln(
  naive_b,
  naive_ids,
  title_prefix = "Naive B-Activated",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

Naive B-Activated : Naïve B activated_0 vs_rest

Top 50 markers: Naïve B activated_0 vs_rest
	avg_log2FC	pct.1	pct.2
IFIT1	3.3929194	0.540	0.139
ISG15	2.7047540	0.681	0.338
OAS3	2.7240499	0.548	0.214
IFI6	2.6444802	0.582	0.262
CMPK2	2.0629185	0.607	0.294
OAS1	1.8915707	0.711	0.401
MX1	2.1252371	0.849	0.556
IFI44	1.4631267	0.793	0.560
LY6E	1.5316758	0.837	0.619
MX2	1.7261878	0.681	0.478
XAF1	1.1676414	0.787	0.653
IFI44L	1.1830349	0.896	0.781
OAS2	1.1848319	0.851	0.752
IRF7	1.2180677	0.809	0.689
IFIT3	2.0331504	0.552	0.296
EIF2AK2	1.3963279	0.660	0.456
HERC5	2.7368925	0.360	0.110
RSAD2	2.5807422	0.374	0.131
HERC6	2.4026739	0.350	0.113
ZBP1	1.9918427	0.427	0.201
USP18	2.6217680	0.308	0.091
IFIT2	1.8883156	0.471	0.285
SAMD9L	1.0521115	0.735	0.674
RNF213	0.8848710	0.770	0.711
ISG20	0.8196066	0.837	0.826
SLFN5	1.3858021	0.509	0.333
HELZ2	1.4243135	0.450	0.288
DRAP1	0.8585704	0.710	0.656
TRIM22	0.5051741	0.906	0.927
DDX60	1.1603079	0.506	0.369
DDX60L	1.4322911	0.416	0.271
HSH2D	0.9882974	0.533	0.431
ADAR	0.5324485	0.782	0.790
PLSCR1	1.1903583	0.418	0.301
JUP	1.5366890	0.307	0.185
SAMD9	1.1986114	0.374	0.255
TRIM25	0.9648850	0.486	0.392
DUSP1	0.8235706	0.592	0.509
LBH	0.6316010	0.671	0.638
EPSTI1	0.5539246	0.729	0.747
FCER2	0.6389317	0.581	0.531
RTCB	1.0034405	0.371	0.297
IFIH1	0.9081863	0.437	0.378
TSC22D3	0.6100916	0.670	0.655
KLF2	0.6093405	0.567	0.536
PARP12	0.6931087	0.457	0.421
PI4K2B	0.9644134	0.324	0.273
PPM1K	0.6121995	0.494	0.480
IFIT5	0.7979580	0.303	0.246
SHISA5	0.5116936	0.556	0.549

Naive B-Activated : Naïve B activated_1 vs_rest

Top 50 markers: Naïve B activated_1 vs_rest
	avg_log2FC	pct.1	pct.2
GBP2	2.0157316	0.713	0.237
GBP4	1.5182638	0.861	0.418
GBP1	1.2646124	0.727	0.314
GBP5	2.0360954	0.575	0.165
WARS	1.6890986	0.543	0.176
IRF1	1.3828729	0.841	0.474
HAPLN3	1.3193073	0.455	0.158
CR1	0.8748697	0.829	0.534
CR2	0.8304897	0.822	0.540
SBNO2	1.0877036	0.596	0.301
BCL3	1.3091765	0.453	0.192
NLRC5	0.7525394	0.656	0.368
SLAMF1	0.8536797	0.573	0.315
PIM2	1.0355775	0.514	0.277
PIM1	1.4877296	0.280	0.096
SOCS1	0.8080687	0.602	0.355
PTPN1	0.7951209	0.721	0.482
CD19	0.5659923	0.875	0.679
EEF1G	0.5113355	0.901	0.727
BCL6	0.8816863	0.406	0.186
GNG10	1.1150000	0.381	0.175
MYBL2	0.7725067	0.361	0.158
CDC42SE2	0.6529883	0.655	0.425
ERAP2	0.6918962	0.545	0.328
APOL2	0.8158037	0.382	0.191
EIF3L	0.6075011	0.585	0.363
SOD2	0.9207330	0.315	0.151
MTHFD2	1.0334797	0.251	0.107
TNFRSF13B	1.0323083	0.289	0.137
HIF1A	0.6671905	0.468	0.271
IFNGR1	0.6331717	0.537	0.334
GCA	0.7015724	0.488	0.292
TP53INP1	0.6623838	0.415	0.231
CHI3L2	0.5933413	0.744	0.566
VMP1	0.7255735	0.392	0.216
HIST1H1E	0.6639839	0.401	0.222
CASP4	0.5176469	0.632	0.428
EEF1B2	0.5168573	0.615	0.408
RAPGEF6	0.5824168	0.553	0.361
ELL3	0.6007360	0.465	0.278
ARPC1B	0.5304369	0.539	0.350
ZNF267	0.8209497	0.254	0.120
PDE4B	0.5256154	0.472	0.288
ZNF101	0.5942186	0.469	0.290
SLC25A6	0.5120975	0.486	0.301
HIST1H1D	0.5562388	0.490	0.309
HINT1	0.6070630	0.395	0.229
LMNB1	0.5199061	0.423	0.249
TBC1D9	0.7789622	0.303	0.164
AMIGO2	0.7223995	0.262	0.134

Naive B-Activated : Naïve B activated_0 vs Naïve B activated_1

Top 50 markers: Naïve B activated_0 vs Naïve B activated_1
	avg_log2FC	pct.1	pct.2
IFIT1	3.3929194	0.540	0.139
ISG15	2.7047540	0.681	0.338
OAS3	2.7240499	0.548	0.214
IFI6	2.6444802	0.582	0.262
CMPK2	2.0629185	0.607	0.294
OAS1	1.8915707	0.711	0.401
MX1	2.1252371	0.849	0.556
IFI44	1.4631267	0.793	0.560
LY6E	1.5316758	0.837	0.619
MX2	1.7261878	0.681	0.478
XAF1	1.1676414	0.787	0.653
IFI44L	1.1830349	0.896	0.781
OAS2	1.1848319	0.851	0.752
IRF7	1.2180677	0.809	0.689
IFIT3	2.0331504	0.552	0.296
EIF2AK2	1.3963279	0.660	0.456
HERC5	2.7368925	0.360	0.110
RSAD2	2.5807422	0.374	0.131
HERC6	2.4026739	0.350	0.113
ZBP1	1.9918427	0.427	0.201
USP18	2.6217680	0.308	0.091
IFIT2	1.8883156	0.471	0.285
SAMD9L	1.0521115	0.735	0.674
RNF213	0.8848710	0.770	0.711
ISG20	0.8196066	0.837	0.826
SLFN5	1.3858021	0.509	0.333
HELZ2	1.4243135	0.450	0.288
DRAP1	0.8585704	0.710	0.656
TRIM22	0.5051741	0.906	0.927
DDX60	1.1603079	0.506	0.369
DDX60L	1.4322911	0.416	0.271
HSH2D	0.9882974	0.533	0.431
ADAR	0.5324485	0.782	0.790
PLSCR1	1.1903583	0.418	0.301
JUP	1.5366890	0.307	0.185
SAMD9	1.1986114	0.374	0.255
TRIM25	0.9648850	0.486	0.392
DUSP1	0.8235706	0.592	0.509
LBH	0.6316010	0.671	0.638
EPSTI1	0.5539246	0.729	0.747
FCER2	0.6389317	0.581	0.531
RTCB	1.0034405	0.371	0.297
IFIH1	0.9081863	0.437	0.378
TSC22D3	0.6100916	0.670	0.655
KLF2	0.6093405	0.567	0.536
PARP12	0.6931087	0.457	0.421
PI4K2B	0.9644134	0.324	0.273
PPM1K	0.6121995	0.494	0.480
IFIT5	0.7979580	0.303	0.246
SHISA5	0.5116936	0.556	0.549

Memory B cells

memory_b <-  subset(paed_sub, idents = "Memory B")
keep_cells <- Idents(paed_sub) %in% "Memory B"
memory_b@assays$RNA@cells@.Data <- paed_sub@assays$RNA@cells@.Data[keep_cells, ]
memory_b

An object of class Seurat 
18076 features across 65617 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.bcells

out1 <- here("output/RDS/Lineages_RDS_combined/SEU_MemoryB_Cells.rds")

if (!file.exists(out1)) {
  memory_b <- memory_b %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() 
  gc() 
  memory_b <- RunUMAP(memory_b, dims = 1:30, reduction = "pca", reduction.name = "umap.memoryB")

  meta_data_columns <- colnames(memory_b@meta.data)
  columns_to_remove <- grep("^RNA_snn_res", meta_data_columns, value = TRUE)
  memory_b@meta.data <- memory_b@meta.data[, !(colnames(memory_b@meta.data) %in% columns_to_remove)]
  resolutions <- seq(0.1, 0.5, by = 0.1)
  memory_b <- FindNeighbors(memory_b, dims = 1:30, reduction = "pca")
  memory_b <- FindClusters(memory_b, resolution = resolutions )

  saveRDS(memory_b, out1)

} else {
  memory_b <- readRDS(out1)
}

clustree(memory_b, prefix = "RNA_snn_res.")

DimPlot(memory_b, group.by = "RNA_snn_res.0.1", reduction = "umap.memoryB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

DimPlot(memory_b, group.by = "RNA_snn_res.0.2", reduction = "umap.memoryB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

DimPlot(memory_b, group.by = "RNA_snn_res.0.4", reduction = "umap.memoryB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

opt_res <- "RNA_snn_res.0.4"  
n <- nlevels(memory_b$RNA_snn_res.0.4)
memory_b$RNA_snn_res.0.4 <- factor(memory_b$RNA_snn_res.0.4, levels = seq(0,n-1))
memory_b$seurat_clusters <- NULL
memory_b$cluster <- memory_b$RNA_snn_res.0.4
Idents(memory_b) <- memory_b$cluster

memory_b$cell_labels <- NULL

Warning: Cannot find cell-level meta data named cell_labels

 features = list(
    MBC         = "CD27",
    ncsMBC    = c("CD267", "IGHD", "IGHM"), # non-class switching MBC
    csMBC      = c("IGHA1", "IGHG1"), # class switching MBC
    ncsMBC_FCRL4_5       = c("FCRL4", "FCRL5", "GSN", "SOX5", "FGR")
  )
for (g in features) {
  print(
    FeaturePlot(
      memory_b,
      features  = g,
      reduction = "umap.memoryB",
      raster    = FALSE,
      label     = TRUE,
      split.by  = "tissue"
    ) 
  )
}

Warning: The following requested variables were not found: CD267

It isn’t really clear what clusters are class switching and non-class switching, According to Azimuth-

table(memory_b$predicted.celltype.l2, memory_b$RNA_snn_res.0.4)

                                
                                    0    1    2    3    4    5    6    7
  CM Pre-non-Tfh                    0    0    0    0    0    1    0    0
  csMBC                          5094  728   85    3  381  108   46   66
  csMBC FCRL4/5+                 1732  293 5266  136   33  388    2   64
  DZ cell cycle exit                0    0    0    0    0    0    1    0
  DZ early Sphase                   0    0    0    0    0    0    1    0
  DZ non proliferative              0    0    0    0    0    0   22    0
  DZ_LZ transition                  3    0    0    0    0    0   44    0
  Early GC-commited NBC            45   64   14   27    0    3    0  162
  Early MBC                       487   52   16    1   39    6  435    9
  GC-commited NBC                  31   60   12   52   15    5   34  222
  IgM+ early PC precursor           1    0    0    0    0    0    0    0
  LZ_DZ reentry commitment          0    1    0    0    0    0    2    0
  MBC derived early PC precursor    1    1    1    1    0    0   43    1
  MBC FCRL5+                     1289   93  283   10  386   36  132  259
  Naive                             1    0    0    0    0    1    2    0
  NBC                            3760 3303  166   20  537  423  910   74
  NBC CD229+                        3    0    0    0    0    0    0    8
  NBC early activation           1218 1236   22   12   37   25   19   29
  NBC IFN-activated                 1   12    8    7    2  969    3    0
  ncsMBC                         3856 6846  261   10  204  378   16   98
  ncsMBC FCRL4/5+                3926 2426 4321   93  127 1109   20  301
  preB                              0    0    0    0    0    0    2    0
  Precursor MBCs                   20    2   20    0    8    0  203   10
  preGC                            32    0    1    1    3    0   29   12
  Proliferative NBC                 0    0    0    0    0    0    1    0
  Reactivated proliferative MBCs    0    0    3    0    0    0    9    0
  Tfh-LZ-GC                         0    0    0    0    1    0    0    0

cell_order <- factor(c(
  # Naive B cells
  "NBC", "NBC early activation", "NBC IFN-activated", "NBC CD229+",
  "Early GC-commited NBC", "GC-commited NBC", "preGC",
  
  # GC Dark Zone
  "DZ early Sphase", "DZ late Sphase", "DZ early G2Mphase", 
  "DZ late G2Mphase", "DZ non proliferative", "DZ migratory PC precursor",
  
  # Transitions
  "DZ_LZ transition", "LZ_DZ transition", "DZ cell cycle exit",
  
  # GC Light Zone  
  "LZ", "LZ proliferative", "LZ_DZ reentry commitment",
  "PC committed Light Zone GCBC",
  
  # Memory B cells
  "ncsMBC", "ncsMBC FCRL4/5+", "csMBC", "csMBC FCRL4/5+",
  "Early MBC", "MBC FCRL5+", "Precursor MBCs", "Reactivated proliferative MBCs",
  
  # Plasma cell precursors & PCs
  "IgG+ PC precursor", "IgM+ PC precursor", "IgD PC precursor", 
  "IgM+ early PC precursor", "preMature IgG+ PC", "preMature IgM+ PC",
  "Mature IgG+ PC", "Mature IgA+ PC", "Mature IgM+ PC",
  "Short lived IgM+ PC", "PB", "PB committed early PC precursor",
  "MBC derived early PC precursor", "MBC derived IgA+ PC", 
  "MBC derived IgG+ PC", "MBC derived PC precursor",
  
  # Other (T cells, FDC, etc.)
  "cycling T"
 ), ordered = TRUE)

ct1 <- table(memory_b$predicted.celltype.l2, memory_b$cluster)
df_atlas_cluster <- as.data.frame(ct1)
colnames(df_atlas_cluster) <- c("predicted.celltype.l2", "cluster", "n")


df_filtered <- df_atlas_cluster |>
  group_by(predicted.celltype.l2) |>
  mutate(total = sum(n)) |>
  ungroup() |>
  filter(total >= 50) |>
  mutate(predicted.celltype.l2 = factor(
    predicted.celltype.l2, 
    levels = cell_order 
  )) |>
  filter(!is.na(predicted.celltype.l2)) 

ggplot(df_filtered, aes(x = cluster, y = predicted.celltype.l2, fill = n)) +
  geom_tile() +
  scale_fill_viridis_c(option = "plasma")  +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "Cluster", y = "predicted.celltype.l2 (Tonsil Atlas)",
       fill = "Cell count")

ggplot(df_filtered, aes(x = cluster, y = predicted.celltype.l2, fill = n)) +
  geom_tile() +
  scale_fill_viridis_c(option = "plasma", trans = "log10") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "Seurat cluster", y = "predicted.celltype.l2", fill = "Cell count (log10)")

Warning in scale_fill_viridis_c(option = "plasma", trans = "log10"): log-10
transformation introduced infinite values.

Idents(memory_b) <- "RNA_snn_res.0.4"
new.cluster.ids <- c("csMBC", 
                     "ncsMBC", 
                     "FCRL4or5",
                     "FCRL4or5",
                     "csMBC",
                     "FCRL4or5",
                     "Early MBC",
                     "FCRL4or5")
names(new.cluster.ids) <- levels(memory_b)
memory_b <- RenameIdents(memory_b, new.cluster.ids)
memory_b$cell_labels_l2 <- Idents(memory_b)
DimPlot(memory_b, raster = F, reduction = "umap.memoryB", label = T, repel = T)

Subclustering Class switching MBC

memory_b <- FindSubCluster(memory_b, cluster = "csMBC", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "MBC")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27042
Number of edges: 478514

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9204
Number of communities: 5
Elapsed time: 2 seconds

3 singletons identified. 2 final clusters.

Idents(memory_b) <- memory_b$MBC
DimPlot(memory_b, reduction = "umap.memoryB", label = TRUE, label.size = 3.5, repel = TRUE, raster = FALSE )

Subclustering Non-class switching MBC

memory_b <- FindSubCluster(memory_b, cluster = "ncsMBC", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "MBC")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 15443
Number of edges: 281121

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9026
Number of communities: 2
Elapsed time: 0 seconds

Idents(memory_b) <- memory_b$MBC
DimPlot(memory_b, reduction = "umap.memoryB", label = TRUE, label.size = 3.5, repel = TRUE, raster = FALSE )

Subclustering FCRL4/5+ cells

memory_b <- FindSubCluster(memory_b, cluster = "FCRL4or5", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "MBC")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21143
Number of edges: 481926

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9316
Number of communities: 4
Elapsed time: 1 seconds

Idents(memory_b) <- memory_b$MBC
DimPlot(memory_b, reduction = "umap.memoryB", label = TRUE, label.size = 3.5, repel = TRUE, raster = FALSE )

Idents(memory_b) <- factor(Idents(memory_b), levels = c("Early MBC","csMBC_0", "csMBC_1", "ncsMBC_0","ncsMBC_1", "FCRL4or5_0","FCRL4or5_1","FCRL4or5_2","FCRL4or5_3"))
memory_b$cell_labels_l3 <- Idents(memory_b)

table(memory_b$cell_labels_l3, memory_b$tissue)

            
             Adenoids   BAL Bronchial_brushings Nasal_brushings Tonsils
  Early MBC        47     2                   3               8    1929
  csMBC_0       12554    24                  90             147    9041
  csMBC_1         689   222                 107            3179     989
  ncsMBC_0       7938    30                  45             167    4165
  ncsMBC_1       2026     6                  15              63     988
  FCRL4or5_0     4349    62                 104             191    6202
  FCRL4or5_1      212   706                1639            2541     137
  FCRL4or5_2     2237     2                  39              96    1158
  FCRL4or5_3      712    13                  22             109     612

df_counts <- memory_b@meta.data |>
  count(cell_labels_l3, tissue, name = "n")
df_totals <- df_counts |>
  group_by(cell_labels_l3) |>
  summarise(total = sum(n), .groups = "drop")

ggplot(df_counts, aes(x = cell_labels_l3, y = n, fill = tissue)) +
  geom_bar(stat = "identity", position = "stack") +
  geom_text(data = df_totals,
            aes(x = cell_labels_l3, 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 = "cell_labels_l3",
       y = "Number of cells",
       fill = "Tissue")

DE features of Memory B cells

The marker genes for this Memory B cell subclustering can be found here-

MemoryB_population_subclusters

gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4764203   254.5   10139294   541.5         NA   10139294   541.5
Vcells 3514245927 26811.6 6081598827 46399.0     163840 6080984109 46394.3

paed_sub.markers <- FindAllMarkers(
  memory_b, only.pos = TRUE,min.pct = 0.25, logfc.threshold = 0.25,features = features.no.mt)

Calculating cluster Early MBC

Calculating cluster csMBC_0

Calculating cluster csMBC_1

Calculating cluster ncsMBC_0

Calculating cluster ncsMBC_1

Calculating cluster FCRL4or5_0

Calculating cluster FCRL4or5_1

Calculating cluster FCRL4or5_2

Calculating cluster FCRL4or5_3

gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4765142   254.5   10139294   541.5         NA   10139294   541.5
Vcells 3514274895 26811.8 6081598827 46399.0     163840 6080984109 46394.3

top10 <- 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: 9 × 2
# Groups:   cluster [9]
  cluster    gene  
  <fct>      <chr> 
1 Early MBC  MEF2B 
2 csMBC_0    TEX9  
3 csMBC_1    IGHG1 
4 ncsMBC_0   PTPRJ 
5 ncsMBC_1   LILRA4
6 FCRL4or5_0 DHRS9 
7 FCRL4or5_1 NR4A1 
8 FCRL4or5_2 IFIT3 
9 FCRL4or5_3 EGR3

FeaturePlot(
  memory_b,
  features = best.wilcox.gene.per.cluster$gene,
  reduction = "umap.memoryB",
  raster = FALSE,
  ncol = 1,
  label = TRUE,
  split.by = "tissue"
)

memory_b <- ScaleData(
  memory_b,
  features = unique(top10$gene),
  assay = "RNA",
  verbose = FALSE
)

Warning: Different features in new layer data than already exists for
scale.data

DoHeatmap(memory_b, features = unique(top10$gene), angle = 90) + NoLegend()

## Seurat top markers
cluster_colors <- paletteer::paletteer_d("pals::glasbey")[factor(top10$cluster)]

DotPlot(memory_b,    
        features = unique(top10$gene),
        group.by = "MBC",
        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)")

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

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 FCRL4or5_1       130
2 FCRL4or5_0        73
3 FCRL4or5_2        63
4   ncsMBC_1        56
5 FCRL4or5_3        51
6   ncsMBC_0        22
7  Early MBC        13
8    csMBC_0         6
9    csMBC_1         1

ggplot(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","B_cell_lineage",
            paste("Marker_genes_Reclustered_MemoryB.",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)
  }
}

DE markers/gene between Memory B sub-clusters

Class switching Memory B cells

mem_ids <- c("csMBC_0", "csMBC_1")

res_all <- run_de_and_vln(
  memory_b,
  mem_ids,
  title_prefix = "csMBC",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

csMBC : csMBC_0 vs_rest

Top 50 markers: csMBC_0 vs_rest
	avg_log2FC	pct.1	pct.2
DUSP1	1.6061086	0.652	0.180
EIF1	0.9946053	0.690	0.234
TSC22D3	0.9580139	0.790	0.336
SELL	0.9738791	0.898	0.454
JUN	1.7767377	0.573	0.164
PLAC8	0.8053995	0.719	0.325
KLF2	0.7296630	0.821	0.437
PNRC1	0.6397612	0.934	0.574
FOSB	2.3559096	0.417	0.063
TXNIP	0.7717838	0.947	0.646
KLF6	0.9979584	0.518	0.177
JUND	0.7302177	0.838	0.471
SCIMP	0.6379611	0.659	0.271
CR1	0.6769531	0.763	0.389
AIM2	0.5352096	0.767	0.349
GPR183	0.6477003	0.581	0.225
JUNB	0.7688705	0.612	0.264
EIF4A2	0.5679080	0.551	0.199
PPP1R15A	1.0529802	0.351	0.089
CD69	0.9861934	0.419	0.137
EVI2B	0.5214540	0.772	0.371
MEF2C	0.5359861	0.633	0.270
FOS	1.3700744	0.342	0.099
TAF7	0.5177084	0.605	0.245
CTSS	0.5415660	0.627	0.266
NFKBIA	0.7145442	0.429	0.143
BMP2K	0.7992549	0.393	0.123
FAM3C	0.6893482	0.429	0.146
RNF19A	0.5490206	0.462	0.165
TSPAN3	0.5245806	0.640	0.288
MARCKSL1	0.5607238	0.481	0.187
MYO1G	0.5219597	0.528	0.211
ITPR1	0.5227466	0.541	0.221
SMIM14	0.5448269	0.629	0.293
GNG7	0.5615935	0.414	0.144
TP53INP1	0.6951894	0.371	0.123
TEX9	0.8293123	0.285	0.073
EAF2	0.5329413	0.414	0.151
HSP90AA1	0.5292988	0.376	0.126
JUP	1.1833381	0.273	0.073
BIRC3	0.6643908	0.358	0.121
LYPLAL1	0.5543856	0.374	0.129
DDX3X	0.6237164	0.364	0.123
CENPM	0.5838450	0.399	0.149
CYSLTR1	0.9218644	0.269	0.069
ZDHHC14	0.5256271	0.409	0.153
HERC4	0.5041560	0.401	0.145
HSPA1B	1.6004907	0.250	0.066
OTUD1	0.7993936	0.286	0.082
CD55	0.5079554	0.494	0.212

csMBC : csMBC_1 vs_rest

Top 50 markers: csMBC_1 vs_rest
	p_val	avg_log2FC	pct.1	pct.2	p_val_adj
IGHM	0.0000000	1.4143597	0.616	0.473	0.0000000
IGHD	0.0000000	1.5990091	0.363	0.212	0.0000000
TRAC	0.0000000	0.5410288	0.149	0.289	0.0000000
MPEG1	0.0000000	0.6606631	0.513	0.620	0.0000000
IGHG3	0.0000000	2.8593245	0.244	0.355	0.0000000
DOK3	0.0000000	0.5461014	0.214	0.331	0.0000000
SMARCB1	0.0000000	0.5254814	0.241	0.377	0.0000000
SBNO2	0.0000000	0.5588660	0.176	0.273	0.0000000
WDFY1	0.0000000	0.5481311	0.163	0.251	0.0000000
SBF1	0.0000000	0.6178918	0.168	0.256	0.0000000
TCIRG1	0.0000000	0.5815054	0.274	0.421	0.0000000
VOPP1	0.0000000	0.6648068	0.202	0.304	0.0000000
GM2A	0.0000000	0.5108908	0.274	0.403	0.0000047
DRAP1	0.0000000	0.6177707	0.237	0.345	0.0000072
ARHGAP24	0.0000000	0.5535911	0.197	0.281	0.0000094
SNRNP70	0.0000000	0.7085054	0.247	0.363	0.0002856
ETV6	0.0000001	0.5854297	0.232	0.329	0.0011294
FCRL5	0.0000025	0.7242486	0.360	0.401	0.0454301
ZEB2	0.0000052	0.5634250	0.266	0.365	0.0931378
CD72	0.0000458	0.9642508	0.299	0.339	0.8282880
PTPN7	0.0001372	0.7746432	0.203	0.273	1.0000000
IGHG1	0.0001624	5.1925353	0.480	0.774	1.0000000
TRABD	0.0004735	0.8393696	0.234	0.318	1.0000000
GRN	0.0041441	0.7146100	0.270	0.367	1.0000000
ARHGAP9	0.0204871	0.5621501	0.363	0.510	1.0000000
SPIB	0.0321025	0.6379678	0.317	0.430	1.0000000
FCRL3	0.0795201	0.6719602	0.238	0.296	1.0000000
FCRL2	0.1294177	0.5884311	0.425	0.551	1.0000000
OAS1	0.3281595	0.7682459	0.222	0.269	1.0000000
CLEC17A	0.3373264	0.8866940	0.254	0.307	1.0000000
CCDC50	0.5365351	0.7695592	0.282	0.367	1.0000000

csMBC : csMBC_0 vs csMBC_1

Top 50 markers: csMBC_0 vs csMBC_1
	avg_log2FC	pct.1	pct.2
DUSP1	1.6061086	0.652	0.180
EIF1	0.9946053	0.690	0.234
TSC22D3	0.9580139	0.790	0.336
SELL	0.9738791	0.898	0.454
JUN	1.7767377	0.573	0.164
PLAC8	0.8053995	0.719	0.325
KLF2	0.7296630	0.821	0.437
PNRC1	0.6397612	0.934	0.574
FOSB	2.3559096	0.417	0.063
TXNIP	0.7717838	0.947	0.646
KLF6	0.9979584	0.518	0.177
JUND	0.7302177	0.838	0.471
SCIMP	0.6379611	0.659	0.271
CR1	0.6769531	0.763	0.389
AIM2	0.5352096	0.767	0.349
GPR183	0.6477003	0.581	0.225
JUNB	0.7688705	0.612	0.264
EIF4A2	0.5679080	0.551	0.199
PPP1R15A	1.0529802	0.351	0.089
CD69	0.9861934	0.419	0.137
EVI2B	0.5214540	0.772	0.371
MEF2C	0.5359861	0.633	0.270
FOS	1.3700744	0.342	0.099
TAF7	0.5177084	0.605	0.245
CTSS	0.5415660	0.627	0.266
NFKBIA	0.7145442	0.429	0.143
BMP2K	0.7992549	0.393	0.123
FAM3C	0.6893482	0.429	0.146
RNF19A	0.5490206	0.462	0.165
TSPAN3	0.5245806	0.640	0.288
MARCKSL1	0.5607238	0.481	0.187
MYO1G	0.5219597	0.528	0.211
ITPR1	0.5227466	0.541	0.221
SMIM14	0.5448269	0.629	0.293
GNG7	0.5615935	0.414	0.144
TP53INP1	0.6951894	0.371	0.123
TEX9	0.8293123	0.285	0.073
EAF2	0.5329413	0.414	0.151
HSP90AA1	0.5292988	0.376	0.126
JUP	1.1833381	0.273	0.073
BIRC3	0.6643908	0.358	0.121
LYPLAL1	0.5543856	0.374	0.129
DDX3X	0.6237164	0.364	0.123
CENPM	0.5838450	0.399	0.149
CYSLTR1	0.9218644	0.269	0.069
ZDHHC14	0.5256271	0.409	0.153
HERC4	0.5041560	0.401	0.145
HSPA1B	1.6004907	0.250	0.066
OTUD1	0.7993936	0.286	0.082
CD55	0.5079554	0.494	0.212

Non-Class switching Memory B cells

mem_ids <- c( "ncsMBC_0","ncsMBC_1")

res_all <- run_de_and_vln(
  memory_b,
  mem_ids,
  title_prefix = "ncsMBC",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

ncsMBC : ncsMBC_0 vs_rest

Top 50 markers: ncsMBC_0 vs_rest
	avg_log2FC	pct.1	pct.2
LTB	1.5000927	0.904	0.624
CD79A	0.5816704	0.993	0.969
CD24	1.1683697	0.791	0.510
ALOX5	1.0899466	0.764	0.505
ATP2A3	0.7380523	0.894	0.763
P2RX5	1.1982622	0.609	0.363
CR1	0.8606342	0.751	0.558
LBH	0.8276861	0.738	0.539
PLAC8	1.0487442	0.647	0.446
NIBAN3	0.7716244	0.703	0.495
BIRC3	1.2345638	0.459	0.244
PTPRJ	1.5662228	0.335	0.137
ITGB7	1.2833842	0.402	0.195
TBC1D9	1.0276029	0.531	0.326
ADAM19	0.7611487	0.667	0.486
CD1D	1.4063110	0.344	0.145
TNFRSF13B	0.6771024	0.772	0.624
PAX5	0.5596504	0.833	0.728
RASGRP2	0.6224150	0.786	0.648
OSBPL10	1.0240192	0.519	0.319
CD1C	0.9183661	0.555	0.357
SLA	0.9148965	0.497	0.291
SELL	0.6067178	0.773	0.617
ETS1	0.5270975	0.820	0.714
PTPRC	0.5291413	0.782	0.670
MOB3B	1.1262265	0.359	0.185
CLEC17A	0.6538095	0.599	0.418
GLIPR1	0.8653029	0.505	0.334
RCSD1	0.5746977	0.759	0.646
PLEKHO1	0.5472146	0.754	0.624
APBB1IP	0.5572090	0.703	0.573
IQSEC1	0.6196726	0.678	0.540
CBLB	0.9064535	0.417	0.246
SIGLEC10	0.8033677	0.539	0.381
PXK	0.6487999	0.620	0.483
EVI2B	0.5075770	0.766	0.669
RIPOR2	0.5711374	0.726	0.608
CCDC141	0.9894317	0.361	0.209
AHNAK	1.0398024	0.351	0.200
CD69	0.6487280	0.624	0.485
TRANK1	0.6986463	0.525	0.378
SPIB	0.7354690	0.499	0.350
KLF2	0.5823767	0.685	0.562
ADGRG5	1.0474097	0.301	0.167
KIAA0040	0.8013927	0.413	0.267
CXXC5	0.6223933	0.537	0.395
MAPRE2	0.5896555	0.580	0.447
IL10RA	0.8274260	0.434	0.298
PFKFB3	0.9862954	0.345	0.209
SEMA7A	0.5961587	0.589	0.460

ncsMBC : ncsMBC_1 vs_rest

Top 50 markers: ncsMBC_1 vs_rest
	avg_log2FC	pct.1	pct.2
RHOBTB2	2.2681169	0.781	0.310
OAS3	3.1753219	0.580	0.132
OAS1	2.2683309	0.786	0.360
FAM102A	1.7313308	0.793	0.468
LILRA4	3.5767993	0.368	0.045
LY9	1.6934658	0.823	0.505
UAP1L1	2.3651891	0.422	0.110
CRIP3	2.3903164	0.394	0.097
SGPP1	1.3660554	0.827	0.538
TEC	2.6026537	0.369	0.085
CD22	1.1424783	0.971	0.907
MS4A1	0.7257480	0.997	0.993
FCMR	0.7640120	0.972	0.908
GM2A	1.2241393	0.784	0.521
KLHL6	1.2748466	0.776	0.517
ASAH1	2.4820348	0.353	0.104
CTSA	1.6096575	0.578	0.280
PARP15	0.7785738	0.931	0.833
SCIMP	1.6093857	0.542	0.249
DTX1	1.5453423	0.566	0.278
TPP1	1.1559260	0.732	0.464
KCNAB2	1.4340258	0.573	0.288
SLC23A2	1.4066789	0.574	0.293
BRI3	1.5690970	0.453	0.182
NEK6	1.3668250	0.653	0.390
SLC35B2	1.7057412	0.445	0.185
HEXA	1.3075926	0.652	0.390
ARID5B	1.2535279	0.693	0.441
RENBP	2.0591460	0.298	0.087
DNMT3A	1.3485399	0.554	0.288
SORL1	1.7957311	0.367	0.135
AMDHD2	1.8783333	0.349	0.125
NPC2	1.2391850	0.580	0.312
MFHAS1	1.6080283	0.394	0.155
OAS2	1.1003543	0.704	0.480
IFI30	1.0141698	0.822	0.666
SLAMF1	1.5797953	0.393	0.166
DOK3	1.0788369	0.634	0.397
SIGLEC6	1.6188471	0.305	0.109
ACP5	1.0960037	0.705	0.511
FCER2	1.2908099	0.515	0.281
BHLHE40	1.0984678	0.574	0.342
FLCN	1.6322797	0.291	0.105
VEGFB	1.0013569	0.629	0.408
TMC8	0.6584677	0.869	0.738
PSAP	1.1050606	0.636	0.437
NFKBIE	1.2174373	0.478	0.266
FCRL5	0.9424738	0.732	0.559
IRF4	1.2331160	0.452	0.239
ST3GAL5	1.0317433	0.577	0.369

ncsMBC : ncsMBC_0 vs ncsMBC_1

Top 50 markers: ncsMBC_0 vs ncsMBC_1
	avg_log2FC	pct.1	pct.2
LTB	1.5000927	0.904	0.624
CD79A	0.5816704	0.993	0.969
CD24	1.1683697	0.791	0.510
ALOX5	1.0899466	0.764	0.505
ATP2A3	0.7380523	0.894	0.763
P2RX5	1.1982622	0.609	0.363
CR1	0.8606342	0.751	0.558
LBH	0.8276861	0.738	0.539
PLAC8	1.0487442	0.647	0.446
NIBAN3	0.7716244	0.703	0.495
BIRC3	1.2345638	0.459	0.244
PTPRJ	1.5662228	0.335	0.137
ITGB7	1.2833842	0.402	0.195
TBC1D9	1.0276029	0.531	0.326
ADAM19	0.7611487	0.667	0.486
CD1D	1.4063110	0.344	0.145
TNFRSF13B	0.6771024	0.772	0.624
PAX5	0.5596504	0.833	0.728
RASGRP2	0.6224150	0.786	0.648
OSBPL10	1.0240192	0.519	0.319
CD1C	0.9183661	0.555	0.357
SLA	0.9148965	0.497	0.291
SELL	0.6067178	0.773	0.617
ETS1	0.5270975	0.820	0.714
PTPRC	0.5291413	0.782	0.670
MOB3B	1.1262265	0.359	0.185
CLEC17A	0.6538095	0.599	0.418
GLIPR1	0.8653029	0.505	0.334
RCSD1	0.5746977	0.759	0.646
PLEKHO1	0.5472146	0.754	0.624
APBB1IP	0.5572090	0.703	0.573
IQSEC1	0.6196726	0.678	0.540
CBLB	0.9064535	0.417	0.246
SIGLEC10	0.8033677	0.539	0.381
PXK	0.6487999	0.620	0.483
EVI2B	0.5075770	0.766	0.669
RIPOR2	0.5711374	0.726	0.608
CCDC141	0.9894317	0.361	0.209
AHNAK	1.0398024	0.351	0.200
CD69	0.6487280	0.624	0.485
TRANK1	0.6986463	0.525	0.378
SPIB	0.7354690	0.499	0.350
KLF2	0.5823767	0.685	0.562
ADGRG5	1.0474097	0.301	0.167
KIAA0040	0.8013927	0.413	0.267
CXXC5	0.6223933	0.537	0.395
MAPRE2	0.5896555	0.580	0.447
IL10RA	0.8274260	0.434	0.298
PFKFB3	0.9862954	0.345	0.209
SEMA7A	0.5961587	0.589	0.460

FCRL4/5+ Memory B cells

mem_ids <- c( "FCRL4or5_0","FCRL4or5_1","FCRL4or5_2","FCRL4or5_3")

res_all <- run_de_and_vln(
  memory_b,
  mem_ids,
  title_prefix = "FCRL4or5",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

FCRL4or5 : FCRL4or5_0 vs_rest

Top 50 markers: FCRL4or5_0 vs_rest
	avg_log2FC	pct.1	pct.2
IFNGR1	1.3729718	0.723	0.325
FCRL4	0.9512132	0.674	0.278
TSPAN3	0.9894633	0.824	0.436
HHEX	1.0020732	0.733	0.347
TRIB2	1.2215299	0.608	0.223
SLC4A7	1.0569550	0.644	0.269
LRRK2	0.9353177	0.721	0.346
CCNG2	0.8773428	0.713	0.341
EVI2B	0.7799391	0.864	0.494
FGR	1.0837904	0.605	0.236
SLAMF6	0.7443635	0.699	0.335
CCDC141	0.9771170	0.624	0.261
RHOQ	0.9019081	0.700	0.339
ADGRG5	0.5392342	0.769	0.410
MARCH1	0.8123578	0.755	0.397
FUT7	0.9967994	0.605	0.249
SOX5	0.8147390	0.621	0.266
OSBPL8	0.8622595	0.648	0.294
MOB1A	0.8144084	0.699	0.345
SESN3	0.6975793	0.857	0.508
FAM49B	0.7734586	0.697	0.349
GAPT	0.8984752	0.619	0.272
ZBED2	1.3844724	0.483	0.137
ETS1	0.6689249	0.879	0.537
BHLHE41	0.9226048	0.762	0.422
FAM214A	0.5680549	0.823	0.485
TLR10	0.8378853	0.913	0.576
METTL7A	0.7168806	0.662	0.326
CLECL1	0.7034118	0.715	0.379
WASHC4	0.6545229	0.726	0.392
RESF1	0.7636337	0.902	0.573
STX7	0.5660515	0.865	0.536
HCK	1.1148400	0.501	0.173
CD47	0.7447616	0.906	0.580
ELK3	0.7054684	0.589	0.263
PFKFB3	0.7058619	0.687	0.362
FAM49A	0.7179805	0.597	0.273
GALNT1	0.8834702	0.558	0.235
PLXNC1	0.9891554	0.533	0.212
SMIM14	0.7653263	0.764	0.443
PTPRC	0.5845872	0.889	0.577
MS4A7	0.8536057	0.576	0.264
MGAT4A	0.7382346	0.572	0.262
ENTPD1	0.6844262	0.819	0.510
FCRL5	0.7726460	0.930	0.623
TTN	0.8748288	0.526	0.219
CD96	1.1442847	0.508	0.207
CBX3	0.8467727	0.466	0.173
CCR6	0.8128635	0.915	0.624
DHRS9	1.7261935	0.379	0.091

FCRL4or5 : FCRL4or5_1 vs_rest

Top 50 markers: FCRL4or5_1 vs_rest
	avg_log2FC	pct.1	pct.2
WFDC2	4.0349844	0.380	0.023
NR4A1	4.2106615	0.375	0.109
NR4A2	3.9447251	0.281	0.084
CFL1	0.8840932	0.837	0.871
ZFP36	1.6379037	0.712	0.678
PFN1	0.6839151	0.927	0.963
ITGAX	1.5393788	0.598	0.455
CORO1A	0.5608385	0.946	0.982
DUSP4	2.4367307	0.435	0.270
CYBA	0.6048758	0.897	0.948
JUND	1.1860058	0.816	0.872
EZR	0.8969320	0.798	0.871
FMNL1	0.8662595	0.762	0.814
COTL1	0.8049097	0.756	0.807
SOCS3	2.2084120	0.356	0.213
KDM6B	2.5393736	0.355	0.218
EGR3	2.1098442	0.299	0.148
TRABD	1.1899356	0.614	0.584
NFKBID	2.6344874	0.305	0.170
LSP1	0.5871224	0.875	0.933
SPIB	1.0325947	0.677	0.677
AKNA	0.6079088	0.834	0.907
ADGRE5	1.3323269	0.649	0.657
IFI30	0.6551252	0.860	0.919
SRGN	1.8465187	0.497	0.454
PLEKHO1	0.6453846	0.788	0.868
TCIRG1	0.9480704	0.663	0.682
IER2	0.9897057	0.683	0.727
CD83	1.4865185	0.587	0.577
DUSP2	2.4099414	0.321	0.215
STAT6	0.7809089	0.712	0.782
TLN1	0.7623649	0.665	0.725
PPP1R9B	0.6919665	0.730	0.808
JUNB	1.1711371	0.665	0.702
CREM	2.0543053	0.338	0.253
METRNL	1.9669860	0.280	0.182
EFHD2	0.9418864	0.563	0.566
TNFRSF13C	0.5205920	0.858	0.926
TYMP	0.9613442	0.552	0.526
GRN	0.8759338	0.637	0.668
ARPC1B	0.7053061	0.631	0.669
PIM3	2.1997649	0.268	0.186
ARHGAP30	0.6917992	0.674	0.766
VASP	0.5896991	0.697	0.784
TAP1	0.6424927	0.712	0.804
CCDC88B	0.7667858	0.639	0.705
H3F3A	0.5114529	0.742	0.854
MAPKAPK2	1.0535174	0.519	0.543
IGHA1	1.0395156	0.542	0.517
ARHGDIA	1.0353375	0.512	0.542

FCRL4or5 : FCRL4or5_2 vs_rest

Top 50 markers: FCRL4or5_2 vs_rest
	avg_log2FC	pct.1	pct.2
IFIT3	3.0273385	0.532	0.142
IFI44L	2.1320345	0.629	0.250
IFIT1	2.9097447	0.475	0.106
XAF1	1.7543418	0.789	0.448
CMPK2	1.9430367	0.567	0.229
STAT1	1.6124238	0.877	0.557
IFI44	1.9697032	0.520	0.212
ISG20	1.3115634	0.807	0.507
EPSTI1	1.5787237	0.751	0.452
EIF2AK2	1.7024914	0.684	0.389
SLFN5	2.1163551	0.469	0.193
IFIT2	2.0522679	0.458	0.201
SAMD9L	1.5790061	0.772	0.518
HERC5	3.0096021	0.322	0.070
TRIM22	1.5357946	0.937	0.685
OAS2	1.4668696	0.858	0.611
TXNIP	1.4480878	0.894	0.653
SELL	1.1542131	0.879	0.644
MX1	1.3744276	0.813	0.597
LY6E	1.1804374	0.826	0.614
IFI16	1.0789824	0.843	0.661
ISG15	1.3550185	0.631	0.370
RSAD2	2.0666016	0.366	0.127
IFI6	1.6490611	0.548	0.292
MX2	1.4666612	0.667	0.449
IRF7	0.9419604	0.834	0.667
CR1	1.2179570	0.733	0.499
RNF213	1.0150739	0.824	0.685
ITGA4	1.2640204	0.640	0.398
CR2	1.3574722	0.544	0.280
OAS3	1.4298778	0.557	0.306
DDX60	1.7541902	0.466	0.240
ADAR	0.8225763	0.835	0.720
AIM2	1.1868281	0.691	0.501
OAS1	1.1766201	0.711	0.532
SP110	0.6767426	0.900	0.805
PARP14	0.8188376	0.845	0.748
UBE2L6	1.0889919	0.673	0.485
HERC6	1.8867878	0.318	0.129
IFIH1	1.3776985	0.480	0.264
ZBTB20	1.6288876	0.407	0.202
SMCHD1	1.0133767	0.684	0.538
APOL6	1.0974406	0.535	0.334
GNG7	1.6065641	0.333	0.148
IFITM1	1.9058396	0.292	0.124
VPREB3	1.6480893	0.281	0.115
STAP1	1.1062776	0.558	0.384
BST2	0.8318930	0.674	0.517
ZBP1	1.4306683	0.416	0.237
EVL	0.5156622	0.912	0.846

FCRL4or5 : FCRL4or5_3 vs_rest

Top 50 markers: FCRL4or5_3 vs_rest
	avg_log2FC	pct.1	pct.2
EGR3	2.2745418	0.572	0.157
CD83	1.5171234	0.924	0.554
ZFP36L1	1.1855603	0.940	0.815
MARCKSL1	2.0811832	0.559	0.210
NFKB2	1.5130486	0.729	0.431
TRAF4	1.7435771	0.684	0.391
REL	1.3865823	0.816	0.620
NFKBIA	1.5000249	0.705	0.413
SYNGR2	0.9189879	0.944	0.878
RELB	1.7780511	0.536	0.242
HIVEP3	2.0531996	0.472	0.200
TNFAIP3	1.7389367	0.481	0.210
EGR2	1.7574602	0.424	0.163
CD69	1.2642557	0.665	0.425
FCER2	1.8862860	0.439	0.203
DUSP2	0.8132792	0.473	0.224
MYC	1.3738630	0.438	0.214
HSP90AB1	0.8169232	0.743	0.570
MAT2A	1.1021638	0.657	0.469
GNL3	1.3104939	0.490	0.282
KDM6B	0.7997236	0.455	0.236
INPP5F	1.0915156	0.604	0.419
SWAP70	0.7469231	0.830	0.750
SLC38A1	0.8704112	0.730	0.601
PNRC1	0.6458392	0.873	0.808
NAB2	1.1738669	0.477	0.284
IL2RG	0.6026298	0.886	0.807
SCIMP	0.9233962	0.687	0.551
NOP16	1.2678837	0.423	0.244
RRAS2	1.6369852	0.318	0.157
NFKBIE	1.0437768	0.546	0.371
IER5	0.7412683	0.735	0.600
ITPKB	1.0949801	0.518	0.344
CCND2	1.3035975	0.433	0.254
BIRC3	1.0765404	0.559	0.394
ZMIZ2	1.0165903	0.554	0.400
DUSP10	1.2095493	0.424	0.252
SAMSN1	1.1434834	0.527	0.376
SLC23A2	1.3173327	0.402	0.235
CR2	0.8335031	0.512	0.310
CD40	0.7473402	0.761	0.672
MDFIC	1.1042520	0.548	0.404
EBI3	1.7055836	0.304	0.156
PHACTR1	0.8617310	0.704	0.601
GPX4	1.1157325	0.489	0.338
SNX11	1.6121154	0.285	0.143
MARCKS	1.1149452	0.394	0.228
FCMR	0.5321356	0.864	0.736
CR1	0.6584725	0.696	0.526
PPRC1	1.3612586	0.283	0.139

FCRL4or5 : FCRL4or5_0 vs FCRL4or5_1

Top 50 markers: FCRL4or5_0 vs FCRL4or5_1
	avg_log2FC	pct.1	pct.2
EVI2B	1.6617870	0.864	0.303
ETS1	1.4746293	0.879	0.343
EIF1	1.4533223	0.750	0.222
PLEKHF2	1.5110906	0.743	0.223
TSPAN3	1.4939734	0.824	0.315
RESF1	1.2990965	0.902	0.405
DRAM2	1.1795051	0.780	0.293
TLR10	1.2325390	0.913	0.426
SESN3	1.1290560	0.857	0.381
SELL	1.4884654	0.788	0.314
LRMP	0.9798055	0.850	0.380
RHOQ	1.3703825	0.700	0.231
IFNGR1	1.5987167	0.723	0.255
CD47	1.1501699	0.906	0.441
FAM214A	0.9743853	0.823	0.360
MOB1A	1.2228108	0.699	0.237
SMIM14	1.4209090	0.764	0.304
SETX	0.9256449	0.796	0.337
WASHC4	1.1470404	0.726	0.267
TUT4	1.0651068	0.710	0.255
CCNG2	1.1601480	0.713	0.261
ST8SIA4	0.8942352	0.843	0.394
HHEX	1.1055717	0.733	0.285
SLAMF6	1.0947747	0.699	0.251
CLK1	0.9397231	0.725	0.277
CTSS	0.9789340	0.771	0.324
CD180	1.0902088	0.721	0.274
LRRK2	1.1917574	0.721	0.276
GAPT	1.3823535	0.619	0.180
CEPT1	1.0765305	0.681	0.243
TAF7	1.0495650	0.680	0.242
PTPRC	0.9044857	0.889	0.451
JMJD1C	0.9081879	0.694	0.259
LNPEP	0.8072141	0.754	0.323
SNX2	1.0493416	0.693	0.262
SNX3	1.1817697	0.649	0.219
ATM	0.7747824	0.828	0.400
ERBIN	0.8692213	0.730	0.303
FAM49B	1.0072482	0.697	0.270
DMXL1	1.1507091	0.637	0.214
MBNL1	0.8017913	0.886	0.466
USP6NL	0.8793090	0.681	0.262
MARCH1	0.9114396	0.755	0.336
STX7	0.7235134	0.865	0.446
OSBPL8	1.0788276	0.648	0.230
UGCG	0.9722610	0.695	0.278
SMC6	1.2495558	0.641	0.225
MIOS	1.1613033	0.610	0.194
LY86	0.8903034	0.713	0.298
RALGPS2	0.8980821	0.751	0.337

FCRL4or5 : FCRL4or5_0 vs FCRL4or5_2

Top 50 markers: FCRL4or5_0 vs FCRL4or5_2
	avg_log2FC	pct.1	pct.2
FCRL4	3.2204685	0.674	0.107
SOX5	3.0007078	0.621	0.098
ITGAX	3.0947252	0.601	0.105
FGR	2.6507315	0.605	0.117
GSN	2.5941909	0.621	0.169
HCK	2.8892137	0.501	0.073
ADGRG5	1.3199044	0.769	0.343
LIMK1	1.3692350	0.734	0.315
RIN3	1.4994818	0.612	0.197
KCTD12	3.1197429	0.474	0.068
FCRL5	1.4823413	0.930	0.538
ZBED2	2.0908096	0.483	0.111
IFI30	1.0238253	0.964	0.809
ACTG1	1.0508611	0.968	0.872
PLD4	1.3102773	0.698	0.309
SPIB	1.1876215	0.778	0.428
CLIP2	1.8164673	0.523	0.147
TRIB2	1.5655471	0.608	0.221
IFNGR1	1.3786780	0.723	0.371
PFKFB3	1.4191344	0.687	0.309
PREX1	1.2343613	0.715	0.346
BHLHE40	1.5322505	0.601	0.222
ZEB2	1.1116011	0.807	0.449
TESC	2.1430077	0.463	0.112
RFTN1	1.2124891	0.686	0.287
FLNA	1.1643584	0.740	0.368
FUT7	1.5177660	0.605	0.230
ITGB2	1.0170260	0.794	0.434
ADAM8	1.3696825	0.596	0.222
DHRS9	2.2328830	0.379	0.074
LSP1	0.6415939	0.974	0.856
GPR34	2.7227593	0.350	0.059
CTSH	0.7487651	0.943	0.754
SLC4A7	1.2039101	0.644	0.284
IL21R	1.6483484	0.427	0.110
SYT11	1.7112914	0.405	0.094
DUSP4	2.2851516	0.351	0.065
VSIR	2.2464637	0.353	0.066
CCR1	2.3293165	0.357	0.070
RASSF2	0.7969615	0.846	0.570
SH3BP5	0.8284513	0.822	0.508
DDIT4	0.9640358	0.781	0.493
COTL1	0.7704804	0.880	0.659
CD82	0.7449710	0.891	0.654
BHLHE41	0.9229875	0.762	0.475
C12orf75	1.9891402	0.361	0.088
SEMA4D	0.8571254	0.773	0.455
ARRB2	1.1037820	0.576	0.247
IVNS1ABP	1.2538561	0.639	0.328
ENTPD1	0.8133910	0.819	0.527

FCRL4or5 : FCRL4or5_0 vs FCRL4or5_3

Top 50 markers: FCRL4or5_0 vs FCRL4or5_3
	avg_log2FC	pct.1	pct.2
FCRL4	2.8978852	0.674	0.110
GSN	3.2217099	0.621	0.103
SOX5	3.0012710	0.621	0.090
FGR	3.1082468	0.605	0.104
HCK	4.2424956	0.501	0.025
THEMIS2	1.7069586	0.780	0.316
ACP5	1.3318307	0.834	0.408
PFKFB3	2.0506393	0.687	0.234
LIMK1	1.6331832	0.734	0.284
PTPN1	1.1278459	0.900	0.586
PLAC8	1.6616059	0.793	0.460
ATP2A3	0.9708655	0.952	0.717
LSP1	0.8599918	0.974	0.810
RIN3	1.9453395	0.612	0.164
FUT7	2.0632060	0.605	0.172
UCP2	0.9884115	0.943	0.703
PREX1	1.4883190	0.715	0.296
TBC1D9	2.0503936	0.572	0.148
SAMD9L	1.6561955	0.666	0.236
MX1	1.6078848	0.702	0.281
KCTD12	2.8730600	0.474	0.087
LBH	1.0632206	0.878	0.531
IL10RA	1.5471671	0.657	0.236
SEMA4D	1.2515198	0.773	0.375
BHLHE41	1.3269012	0.762	0.390
ZBED2	2.3600544	0.483	0.088
CBLB	2.0951509	0.504	0.104
CCR6	0.9445022	0.915	0.656
TNFRSF13B	0.9174447	0.918	0.651
MARCH1	1.2034012	0.755	0.343
RHOH	1.0458392	0.844	0.478
HHEX	1.2760144	0.733	0.343
ITGAX	1.6423553	0.601	0.213
SH3BP5	1.0349396	0.822	0.456
CD19	0.8382504	0.929	0.710
TRIB2	1.6087827	0.608	0.213
CD52	0.6535180	0.990	0.915
ITGB7	1.2470338	0.711	0.319
YPEL3	1.3874979	0.662	0.269
PLD4	1.3130341	0.698	0.314
ADGRG5	1.1224520	0.769	0.385
RIPOR2	0.9100213	0.881	0.567
FCRLA	1.1077434	0.741	0.358
SYK	0.8819439	0.876	0.572
MPEG1	0.8766843	0.888	0.608
GPR34	3.6912094	0.350	0.029
COTL1	0.8784926	0.880	0.623
TCIRG1	1.0582815	0.764	0.404
LRRK2	1.1023688	0.721	0.332
ARRB2	1.4466131	0.576	0.201

FCRL4or5 : FCRL4or5_1 vs FCRL4or5_2

Top 50 markers: FCRL4or5_1 vs FCRL4or5_2
	avg_log2FC	pct.1	pct.2
ITGAX	4.2020825	0.598	0.105
DUSP4	4.3789125	0.435	0.065
WFDC2	3.8553663	0.380	0.023
IFI30	1.4410110	0.860	0.809
PFN1	1.1171542	0.927	0.924
NR4A1	5.6611038	0.375	0.048
SPIB	1.9833991	0.677	0.428
GSN	2.9446112	0.502	0.169
KCTD12	3.8871629	0.394	0.068
CFL1	1.1627039	0.837	0.781
FCRL4	3.0704209	0.440	0.107
SOX5	2.9895165	0.428	0.098
COTL1	1.3678775	0.756	0.659
LSP1	1.0424011	0.875	0.856
TRABD	1.8626922	0.614	0.356
ZFP36	1.7695230	0.712	0.553
BHLHE40	2.2104590	0.504	0.222
CORO1A	0.7270540	0.946	0.965
FMNL1	1.1446240	0.762	0.676
TLN1	1.2993982	0.665	0.525
CYBA	0.7175306	0.897	0.899
ARPC1B	1.3444831	0.631	0.474
AKNA	0.8570120	0.834	0.817
NR4A2	4.0588216	0.281	0.058
SRGN	2.1749414	0.497	0.281
PREX1	1.6633280	0.550	0.346
EFHD2	1.5477181	0.563	0.364
GRN	1.4867120	0.637	0.483
METRNL	3.1304582	0.280	0.061
FGR	2.2969314	0.353	0.117
EGR3	3.3132489	0.299	0.084
ADGRE5	1.6056986	0.649	0.515
DUSP2	3.4509197	0.321	0.109
APOBR	2.9857975	0.258	0.055
IL2RB	3.4808238	0.264	0.065
ACTG1	0.9117874	0.845	0.872
HCK	2.5618936	0.281	0.073
WDFY4	0.7928556	0.831	0.796
LIMK1	1.5695596	0.513	0.315
NFKBID	3.1972019	0.305	0.099
ITGB2	1.3449231	0.588	0.434
CLIP2	2.1782126	0.359	0.147
TESC	2.2011837	0.324	0.112
ADAM8	1.7731394	0.432	0.222
CAPG	1.1796470	0.639	0.525
SRC	2.6685592	0.277	0.082
CD82	1.0357825	0.713	0.654
TCIRG1	1.1121479	0.663	0.545
SIGLEC6	2.2703723	0.295	0.098
ZBTB32	2.1279355	0.354	0.153

FCRL4or5 : FCRL4or5_1 vs FCRL4or5_3

Top 50 markers: FCRL4or5_1 vs FCRL4or5_3
	avg_log2FC	pct.1	pct.2
LSP1	1.2607990	0.875	0.810
ITGAX	2.7497126	0.598	0.213
GSN	3.5721302	0.502	0.103
COTL1	1.4758897	0.756	0.623
TCIRG1	1.8708885	0.663	0.404
CFL1	1.0726985	0.837	0.813
TRABD	1.9437902	0.614	0.336
SOX5	2.9900797	0.428	0.090
WFDC2	2.6578591	0.380	0.046
IFI30	1.2152072	0.860	0.849
CORO1A	0.7858922	0.946	0.952
FCRL4	2.7478376	0.440	0.110
ATP2A3	1.2007129	0.813	0.717
AKNA	1.0442418	0.834	0.774
THEMIS2	1.9948499	0.585	0.316
KCTD12	3.6404800	0.394	0.087
PFN1	0.8377151	0.927	0.941
FMNL1	1.2338682	0.762	0.666
PREX1	1.9172857	0.550	0.296
PTPN1	1.2018637	0.730	0.586
ADGRE5	1.8322012	0.649	0.461
BHLHE40	2.0188572	0.504	0.231
DUSP4	2.7828805	0.435	0.162
HCK	3.9151755	0.281	0.025
WDFY4	0.9151571	0.831	0.777
SOCS3	3.2382987	0.356	0.091
ZFP36	1.5223217	0.712	0.577
SAT1	1.5639689	0.610	0.394
GRN	1.5806622	0.637	0.471
IRF7	1.7677293	0.611	0.428
LIMK1	1.8335078	0.513	0.284
SPIB	1.2910281	0.677	0.526
SIGLEC6	3.4680994	0.295	0.054
FGR	2.7544467	0.353	0.104
ITGB7	1.6851030	0.535	0.319
TYMP	1.5818013	0.552	0.328
HSH2D	1.4259866	0.593	0.424
CBLB	2.4409737	0.346	0.104
S100A11	2.0496893	0.378	0.138
RIN3	2.0795028	0.399	0.164
EFHD2	1.4461937	0.563	0.386
TNFRSF13B	0.9461050	0.750	0.651
PLEKHO1	0.8628788	0.788	0.755
IFI6	3.1407757	0.321	0.101
TLN1	1.0784411	0.665	0.574
APOBR	3.3303673	0.258	0.048
CCDC50	1.2534667	0.581	0.403
ISG15	2.9439957	0.389	0.185
ARPC1B	1.1109492	0.631	0.517
CAPG	1.1486304	0.639	0.537

FCRL4or5 : FCRL4or5_2 vs FCRL4or5_3

Top 50 markers: FCRL4or5_2 vs FCRL4or5_3
	avg_log2FC	pct.1	pct.2
MX1	2.9319316	0.813	0.281
TRIM22	1.7390086	0.937	0.628
SAMD9L	2.9626331	0.772	0.236
XAF1	2.6200571	0.789	0.260
LY6E	2.3576559	0.826	0.384
STAT1	1.8649302	0.877	0.448
IFI44L	4.0120151	0.629	0.100
IRF7	2.0741270	0.834	0.428
ISG20	2.2279320	0.807	0.352
OAS2	1.8920457	0.858	0.485
CMPK2	4.1906982	0.567	0.061
EPSTI1	2.1866869	0.751	0.298
OAS1	2.3169901	0.711	0.230
MX2	2.7057244	0.667	0.222
ISG15	3.2942656	0.631	0.185
IFIT3	4.0488069	0.532	0.062
IFIT1	5.9205717	0.475	0.018
IFI6	3.8245603	0.548	0.101
IFI44	4.0059144	0.520	0.076
EIF2AK2	2.1220074	0.684	0.272
PLAC8	1.7666152	0.791	0.460
RNF213	1.5644929	0.824	0.543
SP110	1.1057573	0.900	0.670
SLFN5	3.5752559	0.469	0.072
APOL6	2.6154211	0.535	0.143
RSAD2	4.9669547	0.366	0.020
IFIT2	3.2193379	0.458	0.095
UBE2L6	1.6198379	0.673	0.330
IFI16	1.1308085	0.843	0.603
PARP9	1.7930052	0.596	0.255
DDX60	2.5032213	0.466	0.124
STAT2	2.0105560	0.552	0.213
ZBP1	2.7705584	0.416	0.102
OAS3	1.9775209	0.557	0.248
TNFSF10	2.7448582	0.409	0.099
SOCS1	1.8890077	0.514	0.200
HERC5	3.7363814	0.322	0.042
BST2	1.3132863	0.674	0.396
ADAR	0.8778047	0.835	0.677
TMEM140	2.2141932	0.423	0.130
USP18	4.5865400	0.266	0.016
GBP4	2.0849820	0.452	0.161
HERC6	2.7878484	0.318	0.061
DTX3L	1.2117832	0.652	0.407
IFITM2	1.2686225	0.638	0.392
PLSCR1	1.8769940	0.418	0.149
DDX60L	2.3259398	0.369	0.112
IFI35	1.9318911	0.409	0.139
TRANK1	1.3466136	0.550	0.286
RIPOR2	0.8749143	0.772	0.567

GC B cells

gc_b <-  subset(paed_sub, idents = c("Non-Cycling GCB", "Cycling GCB"))
keep_cells <- Idents(paed_sub) %in% c("Non-Cycling GCB", "Cycling GCB")
gc_b@assays$RNA@cells@.Data <- paed_sub@assays$RNA@cells@.Data[keep_cells, ]
gc_b

An object of class Seurat 
18076 features across 104759 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.bcells

out2 <- here("output/RDS/Lineages_RDS_combined/SEU_GCB_Cells.rds")

if (!file.exists(out2)) {
  gc_b <- gc_b %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() 
  
  gc() 
  gc_b <- RunUMAP(gc_b, dims = 1:30, reduction = "pca", reduction.name = "umap.gcB")

  meta_data_columns <- colnames(gc_b@meta.data)
  columns_to_remove <- grep("^RNA_snn_res", meta_data_columns, value = TRUE)
  gc_b@meta.data <- gc_b@meta.data[, !(colnames(gc_b@meta.data) %in% columns_to_remove)]
  resolutions <- seq(0.1, 0.5, by = 0.1)
  gc_b <- FindNeighbors(gc_b, dims = 1:30, reduction = "pca")
  gc_b <- FindClusters(gc_b, resolution = resolutions )
  
  saveRDS(gc_b, out2)

} else {
  gc_b <- readRDS(out2)
}

clustree(gc_b, prefix = "RNA_snn_res.")

DimPlot(gc_b, group.by = "RNA_snn_res.0.1", reduction = "umap.gcB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

DimPlot(gc_b, group.by = "RNA_snn_res.0.5", reduction = "umap.gcB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

DimPlot(gc_b, group.by = "cell_labels_l1", reduction = "umap.gcB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

Idents(gc_b) <- gc_b$cell_labels_l1

gc_b$cell_labels <- NULL

Warning: Cannot find cell-level meta data named cell_labels

#gc_b$cell_labels_v2 <- NULL

features <- list(
  # Core DZ identity
  DZ_core = c("CXCR4", "FOXP1", "MME", "AICDA"),

  # DZ early S phase
  DZ_early_S = c("MCM2", "MCM3", "MCM4", "MCM5", "MCM6", "MCM7", "POLA1", "PCNA"),

  # DZ late S phase
  DZ_late_S = c("HIST1H4C", "HIST1H1B", "HIST2H2AH", "HIST1H3D"),

  # DZ early G2M phase
  DZ_early_G2M = c("BUB1", "AURKA", "CDK1"),

  # DZ late G2M phase
  DZ_late_G2M = c("CCNB1", "CCNB2", "CDC20"),

  # DZ cell‑cycle exit
  DZ_exit = c("HMGN1", "HMGN2", "STMN1", "BIRC5"),

  # DZ–LZ transition
  DZ_LZ_transition = c("CD83", "LMO2"),

  # LZ core
  LZ_core = c("BCL2A1", "BCL2L1", "TRAF1", "TRAF4", "EBI3"),

  # LZ–DZ re‑entry
  LZ_DZ_reentry = c("MYC", "MIR155HG", "NFKB1", "NFKB2", "NFKBIA", "NFKBID", "RELB", "BATF"),

  # LZ proliferative
  LZ_prolif = c("MKI67", "TOP2A"),

  # LZ–DZ proliferative transition
  LZ_DZ_prolif_transition = c("TUBA1B", "TUBB")  # representative tubulins
)

for (g in features) {
  print(
    FeaturePlot(
      gc_b,
      features  = g,
      reduction = "umap.gcB",
      raster    = FALSE,
      label     = TRUE,
      split.by  = "tissue"
    ) #+ ggtitle(paste("Naive marker:", g))
  )
}

Warning: The following requested variables were not found: HIST2H2AH, HIST1H3D

Warning: The following requested variables were not found: HMGN2

Warning: The following requested variables were not found: MIR155HG

DimPlot(gc_b, split.by = "Phase", reduction = "umap.gcB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )
table(gc_b$Phase, gc_b$cluster)

Subclustering Non-cycling GCB

gc_b <- FindSubCluster(gc_b, cluster = "Non-Cycling GCB", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "GCBC")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 60662
Number of edges: 1511393

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9255
Number of communities: 9
Elapsed time: 13 seconds

3 singletons identified. 6 final clusters.

Idents(gc_b) <- gc_b$GCBC

gc_b <- FindSubCluster(gc_b, cluster = "Cycling GCB", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "GCBC")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 44097
Number of edges: 1297278

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9316
Number of communities: 22
Elapsed time: 7 seconds

18 singletons identified. 4 final clusters.

Idents(gc_b) <- gc_b$GCBC

#gc_b$cell_labels_l2 <- Idents(gc_b)

Renaming Idents

Idents(gc_b) <- factor(Idents(gc_b), levels = c("Non-Cycling GCB_0","Non-Cycling GCB_1","Non-Cycling GCB_2","Non-Cycling GCB_3","Non-Cycling GCB_4","Non-Cycling GCB_5", "Cycling GCB_0", "Cycling GCB_1", "Cycling GCB_2", "Cycling GCB_3"))
new.cluster.ids <- c(
  "Non-Cycling GCB_0",
  "Non-Cycling GCB_1",
  "Non-Cycling GCB_2",
    "Non-Cycling GCB_3",
  "Cycling GCB_0",
  "Cycling GCB_2",
  "Cycling GCB_0",
    "Cycling GCB_1",
  "Cycling GCB_2",
  "Cycling GCB_3"
)
names(new.cluster.ids) <- levels(gc_b)
gc_b<- RenameIdents(gc_b, new.cluster.ids)
gc_b$cell_labels_l2 <- Idents(gc_b)
gc_b$cell_labels_l3 <- Idents(gc_b)
gc_b$GCBC <- Idents(gc_b)

DimPlot(gc_b, reduction = "umap.gcB", group.by = "GCBC", label = TRUE, label.size = 3.5, repel = TRUE, raster = FALSE )

DE features of Germinal Center B cells

The marker genes for this Germinal Center B cell subclustering can be found here-

GCB_population_subclusters

gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4774295   255.0   10139294   541.5         NA   10139294   541.5
Vcells 4565392107 34831.2 7297998592 55679.4     163840 6081594399 46398.9

paed_sub.markers <- FindAllMarkers(
  gc_b,
  only.pos = TRUE,
  min.pct = 0.25,
  logfc.threshold = 0.25, features = features.no.mt
)

Calculating cluster Non-Cycling GCB_0

Calculating cluster Non-Cycling GCB_1

Calculating cluster Non-Cycling GCB_2

Calculating cluster Non-Cycling GCB_3

Calculating cluster Cycling GCB_0

Calculating cluster Cycling GCB_2

Calculating cluster Cycling GCB_1

Calculating cluster Cycling GCB_3

gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4779578   255.3   10139294   541.5         NA   10139294   541.5
Vcells 4565465678 34831.8 8757678310 66815.8     163840 8744976459 66718.9

top10 <- 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: 8 × 2
# Groups:   cluster [8]
  cluster           gene    
  <fct>             <chr>   
1 Non-Cycling GCB_0 FCER2   
2 Non-Cycling GCB_1 WNK2    
3 Non-Cycling GCB_2 PLAC8   
4 Non-Cycling GCB_3 CCR5    
5 Cycling GCB_0     HIST1H1A
6 Cycling GCB_2     GAS2L3  
7 Cycling GCB_1     DTL     
8 Cycling GCB_3     TUBA1C

FeaturePlot(
  gc_b,
  features = best.wilcox.gene.per.cluster$gene,
  reduction = "umap.gcB",
  raster = FALSE,
  ncol = 1,
  label = TRUE,
  split.by = "tissue"
)

gc_b <- ScaleData(
  gc_b,
  features = unique(top10$gene),
  assay = "RNA",
  verbose = FALSE
)

Warning: Different features in new layer data than already exists for
scale.data

DoHeatmap(gc_b, features = unique(top10$gene), angle = 90) + NoLegend()

cluster_colors <- paletteer::paletteer_d("pals::glasbey")[factor(top10$cluster)]

DotPlot(gc_b,    
        features = unique(top10$gene),
        #group.by = opt_res,
        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)")

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

table(gc_b$tissue)


           Adenoids                 BAL Bronchial_brushings     Nasal_brushings 
              43433                 473                 649                1936 
            Tonsils 
              58268

table(gc_b$tissue, gc_b$GCBC)

                     
                      Non-Cycling GCB_0 Non-Cycling GCB_1 Non-Cycling GCB_2
  Adenoids                        18403              5655               877
  BAL                               237                49                 8
  Bronchial_brushings               158                31                12
  Nasal_brushings                   962               170                61
  Tonsils                         23704              9006              1044
                     
                      Non-Cycling GCB_3 Cycling GCB_0 Cycling GCB_2
  Adenoids                            1          5926          3646
  BAL                                31            57            16
  Bronchial_brushings               136           149            22
  Nasal_brushings                    77           230            94
  Tonsils                            36          7917          5146
                     
                      Cycling GCB_1 Cycling GCB_3
  Adenoids                     6288          2637
  BAL                            32            43
  Bronchial_brushings            77            64
  Nasal_brushings               176           166
  Tonsils                      7247          4168

df_counts <- gc_b@meta.data |>
  count(GCBC, tissue, name = "n")
df_totals <- df_counts |>
  group_by(GCBC) |>
  summarise(total = sum(n), .groups = "drop")

ggplot(df_counts, aes(x = GCBC, y = n, fill = tissue)) +
  geom_bar(stat = "identity", position = "stack") +
  geom_text(data = df_totals,
            aes(x = GCBC, 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 = "clusters after subclustering",
       y = "Number of cells",
       fill = "Tissue")

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 Non-Cycling GCB_3       764
2     Cycling GCB_0       426
3     Cycling GCB_2       307
4     Cycling GCB_3       291
5 Non-Cycling GCB_2       280
6     Cycling GCB_1       196
7 Non-Cycling GCB_0       162
8 Non-Cycling GCB_1       133

ggplot(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","B_cell_lineage",
            paste("Marker_genes_Reclustered_GCB.",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)
  }
}

DE markers/gene between GCB sub-clusters

Non-cycling GCB cells

gc_ids <- c("Non-Cycling GCB_0","Non-Cycling GCB_1","Non-Cycling GCB_2","Non-Cycling GCB_3")

res_all <- run_de_and_vln(
  gc_b,
  gc_ids,
  title_prefix = "Non-Cycling GCB",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

Non-Cycling GCB : Non-Cycling GCB_0 vs_rest

Top 50 markers: Non-Cycling GCB_0 vs_rest
	avg_log2FC	pct.1	pct.2
SEMA7A	2.445011	0.662	0.174
DHRS9	2.443844	0.537	0.085
PTPN6	1.360924	0.905	0.468
LMO2	2.179688	0.913	0.498
ADGRG5	1.833174	0.703	0.295
FCRL5	2.187696	0.582	0.202
CR2	1.670004	0.703	0.326
TMC8	1.477650	0.768	0.395
PLEK	1.446334	0.756	0.384
ITGB2	1.546695	0.704	0.332
CAMK1	2.701002	0.528	0.158
CD72	1.570655	0.815	0.447
PARP14	1.741189	0.626	0.259
SNX22	1.682805	0.574	0.213
CIITA	1.765476	0.869	0.508
RASGRP1	1.813386	0.532	0.177
IER2	1.314060	0.715	0.380
SNX29	1.469083	0.635	0.301
BCAR3	1.551409	0.504	0.180
LPCAT1	1.404621	0.797	0.474
CD83	2.030048	0.799	0.477
HVCN1	1.142694	0.731	0.410
CYTH4	1.416476	0.524	0.206
RELT	1.375363	0.622	0.306
IFI30	1.189907	0.896	0.581
LCK	1.311037	0.598	0.289
SERPINA9	1.287419	0.722	0.415
SLAMF1	2.496763	0.406	0.100
BPNT1	1.225050	0.774	0.473
HIVEP3	2.306393	0.389	0.091
ZNF318	1.151377	0.705	0.407
TTLL3	1.258795	0.752	0.457
SYTL1	1.123089	0.690	0.397
P2RY12	1.933286	0.445	0.156
EHBP1L1	1.114066	0.680	0.393
CALR	1.397983	0.525	0.241
DNAJC10	1.380350	0.886	0.604
SLC7A6	1.171491	0.703	0.422
IL21R	1.133317	0.761	0.482
GEN1	1.347695	0.671	0.394
ERP44	1.223878	0.670	0.396
SIGLEC10	1.698436	0.465	0.191
GMDS	1.590202	0.471	0.199
PIP4K2A	1.023701	0.709	0.437
HTR3A	1.259914	0.551	0.280
PHF6	1.307864	0.743	0.472
ALCAM	2.063876	0.381	0.111
CCDC88B	1.056524	0.607	0.337
CEP85L	1.172535	0.566	0.297
NIBAN1	1.103672	0.605	0.337

Non-Cycling GCB : Non-Cycling GCB_1 vs_rest

Top 50 markers: Non-Cycling GCB_1 vs_rest
	avg_log2FC	pct.1	pct.2
AICDA	2.252572	0.909	0.353
CXCR4	2.131657	0.985	0.569
PIM1	1.770124	0.701	0.293
SLC30A4	1.663007	0.757	0.356
BCR	2.327738	0.580	0.191
USP53	3.041568	0.495	0.122
ST14	1.639492	0.772	0.408
ZNF101	1.687975	0.732	0.368
KANK2	3.934386	0.399	0.039
SLC2A5	1.767687	0.808	0.453
RUBCNL	1.934077	0.808	0.458
PRDM15	1.742024	0.620	0.271
FOXP1	1.862559	0.846	0.501
MME	1.688515	0.894	0.552
PEG10	3.213279	0.409	0.068
MDM2	1.686596	0.729	0.395
PIM2	1.453240	0.706	0.376
IFT57	1.705058	0.673	0.344
STMN1	2.967421	0.431	0.105
HRK	2.332611	0.448	0.123
SUSD3	1.399938	0.896	0.576
PEX5	1.368056	0.656	0.340
ABCA1	2.236008	0.430	0.122
KMT2A	1.611985	0.872	0.570
S100A10	1.143520	0.755	0.454
RNF144B	1.330088	0.668	0.369
CHMP7	1.446216	0.838	0.539
ANK1	1.488092	0.583	0.288
LSM10	1.390769	0.810	0.516
FAM241A	2.103632	0.462	0.169
SLC35E3	1.844576	0.450	0.165
JCHAIN	1.061801	0.881	0.597
HMGB2	2.772198	0.576	0.296
TCF3	1.186161	0.817	0.541
RASSF6	1.553857	0.402	0.127
STK40	1.525963	0.918	0.644
CDK13	1.347986	0.795	0.522
MFHAS1	1.278165	0.616	0.346
CSNK1E	1.952974	0.458	0.190
MYBL2	1.198345	0.794	0.527
TACC3	1.645283	0.526	0.262
WNK2	3.566791	0.289	0.031
BLOC1S6	1.157909	0.664	0.406
NKX6-3	1.921777	0.362	0.107
ORAI2	1.346144	0.800	0.547
RBBP7	1.208163	0.643	0.393
TP53INP1	1.378061	0.611	0.365
RNF19B	1.121367	0.630	0.390
NT5C2	1.207401	0.644	0.409
SH3TC1	1.457741	0.487	0.253

Non-Cycling GCB : Non-Cycling GCB_2 vs_rest

Top 50 markers: Non-Cycling GCB_2 vs_rest
	avg_log2FC	pct.1	pct.2
PLAC8	7.8471514	0.451	0.057
VIM	4.1281460	0.631	0.252
BHLHE41	6.8537099	0.343	0.036
CAPG	5.6682056	0.388	0.082
BHLHE40	6.4806394	0.296	0.031
PREX1	4.9893117	0.333	0.096
TNFRSF13B	4.7345596	0.312	0.077
RAB31	7.1980962	0.252	0.019
UBA52	1.3787967	0.850	0.916
TMSB10	1.6733976	0.804	0.864
EEF2	1.0554863	0.927	0.978
ARHGDIB	1.0744431	0.927	0.975
RAC2	1.5165648	0.904	0.947
ACTG1	1.0445176	0.959	0.987
EEF1G	1.7549086	0.845	0.872
PFN1	1.2803690	0.974	0.984
TMSB4X	1.4664248	0.978	0.987
ACTB	1.2668537	1.000	0.994
LSP1	1.6651880	0.823	0.927
CFL1	1.0474128	0.896	0.960
KCTD12	4.8580190	0.261	0.065
COTL1	1.8546389	0.744	0.806
RACK1	1.1529078	0.807	0.915
PSME1	1.8150128	0.694	0.739
LITAF	3.2441202	0.503	0.327
ANXA6	2.5304799	0.598	0.527
HCLS1	0.9614109	0.798	0.939
CD44	3.2661667	0.446	0.248
MSN	1.7429472	0.672	0.752
CCDC50	3.7158297	0.389	0.210
PTPN1	2.8826505	0.539	0.439
SH3BGRL3	1.7397776	0.659	0.741
BANK1	2.0810093	0.625	0.634
CNN2	1.0417530	0.760	0.895
ARPC1B	1.3109168	0.672	0.784
CORO1A	0.5476379	0.935	0.992
PTPN6	1.0229926	0.734	0.782
CAPZB	1.3331232	0.662	0.809
PLEKHO1	2.3091266	0.541	0.507
TRBC2	3.2455347	0.385	0.227
VASP	1.2555004	0.686	0.796
TOMM7	1.7505626	0.600	0.670
FTH1	1.7033469	0.615	0.708
PSMB9	1.7928487	0.569	0.614
SASH3	1.0550057	0.727	0.910
IFITM2	3.2453784	0.325	0.200
TESC	3.2080982	0.281	0.162
IL16	1.1803700	0.673	0.824
ENO1	1.8055954	0.554	0.635
DTX3L	3.2905141	0.313	0.201

Non-Cycling GCB : Non-Cycling GCB_3 vs_rest

Top 50 markers: Non-Cycling GCB_3 vs_rest
	avg_log2FC	pct.1	pct.2
TYMS	5.225240	0.851	0.055
TNFRSF13B	6.125881	0.868	0.081
FLNA	5.266521	0.879	0.107
CAPG	5.361603	0.847	0.089
ASF1B	5.761061	0.769	0.026
UHRF1	5.596837	0.790	0.053
EMP3	5.419086	0.819	0.124
TK1	4.593733	0.747	0.062
PREX1	4.839204	0.783	0.101
TCF19	5.021420	0.715	0.041
CDT1	4.061909	0.751	0.080
LY6E	5.565437	0.769	0.105
ITGAX	7.125384	0.705	0.042
BHLHE41	4.313481	0.698	0.043
BHLHE40	5.456861	0.690	0.037
KIFC1	4.672234	0.690	0.045
KCTD12	4.741665	0.712	0.068
ZWINT	4.948260	0.669	0.038
ARID3A	4.673324	0.665	0.040
MYO1F	6.292452	0.641	0.023
S100A4	5.626913	0.662	0.046
FCRL4	6.714031	0.662	0.048
AURKB	4.918208	0.648	0.044
CAPN2	6.198728	0.619	0.020
IL2RB	5.588775	0.648	0.049
FOXM1	5.462251	0.584	0.021
ESPL1	5.286797	0.577	0.020
TNFRSF1B	7.706513	0.584	0.031
PKMYT1	7.880483	0.544	0.004
DUSP4	5.894027	0.559	0.021
CDCA5	6.698777	0.537	0.008
CIT	4.405950	0.559	0.030
RAB31	4.337839	0.548	0.024
OAS3	5.286467	0.577	0.059
CALHM2	5.318743	0.544	0.029
RIN3	5.339045	0.559	0.048
GPR183	6.370679	0.559	0.054
CDC45	6.182065	0.512	0.009
ZBTB32	5.467796	0.523	0.023
KIF2C	4.860372	0.516	0.022
RRM2	5.705373	0.516	0.022
CDCA8	3.994105	0.527	0.037
E2F2	6.194287	0.498	0.010
HCK	5.297942	0.509	0.023
CCNF	4.631579	0.502	0.033
TBC1D9	5.483757	0.495	0.034
IMPA2	3.938266	0.498	0.038
WFDC2	6.628162	0.477	0.017
FUT7	5.205117	0.488	0.029
RAD54L	6.153866	0.466	0.008

Non-Cycling GCB : Non-Cycling GCB_0 vs Non-Cycling GCB_1

Top 50 markers: Non-Cycling GCB_0 vs Non-Cycling GCB_1
	avg_log2FC	pct.1	pct.2
SEMA7A	3.154561	0.662	0.149
PTPN6	2.286895	0.905	0.423
DHRS9	3.969974	0.537	0.063
LMO2	2.541670	0.913	0.504
ADGRG5	1.882328	0.703	0.309
FCRL5	2.458953	0.582	0.200
PLEK	1.928068	0.756	0.377
PARP14	1.972400	0.626	0.261
SNX22	1.983134	0.574	0.211
CAMK1	2.860468	0.528	0.168
ITGB2	1.639091	0.704	0.344
TMC8	1.552260	0.768	0.414
CIITA	1.833976	0.869	0.519
RASGRP1	1.954251	0.532	0.185
CR2	1.516725	0.703	0.363
CD72	1.543075	0.815	0.477
IER2	1.655984	0.715	0.378
BCAR3	1.964129	0.504	0.176
CYTH4	1.683656	0.524	0.202
IFI30	1.683998	0.896	0.580
SNX29	1.449568	0.635	0.322
HIVEP3	2.754198	0.389	0.085
SLAMF1	2.583546	0.406	0.104
HVCN1	1.123965	0.731	0.433
CD83	2.008079	0.799	0.509
RELT	1.288443	0.622	0.332
LPCAT1	1.399501	0.797	0.509
ZMIZ1	1.565660	0.491	0.205
LCK	1.313236	0.598	0.317
DDIT4	1.777197	0.443	0.167
BCL2A1	2.490181	0.500	0.226
P2RY12	1.864738	0.445	0.172
CALR	1.421801	0.525	0.254
SYTL1	1.106694	0.690	0.422
EHBP1L1	1.176820	0.680	0.414
SIGLEC10	1.731005	0.465	0.199
PIP4K2A	1.319717	0.709	0.444
PLD4	4.243457	0.293	0.028
ALCAM	2.080518	0.381	0.118
BPNT1	1.224585	0.774	0.513
GMDS	1.745203	0.471	0.210
INF2	1.657713	0.415	0.155
FCER2	2.857043	0.335	0.075
IQSEC1	1.711849	0.440	0.182
IL7	2.636745	0.322	0.065
LGALS9	1.553469	0.452	0.195
CCDC88B	1.131797	0.607	0.352
NAB2	2.544026	0.334	0.079
JAK3	1.029081	0.649	0.394
SERPINA9	1.119300	0.722	0.470

Non-Cycling GCB : Non-Cycling GCB_0 vs Non-Cycling GCB_2

Top 50 markers: Non-Cycling GCB_0 vs Non-Cycling GCB_2
	avg_log2FC	pct.1	pct.2
SYVN1	4.127659	0.918	0.102
SEL1L3	3.886095	0.973	0.169
RBM6	3.613543	0.885	0.084
MICAL3	3.429060	0.941	0.142
TMEM131L	3.687252	0.948	0.150
NEIL1	4.512952	0.872	0.078
DNAJC10	3.684824	0.886	0.097
LBR	3.245323	0.899	0.115
LENG8	3.633000	0.875	0.096
SYNE2	3.067660	0.933	0.155
P2RX5	3.112826	0.945	0.174
CD38	4.423473	0.830	0.061
PTPRC	3.475559	0.902	0.139
RSRP1	3.161706	0.857	0.098
EDEM1	2.974379	0.914	0.156
LPP	3.757201	0.831	0.076
PILRB	4.672972	0.770	0.025
CD40	3.248773	0.950	0.205
ARGLU1	2.548183	0.896	0.152
FCRL1	4.273117	0.807	0.064
PAG1	3.097913	0.862	0.119
HMCES	2.251622	0.904	0.167
S1PR2	3.240704	0.823	0.089
FCRL2	4.372901	0.811	0.078
CLEC2D	3.316602	0.867	0.134
TTLL3	4.969423	0.752	0.021
AMFR	3.029647	0.926	0.202
BCL6	2.407539	0.911	0.188
CCDC88A	2.688342	0.863	0.140
TMC6	3.653726	0.834	0.112
ATP8A1	4.633623	0.764	0.044
SREBF2	2.741568	0.868	0.148
P2RY8	3.622988	0.783	0.063
SEMA4A	3.941793	0.772	0.054
MDM4	2.818892	0.957	0.241
ATP2A3	3.239464	0.962	0.248
MEF2B	1.765477	0.963	0.250
LNPEP	3.211371	0.819	0.112
MCTP2	4.402194	0.738	0.031
SGPP1	2.769522	0.825	0.121
ELL3	2.295767	0.923	0.222
AFF2	2.298675	0.841	0.141
FCRLA	2.680658	0.844	0.147
SORL1	2.443472	0.816	0.119
NLK	2.771764	0.801	0.106
NKTR	3.506520	0.750	0.056
NUGGC	3.554317	0.753	0.059
LUC7L3	2.193545	0.870	0.177
LAT2	2.307509	0.874	0.183
ASH1L	3.532633	0.766	0.078

Non-Cycling GCB : Non-Cycling GCB_0 vs Non-Cycling GCB_3

Top 50 markers: Non-Cycling GCB_0 vs Non-Cycling GCB_3
	avg_log2FC	pct.1	pct.2
MARCKSL1	5.309273	0.972	0.295
MEF2B	5.827558	0.963	0.107
LRMP	3.735241	0.979	0.523
CD22	2.603050	0.993	0.811
SEL1L3	3.491660	0.973	0.448
CD79B	2.650675	0.988	0.722
P2RX5	4.286110	0.945	0.167
RGS13	4.793354	0.922	0.149
HMCES	5.933410	0.904	0.085
KLHL6	2.921519	0.969	0.687
ELL3	4.380318	0.923	0.260
BCL6	4.780875	0.911	0.149
DTX1	4.439041	0.920	0.057
TCL1A	4.772439	0.911	0.075
CD37	2.099184	0.990	0.811
NEIL1	5.432324	0.872	0.068
DNAJC10	4.036875	0.886	0.228
AFF2	5.306030	0.841	0.071
CCDC88A	4.460547	0.863	0.203
IRF8	1.585126	0.994	0.875
MS4A1	1.727996	0.994	0.875
LAPTM5	1.353114	0.995	0.972
RFTN1	1.910495	0.962	0.705
LPP	4.502295	0.831	0.192
PLEKHF2	2.619325	0.904	0.434
SEMA4A	6.387262	0.772	0.014
SYNE2	2.426322	0.933	0.651
SYVN1	2.245679	0.918	0.616
MDM4	1.837577	0.957	0.701
S1PR2	3.743979	0.823	0.206
TMEM131L	1.902408	0.948	0.690
NUGGC	6.779373	0.753	0.018
AMFR	2.206393	0.926	0.633
FNBP1	1.703010	0.957	0.719
SWAP70	1.620494	0.964	0.790
CD79A	1.213295	0.994	0.911
LMO2	2.425182	0.913	0.573
CD40	1.932160	0.950	0.648
UBE2J1	2.199236	0.924	0.687
GGA2	1.547781	0.975	0.858
BCL7A	3.602122	0.809	0.192
MCTP2	6.205765	0.738	0.032
TMEM123	3.942888	0.781	0.149
SERPINA9	7.347111	0.722	0.014
ETS1	1.796963	0.935	0.616
MYO1E	2.072710	0.914	0.580
GNA13	2.383569	0.868	0.441
POU2AF1	1.607699	0.966	0.794
FGD6	5.925968	0.717	0.036
NCOA3	2.152699	0.897	0.527

Non-Cycling GCB : Non-Cycling GCB_1 vs Non-Cycling GCB_2

Top 50 markers: Non-Cycling GCB_1 vs Non-Cycling GCB_2
	avg_log2FC	pct.1	pct.2
MME	5.638969	0.894	0.030
NEIL1	4.795502	0.931	0.078
CD38	4.988370	0.912	0.061
AICDA	2.766739	0.909	0.088
SYNE2	3.514378	0.973	0.155
SUSD3	3.897838	0.896	0.083
SEL1L3	4.067398	0.979	0.169
KMT2A	4.456662	0.872	0.068
CLEC2D	3.968331	0.938	0.134
EDEM1	3.512997	0.958	0.156
JCHAIN	4.697389	0.881	0.088
SLC2A5	6.676591	0.808	0.017
SYVN1	3.819415	0.891	0.102
LPP	3.759378	0.863	0.076
LBR	3.198193	0.890	0.115
P2RX5	3.159254	0.946	0.174
BCL7A	2.473539	0.928	0.162
SEC14L1	2.610884	0.951	0.188
TMEM131L	3.236376	0.912	0.150
RBM6	3.363377	0.841	0.084
BCL6	2.429295	0.941	0.188
ST14	5.714503	0.772	0.020
CXCR4	4.246837	0.985	0.235
P2RY8	3.729705	0.808	0.063
DCAF12	2.331425	0.894	0.149
AFF2	2.556052	0.884	0.141
RSRP1	3.142111	0.839	0.098
DTX1	2.533790	0.982	0.243
SLC30A4	5.381226	0.757	0.019
PTPRC	3.208588	0.876	0.139
FAM214A	3.443963	0.883	0.146
CD27	3.123966	0.889	0.155
S1PR2	3.161887	0.820	0.089
MYBL1	4.486826	0.759	0.029
MDM4	3.054775	0.969	0.241
TMEM156	3.353434	0.832	0.104
ARGLU1	2.520028	0.879	0.152
AMFR	2.915579	0.928	0.202
ELL3	2.270100	0.947	0.222
NUGGC	3.523413	0.780	0.059
ST6GAL1	2.849034	0.939	0.218
OS9	3.231786	0.824	0.107
KLHL5	2.159774	0.869	0.155
MEF2B	1.459807	0.963	0.250
BACH2	3.385106	0.806	0.095
FCRL1	4.288772	0.774	0.064
HMCES	1.830274	0.871	0.167
SGPP1	2.730377	0.823	0.121
FCRL2	4.090242	0.778	0.078
USP34	2.438757	0.926	0.226

Non-Cycling GCB : Non-Cycling GCB_1 vs Non-Cycling GCB_3

Top 50 markers: Non-Cycling GCB_1 vs Non-Cycling GCB_3
	avg_log2FC	pct.1	pct.2
TCL1A	5.663832	0.981	0.075
DTX1	5.304685	0.982	0.057
CD79B	3.237999	0.994	0.722
MEF2B	5.521888	0.963	0.107
SEL1L3	3.672963	0.979	0.448
ELL3	4.354651	0.947	0.260
BCL6	4.802631	0.941	0.149
NEIL1	5.714875	0.931	0.068
IRF8	1.961999	0.999	0.875
P2RX5	4.332537	0.946	0.167
UBE2J1	2.775523	0.973	0.687
EZR	1.880446	0.997	0.943
SEC14L1	3.459511	0.951	0.399
SYNE2	2.873040	0.973	0.651
BCL7A	4.285475	0.928	0.192
MME	6.512223	0.894	0.025
CD37	2.290212	0.991	0.811
LAPTM5	1.485041	0.998	0.972
AFF2	5.563407	0.884	0.071
DCAF12	4.861703	0.894	0.146
POU2AF1	2.135238	0.989	0.794
HMCES	5.512062	0.871	0.085
ETS1	2.219995	0.970	0.616
NCOA3	2.696817	0.943	0.527
AICDA	3.687521	0.909	0.160
LRMP	2.872831	0.949	0.523
JCHAIN	4.406718	0.881	0.100
CLIC4	3.173392	0.909	0.363
CD38	3.314316	0.912	0.256
RGS13	3.935465	0.881	0.149
MDM4	2.073460	0.969	0.701
MARCKSL1	3.803797	0.890	0.295
CLEC2D	2.706076	0.938	0.441
LPP	4.504472	0.863	0.192
USP34	2.575004	0.926	0.591
SLC2A5	6.308102	0.808	0.043
FOXO1	3.438640	0.873	0.246
SUSD3	3.125432	0.896	0.320
KLHL5	3.171269	0.869	0.342
EDEM1	2.011967	0.958	0.683
NUGGC	6.748469	0.780	0.018
BACH2	5.042626	0.806	0.114
FNBP1	1.881458	0.960	0.719
CD27	2.982137	0.889	0.320
ST6GAL1	2.237261	0.939	0.605
TERF2	2.486743	0.904	0.495
GNA13	2.502970	0.897	0.441
MYBL1	7.306966	0.759	0.018
ST14	5.515004	0.772	0.082
GGA2	1.495046	0.983	0.858

Non-Cycling GCB : Non-Cycling GCB_2 vs Non-Cycling GCB_3

Top 50 markers: Non-Cycling GCB_2 vs Non-Cycling GCB_3
	avg_log2FC	pct.1	pct.2
RACK1	1.8887705	0.807	0.829
UBA52	1.2840342	0.850	0.922
ARHGDIB	1.0254284	0.927	0.986
TMSB4X	0.7122633	0.978	1.000
EEF1G	1.3583392	0.845	0.907
EEF2	0.9136063	0.927	0.972
OAZ1	0.7410844	0.962	0.996
ACTB	0.5898635	1.000	0.996
RAC2	0.9410424	0.904	0.975
ACTG1	0.8540465	0.959	0.986
ETS1	1.9460849	0.653	0.616
LCP1	0.8948083	0.911	0.961
TTLL12	0.5298232	0.076	0.299
RAC1	0.5156014	0.114	0.374
UCP2	1.1945254	0.777	0.918
USP5	0.6098675	0.117	0.374
IP6K1	0.5865592	0.105	0.345
BOP1	0.6071143	0.093	0.313
LAGE3	0.5711725	0.119	0.370
CAMKK2	0.6634295	0.097	0.320
POLR2H	0.6883419	0.137	0.409
PDCD5	0.5026527	0.108	0.342
HCLS1	0.8413775	0.798	0.957
NSUN2	0.5232221	0.172	0.477
PRPF4	0.5485451	0.115	0.352
MTLN	0.6464583	0.094	0.306
CFAP20	0.5187680	0.081	0.274
PSMD11	0.6006317	0.108	0.335
RUVBL2	0.6540035	0.113	0.345
MFAP1	0.7350776	0.147	0.420
PSMB5	0.6381873	0.088	0.288
TGS1	0.6706737	0.121	0.359
TCL1A	3.6926205	0.310	0.075
BCKDK	0.6069432	0.121	0.359
GABPB1	0.7254950	0.120	0.356
HEATR1	0.6519023	0.106	0.324
GPI	0.5829059	0.160	0.441
NARS	0.6527729	0.190	0.509
POLR3H	0.7658764	0.076	0.256
TIMM17A	0.5773934	0.150	0.416
TXNRD1	0.5401829	0.187	0.495
TIMM44	0.5928871	0.095	0.295
TSR2	0.6154845	0.101	0.310
ZNF296	0.6807894	0.109	0.327
SUGT1	0.8242193	0.132	0.377
CCNI	1.3118804	0.669	0.786
CDK4	0.6462153	0.214	0.552
PANK3	0.5175509	0.082	0.267
TRAF7	0.5674977	0.109	0.324
TAGLN2	0.6129249	0.188	0.498

Cycling GCB cells

gc_ids <- c("Cycling GCB_0", "Cycling GCB_1", "Cycling GCB_2", "Cycling GCB_3")

res_all <- run_de_and_vln(
  gc_b,
  gc_ids,
  title_prefix = "Cycling GCB",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

Cycling GCB : Cycling GCB_0 vs_rest

Top 50 markers: Cycling GCB_0 vs_rest
	avg_log2FC	pct.1	pct.2
HIST1H2AE	1.9668161	0.879	0.281
HIST1H2BB	1.9919248	0.938	0.355
HIST1H2BH	1.9332121	0.985	0.497
E2F8	1.4040347	0.849	0.364
HIST1H1A	1.9972762	0.645	0.199
RAD54L	1.2876729	0.815	0.370
HIST1H4J	1.8194419	0.599	0.158
HIST1H1D	1.8888341	0.981	0.555
PKMYT1	1.1985973	0.791	0.370
SPC25	1.1166291	0.881	0.460
SLC20A1	1.5047580	0.835	0.417
HIST1H2BN	1.6449886	0.574	0.158
ESCO2	1.0736617	0.711	0.308
HIST1H2AK	2.0282409	0.513	0.117
RRM2	1.2783334	0.971	0.587
FBXO5	0.9034818	0.816	0.444
HIST1H1E	1.5899008	0.972	0.602
MXD3	0.6915314	0.742	0.379
CDCA5	0.7929073	0.776	0.416
ANLN	0.5772537	0.796	0.440
RECQL4	1.1411965	0.660	0.305
E2F2	0.9098880	0.797	0.449
RTN3	1.0382087	0.806	0.458
HIST1H4C	1.6111251	0.990	0.642
DSCC1	0.8617734	0.736	0.389
HIST1H2BG	1.4929048	0.524	0.182
NCAPD3	0.9114078	0.852	0.512
DIAPH3	1.0203923	0.638	0.300
TMEM106C	0.9908682	0.904	0.567
ATG16L2	1.2626531	0.681	0.346
E2F7	1.7861208	0.463	0.128
ESPL1	0.5205386	0.799	0.465
CENPP	0.8068352	0.827	0.494
DONSON	0.8279915	0.775	0.445
TCF19	0.7257852	0.881	0.553
CENPI	0.7733891	0.720	0.393
FOXM1	0.6033070	0.891	0.565
SAC3D1	1.0639012	0.623	0.297
FAM111B	0.6159868	0.760	0.435
FAM111A	0.9273416	0.902	0.579
IMPA2	1.0755300	0.558	0.235
MELK	0.8189593	0.666	0.343
HIST1H1B	1.7902195	0.998	0.675
NEIL3	0.8337949	0.666	0.345
WWOX	0.9139968	0.763	0.444
ATAD2	0.7938541	0.805	0.487
LMF2	1.1037347	0.673	0.356
CDCA4	0.9224562	0.692	0.375
POLA1	0.7029765	0.806	0.490
POLQ	0.7633940	0.750	0.434

Cycling GCB : Cycling GCB_1 vs_rest

Top 50 markers: Cycling GCB_1 vs_rest
	avg_log2FC	pct.1	pct.2
UNG	1.5029655	0.676	0.249
DTL	1.5846622	0.707	0.352
GINS2	1.1630190	0.856	0.512
AEN	1.4420411	0.539	0.226
E2F1	1.1239766	0.894	0.589
ZMYND19	1.4302808	0.544	0.241
FCRL3	1.8335256	0.549	0.251
PPRC1	1.3023364	0.512	0.223
FCRL2	1.5544073	0.709	0.421
HHEX	1.2608534	0.557	0.272
MCM6	0.8232542	0.933	0.656
SLC1A5	1.2510598	0.581	0.304
COLGALT1	1.2964047	0.543	0.272
DCTPP1	0.7751928	0.696	0.425
USP37	1.0502989	0.626	0.363
PLCXD1	1.1202453	0.602	0.339
MYBBP1A	1.0610236	0.512	0.252
PUS1	0.9792840	0.526	0.270
CDC6	0.9195747	0.611	0.357
MCM4	0.8247742	0.948	0.698
GNL3	0.7971390	0.665	0.416
SRPRB	1.4379722	0.455	0.207
BYSL	0.8867540	0.542	0.297
RRS1	0.9446563	0.434	0.191
PPAN	0.9634136	0.529	0.288
CNR2	1.3273783	0.522	0.281
RNF121	1.3486755	0.508	0.267
XRCC3	1.0670239	0.579	0.338
DIS3	0.7995774	0.647	0.407
WDR43	0.8562509	0.598	0.359
CHAF1A	0.7880993	0.842	0.606
ATP6V0A2	1.0125406	0.644	0.409
NOP16	0.7134518	0.627	0.392
RCL1	1.2358903	0.391	0.157
AKAP1	1.4238528	0.441	0.207
FUCA1	1.3451932	0.513	0.279
ZNF141	1.0338451	0.752	0.519
RRP12	1.0634594	0.453	0.221
ADCY3	0.9504196	0.595	0.363
ECE1	0.9068432	0.685	0.455
SP110	0.6297348	0.548	0.318
CXCR5	0.8729686	0.816	0.587
CASP8AP2	0.9689284	0.557	0.330
NOP14	0.6416175	0.646	0.420
GGCT	0.7421778	0.558	0.332
CTPS1	0.7198280	0.608	0.382
INTS7	0.8316443	0.620	0.395
SEMA7A	1.5109109	0.469	0.244
GPATCH4	0.7285786	0.520	0.296
BCL2L11	1.1089305	0.649	0.427

Cycling GCB : Cycling GCB_2 vs_rest

Top 50 markers: Cycling GCB_2 vs_rest
	avg_log2FC	pct.1	pct.2
KIF20A	2.433514	0.899	0.242
PIF1	3.242320	0.818	0.165
PSRC1	2.886712	0.735	0.119
KIF14	2.432388	0.864	0.262
CENPA	2.194573	0.912	0.311
NEK2	2.447029	0.873	0.275
CENPE	2.358962	0.938	0.368
AURKA	2.089659	0.895	0.326
KNSTRN	2.153935	0.914	0.362
TROAP	2.596349	0.893	0.342
PLK1	2.221223	0.992	0.443
DEPDC1	2.348860	0.895	0.347
CDC20	2.187525	0.987	0.451
HMMR	2.303239	0.971	0.439
ARL6IP1	2.696521	0.959	0.433
GPSM2	2.145332	0.802	0.283
ASPM	2.488941	0.919	0.406
CENPF	2.318908	0.975	0.474
SGO2	1.933021	0.882	0.381
DLGAP5	1.761252	0.936	0.442
FAM83D	2.112088	0.749	0.263
KIF23	1.696575	0.917	0.458
ECT2	1.759747	0.854	0.404
NUF2	1.809934	0.907	0.460
MYEF2	2.016720	0.876	0.432
SUN2	1.955142	0.934	0.495
G2E3	1.848847	0.785	0.349
CDKN3	1.623865	0.869	0.436
RNF26	1.693369	0.793	0.365
CCNB1	1.995027	0.979	0.551
BRD8	1.709019	0.799	0.377
CKAP2L	1.478315	0.842	0.422
CDCA8	1.629255	0.961	0.541
TTK	1.656035	0.792	0.374
CKAP2	1.784009	0.898	0.480
PRR11	1.517472	0.892	0.474
GTSE1	1.409526	0.892	0.477
SPTBN1	1.995626	0.659	0.248
KIF18B	1.428923	0.851	0.443
CDCA3	1.507102	0.854	0.448
TUBA1C	1.375312	0.864	0.462
RANGAP1	1.212514	0.824	0.428
CCNB2	2.100307	0.993	0.598
SAPCD2	1.588462	0.666	0.274
UBE2C	1.727597	0.889	0.497
KIF4A	1.333625	0.733	0.341
DEPDC1B	1.338567	0.881	0.492
ATP8B3	2.957713	0.470	0.082
TRIM59	1.529394	0.854	0.472
PRC1	1.452672	0.859	0.478

Cycling GCB : Cycling GCB_3 vs_rest

Top 50 markers: Cycling GCB_3 vs_rest
	avg_log2FC	pct.1	pct.2
PKM	0.7814503	0.787	0.944
HSPA8	0.9266271	0.785	0.939
LDHB	0.9611074	0.808	0.947
ENO1	1.2241599	0.748	0.885
EEF1G	0.7120596	0.844	0.970
TUBB	0.8733832	0.857	0.938
HNRNPA2B1	0.5011495	0.923	0.992
EEF2	0.5693796	0.940	0.996
ACTG1	0.6277006	0.982	0.998
PFN1	0.6347506	0.988	0.999
TMSB4X	0.6755024	0.985	0.996
ACTB	0.6367547	0.999	1.000
MIF	0.9166250	0.733	0.895
YBX1	0.8904666	0.730	0.907
EIF4A1	1.1438221	0.682	0.839
UBA52	0.5382661	0.828	0.976
TMSB10	0.6745963	0.810	0.968
COTL1	0.8724526	0.738	0.916
PSME1	0.9434671	0.698	0.894
HSP90AB1	1.1893243	0.671	0.813
PRDX1	0.9990514	0.669	0.843
CSK	0.5260575	0.831	0.974
HCLS1	0.5800048	0.821	0.977
BECN1	0.5080404	0.219	0.494
VAMP7	0.5210004	0.166	0.397
CALCOCO2	0.5491480	0.251	0.546
C9orf40	0.5338246	0.224	0.495
UNC13D	0.5023503	0.199	0.451
NUP54	0.5223671	0.233	0.513
SNX20	0.5570184	0.212	0.471
ERCC1	0.5364479	0.183	0.420
CHCHD5	0.5752018	0.184	0.424
RIC8A	0.5015294	0.218	0.484
UBAC1	0.5193670	0.179	0.412
FLNA	0.6493532	0.195	0.439
UROD	0.5523865	0.226	0.495
NRBP1	0.5410770	0.268	0.567
PPP1R11	0.5564531	0.204	0.454
BAHD1	0.5728983	0.137	0.338
FOXP4	0.5038824	0.180	0.411
DMAC2	0.5079310	0.156	0.369
SNW1	0.5448309	0.170	0.394
TRIM44	0.5093586	0.159	0.372
NCL	1.0803806	0.652	0.849
PGM2	0.5406481	0.181	0.412
TAOK3	0.5448783	0.230	0.497
COMMD1	0.5723665	0.239	0.514
PPID	0.5401004	0.207	0.455
ATP6V1B2	0.5906437	0.233	0.507
THUMPD3	0.5274570	0.199	0.441

Cycling GCB : Cycling GCB_0 vs Cycling GCB_1

Top 50 markers: Cycling GCB_0 vs Cycling GCB_1
	avg_log2FC	pct.1	pct.2
HIST1H2BB	3.357980	0.938	0.254
HIST1H2AE	3.276382	0.879	0.203
HIST1H2BH	3.248609	0.985	0.375
HJURP	2.020266	0.869	0.343
MXD3	2.596292	0.742	0.227
KIF23	2.029000	0.739	0.234
HIST1H1A	3.415099	0.645	0.147
SPC25	1.909590	0.881	0.391
NDC80	1.874038	0.879	0.389
AURKB	1.880672	0.937	0.456
ESPL1	1.850542	0.799	0.320
HIST1H2BN	2.868240	0.574	0.109
HIST1H1D	2.888959	0.981	0.519
GTSE1	1.757284	0.747	0.286
HIST1H4J	2.671507	0.599	0.139
KIF18B	1.911400	0.720	0.262
ANLN	1.841960	0.796	0.340
CKAP2L	1.911128	0.692	0.243
KIF2C	1.550358	0.823	0.375
HIST1H2AK	2.969546	0.513	0.091
FOXM1	1.497741	0.891	0.474
CDCA8	1.487476	0.770	0.358
HIST1H1B	3.247035	0.998	0.589
INCENP	1.666689	0.900	0.491
ARHGAP33	2.385269	0.559	0.156
CDCA3	1.672708	0.690	0.288
SGO2	1.793371	0.613	0.216
CCNF	1.581733	0.716	0.321
HIST1H4C	3.007177	0.990	0.599
NCAPG	1.538799	0.920	0.533
TOP2A	1.728539	0.909	0.522
E2F8	1.137562	0.849	0.464
CDK1	1.616717	0.951	0.567
HIST1H2BG	2.205421	0.524	0.142
CDKN2D	1.964406	0.592	0.211
BUB1B	1.388937	0.803	0.423
CENPI	1.381815	0.720	0.340
CCNA2	1.239625	0.787	0.408
SPAG5	1.300384	0.828	0.449
DEPDC1B	1.361057	0.724	0.350
CDCA2	1.381183	0.752	0.380
RRM2	1.201910	0.971	0.600
KIFC1	1.723386	0.968	0.601
BUB1	1.318914	0.835	0.470
TPX2	1.144942	0.894	0.529
DEPDC1	1.744092	0.567	0.205
UBE2C	1.487037	0.700	0.339
ASPM	1.361057	0.641	0.284
NUF2	1.223777	0.682	0.327
HIST1H1E	2.368897	0.972	0.619

Cycling GCB : Cycling GCB_0 vs Cycling GCB_2

Top 50 markers: Cycling GCB_0 vs Cycling GCB_2
	avg_log2FC	pct.1	pct.2
SLC20A1	3.063245	0.835	0.258
FAM111B	3.137904	0.760	0.195
RAD54L	2.514570	0.815	0.287
POLA1	2.547037	0.806	0.287
E2F8	2.164102	0.849	0.330
PCNA	2.611932	0.866	0.353
PKMYT1	2.346882	0.791	0.288
MCM4	2.423636	0.912	0.418
CLSPN	2.475743	0.703	0.221
MCM6	2.008260	0.868	0.390
CDC45	2.621027	0.705	0.231
POLD3	2.307010	0.704	0.237
HIST1H2AE	1.449761	0.879	0.418
EXO1	2.828808	0.595	0.141
RFC2	2.034424	0.797	0.345
FEN1	2.111160	0.863	0.413
POLD1	2.007983	0.852	0.403
ATAD2	2.238892	0.805	0.357
CHAF1A	1.985656	0.857	0.409
CCNE2	3.337019	0.556	0.110
DONSON	2.141740	0.775	0.331
RECQL4	2.376626	0.660	0.224
DSCC1	1.997579	0.736	0.310
E2F1	1.739866	0.838	0.413
HIST1H2BB	1.540839	0.938	0.514
MCM3	2.126809	0.921	0.499
CTNNAL1	2.089575	0.657	0.238
SLC43A3	1.967329	0.711	0.297
IMPA2	2.651771	0.558	0.145
RFC4	1.948240	0.692	0.279
TCF19	1.927306	0.881	0.471
CDC6	2.792091	0.552	0.142
ESCO2	1.755738	0.711	0.306
MSH2	1.752043	0.832	0.430
HIST1H1A	3.017087	0.645	0.245
CTPS1	2.235618	0.581	0.181
ADCY3	2.216001	0.602	0.204
POLA2	1.660822	0.773	0.385
DNA2	1.685102	0.772	0.386
GINS2	1.881464	0.698	0.315
SLC7A1	1.749081	0.674	0.293
TMEM106C	1.855773	0.904	0.523
HIST1H4J	1.652433	0.599	0.220
CDK4	1.669856	0.847	0.469
MCM10	2.490202	0.513	0.136
BTG3	2.003865	0.588	0.213
NCAPH2	1.874971	0.935	0.561
HELLS	2.360705	0.969	0.596
RRM2	2.408373	0.971	0.599
MTHFD1	2.033832	0.598	0.226

Cycling GCB : Cycling GCB_0 vs Cycling GCB_3

Top 50 markers: Cycling GCB_0 vs Cycling GCB_3
	avg_log2FC	pct.1	pct.2
PILRB	2.090763	0.889	0.127
LENG8	1.984479	0.925	0.201
ATP8A1	2.631005	0.834	0.111
NEMP1	2.162382	0.835	0.118
ENTPD4	2.563637	0.810	0.095
TCIRG1	2.402226	0.812	0.100
UHRF2	1.891723	0.893	0.182
CD38	2.339777	0.944	0.233
SLC20A1	2.524432	0.835	0.127
NUP210	2.052609	0.970	0.266
ITPR3	1.846421	0.900	0.196
SLC1A4	2.067260	0.838	0.135
ASH1L	2.018615	0.826	0.125
FANCA	1.939364	0.938	0.239
SUGP2	1.781642	0.933	0.239
EML6	2.349717	0.811	0.118
NKTR	1.906729	0.856	0.164
LNPEP	1.753225	0.925	0.234
SYVN1	2.203559	0.963	0.272
PTPRC	1.955141	0.940	0.251
DMTF1	1.988484	0.817	0.129
AC091057.6	3.141179	0.726	0.038
P2RY8	1.703756	0.904	0.220
TGOLN2	1.684115	0.903	0.221
SLC15A4	1.982876	0.814	0.133
MCOLN2	2.007764	0.794	0.115
RBM6	1.626986	0.935	0.258
PLXNB2	1.657542	0.834	0.159
RTN3	1.714541	0.806	0.134
ANKLE2	1.527446	0.880	0.208
RSRP1	1.411759	0.887	0.217
NSD2	1.710354	0.812	0.142
CD47	1.966743	0.842	0.172
SLC15A2	2.977727	0.715	0.046
CREBZF	1.712281	0.827	0.158
DNAJC10	1.758498	0.880	0.213
KNTC1	1.533616	0.839	0.172
MME	1.789050	0.849	0.183
MICAL3	1.522134	0.956	0.292
TACC1	1.427932	0.910	0.249
NEIL1	1.490978	0.869	0.209
CCNL1	1.984900	0.791	0.132
SUSD6	1.763772	0.802	0.143
PHTF2	1.816452	0.789	0.131
VAMP1	2.018137	0.763	0.105
CLK1	1.792580	0.798	0.141
CD40	1.733127	0.903	0.246
FCRLA	1.505787	0.895	0.239
CD19	1.723585	0.908	0.252
MYBL1	1.797552	0.957	0.302

Cycling GCB : Cycling GCB_1 vs Cycling GCB_2

Top 50 markers: Cycling GCB_1 vs Cycling GCB_2
	avg_log2FC	pct.1	pct.2
UNG	4.824678	0.676	0.069
MCM6	2.541050	0.933	0.390
GINS2	2.808605	0.856	0.315
MCM4	2.953909	0.948	0.418
DTL	2.869194	0.707	0.222
FAM111B	3.088398	0.676	0.195
E2F1	2.346611	0.894	0.413
CDC6	3.339606	0.611	0.142
RFC2	2.177072	0.807	0.345
POLA1	2.455394	0.743	0.287
PCNA	2.436234	0.808	0.353
CHAF1A	2.304880	0.842	0.409
CTPS1	2.628213	0.608	0.181
MCM3	2.225233	0.925	0.499
CDC45	2.818727	0.657	0.231
MTHFD1	2.505553	0.647	0.226
MCM10	3.066447	0.548	0.136
SLC20A1	2.312239	0.668	0.258
POLD3	2.319412	0.632	0.237
SRM	2.547398	0.599	0.206
ADCY3	2.432842	0.595	0.204
CCNE2	3.418124	0.501	0.110
MCM5	2.624535	0.985	0.599
RFC4	2.072365	0.660	0.279
MCM2	2.174478	0.960	0.580
MSH2	1.858804	0.807	0.430
POLD1	1.947576	0.780	0.403
HELLS	2.592410	0.970	0.596
CHAF1B	3.357059	0.451	0.078
USP37	2.019494	0.626	0.254
CLSPN	2.334393	0.589	0.221
CDK4	1.908533	0.836	0.469
DCTPP1	1.904859	0.696	0.331
SLC1A5	2.202443	0.581	0.219
VARS	2.265766	0.550	0.194
POLE2	2.430782	0.514	0.162
ATIC	1.734816	0.709	0.359
C19orf48	1.884926	0.843	0.495
WDHD1	1.880835	0.644	0.297
SLBP	2.144403	0.957	0.610
XRCC3	2.123507	0.579	0.232
CDC25A	2.265895	0.557	0.211
ZMYND19	2.171003	0.544	0.198
POLA2	1.773815	0.729	0.385
FCRL3	2.367478	0.549	0.207
MMS22L	1.927008	0.636	0.295
ORC1	1.756834	0.663	0.323
CTNNAL1	2.028965	0.573	0.238
MYBBP1A	2.290387	0.512	0.178
GGCT	1.919760	0.558	0.228

Cycling GCB : Cycling GCB_1 vs Cycling GCB_3

Top 50 markers: Cycling GCB_1 vs Cycling GCB_3
	avg_log2FC	pct.1	pct.2
ASH1L	2.094661	0.805	0.125
LENG8	1.790036	0.873	0.201
NUP210	1.720252	0.935	0.266
RSRP1	1.589884	0.882	0.217
PTPRC	1.788521	0.915	0.251
ATP8A1	2.572331	0.773	0.111
SYVN1	1.861086	0.929	0.272
SLC15A4	2.148837	0.790	0.133
FANCA	1.911629	0.891	0.239
ADAM19	1.862379	0.833	0.186
CD40	2.073826	0.893	0.246
SUSD6	1.933148	0.787	0.143
OS9	1.623584	0.817	0.175
MTDH	1.680115	0.875	0.234
TGOLN2	1.565364	0.861	0.221
ITPR3	1.680105	0.836	0.196
ST6GAL1	1.980540	0.940	0.301
LNPEP	1.520153	0.872	0.234
CYBB	1.870505	0.843	0.206
ENTPD4	2.385035	0.730	0.095
CD19	1.776904	0.887	0.252
CXCR5	1.859096	0.816	0.182
PLXNB2	1.791560	0.791	0.159
P2RY8	1.590051	0.851	0.220
ZNF141	2.132479	0.752	0.122
KMT2A	2.053063	0.782	0.153
OGT	1.591831	0.849	0.221
MCOLN2	2.114446	0.742	0.115
FCRL2	3.318192	0.709	0.082
RBM6	1.416201	0.885	0.258
MICAL3	1.419752	0.919	0.292
CREBZF	1.684635	0.783	0.158
CD38	2.071677	0.856	0.233
SREBF2	1.476028	0.918	0.295
SLC44A2	1.663596	0.800	0.180
SEC62	1.235551	0.887	0.268
DMTF1	1.819878	0.748	0.129
UHRF2	1.515031	0.801	0.182
PILRB	1.467858	0.745	0.127
ANKLE2	1.395092	0.823	0.208
PHTF2	1.836042	0.745	0.131
TACC1	1.302337	0.863	0.249
ATP2B1	2.340039	0.709	0.095
LPCAT1	1.542318	0.839	0.226
FAM214A	1.675048	0.823	0.210
SLAMF6	1.764682	0.755	0.144
UGGT1	1.685770	0.795	0.184
CLEC2D	1.668252	0.923	0.315
NKTR	1.638985	0.772	0.164
TAPBP	1.509821	0.839	0.231

Cycling GCB : Cycling GCB_2 vs Cycling GCB_3

Top 50 markers: Cycling GCB_2 vs Cycling GCB_3
	avg_log2FC	pct.1	pct.2
PILRB	3.053851	0.926	0.127
ARL6IP1	2.677375	0.959	0.185
SUN2	2.475297	0.934	0.167
NEIL1	2.497082	0.959	0.209
ASPM	2.589396	0.919	0.170
LNPEP	2.628017	0.972	0.234
TROAP	2.193698	0.893	0.164
PIF1	2.767447	0.818	0.089
AC091057.6	3.908226	0.763	0.038
MYEF2	2.106015	0.876	0.152
ENTPD4	2.707570	0.810	0.095
CD38	2.427239	0.947	0.233
CR1	3.167923	0.817	0.104
DEPDC1	1.911453	0.895	0.183
PTPRC	2.265394	0.960	0.251
P2RY8	1.892274	0.918	0.220
SLAMF6	2.112805	0.842	0.144
OS9	1.890944	0.873	0.175
FCRLA	1.819004	0.933	0.239
CENPE	1.697066	0.938	0.247
SYVN1	2.193325	0.963	0.272
SUGP2	2.015286	0.928	0.239
CLK1	2.114745	0.829	0.141
FAM214A	1.990165	0.896	0.210
SGPP1	2.576504	0.992	0.308
ASH1L	2.106523	0.808	0.125
CENPF	1.731353	0.975	0.296
ATP8A1	2.609523	0.790	0.111
HMMR	1.659124	0.971	0.293
CR2	2.347380	0.803	0.126
ST8SIA4	2.201086	0.833	0.156
KIF14	1.588894	0.864	0.188
MME	1.993886	0.858	0.183
EVI2B	1.985249	0.914	0.239
ANLN	1.922638	0.824	0.151
CLEC2D	2.525333	0.986	0.315
CCDC18	1.935233	0.810	0.140
UHRF2	1.756385	0.849	0.182
NDC1	1.693154	0.940	0.274
DMTF1	2.006607	0.794	0.129
TMEM131	1.800055	0.835	0.171
MYBL1	1.918868	0.965	0.302
SERINC3	1.816407	0.794	0.133
GEN1	1.560910	0.876	0.217
KMT2A	1.917804	0.810	0.153
MCOLN2	2.058881	0.771	0.115
SUSD6	1.909183	0.799	0.143
PIKFYVE	1.444140	0.856	0.202
STIM2	2.165119	0.756	0.104
PAN3	1.570905	0.890	0.238

Plasma B cells

plasma_b <-  subset(paed_sub, idents = "Plasma B")
keep_cells <- Idents(paed_sub) %in% "Plasma B"
plasma_b@assays$RNA@cells@.Data <- paed_sub@assays$RNA@cells@.Data[keep_cells, ]
plasma_b

An object of class Seurat 
18076 features across 14390 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.bcells

out3 <- here("output/RDS/Lineages_RDS_combined/SEU_PlasmaB_Cells.rds")

if (!file.exists(out3)) {
  plasma_b <- plasma_b %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() 
  gc() 
  plasma_b <- RunUMAP(plasma_b, dims = 1:30, reduction = "pca", reduction.name = "umap.plasmaB")

  meta_data_columns <- colnames(plasma_b@meta.data)
  columns_to_remove <- grep("^RNA_snn_res", meta_data_columns, value = TRUE)
  plasma_b@meta.data <- plasma_b@meta.data[, !(colnames(plasma_b@meta.data) %in% columns_to_remove)]
  resolutions <- seq(0.1, 0.5, by = 0.1)
  plasma_b <- FindNeighbors(plasma_b, dims = 1:30, reduction = "pca")
  plasma_b <- FindClusters(plasma_b, resolution = resolutions )

  saveRDS(plasma_b, out3)

} else {
  plasma_b <- readRDS(out3)
}

clustree(plasma_b, prefix = "RNA_snn_res.")

DimPlot(plasma_b, group.by = "RNA_snn_res.0.1", reduction = "umap.plasmaB", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

opt_res <- "RNA_snn_res.0.1"  
n <- nlevels(plasma_b$RNA_snn_res.0.1)
plasma_b$RNA_snn_res.0.1 <- factor(plasma_b$RNA_snn_res.0.1, levels = seq(0,n-1))
plasma_b$seurat_clusters <- NULL
plasma_b$cluster <- plasma_b$RNA_snn_res.0.1
Idents(plasma_b) <- plasma_b$cluster

plasma_b$cell_labels <- NULL

Warning: Cannot find cell-level meta data named cell_labels

plasma_b$cell_labels_v2 <- NULL

Idents(plasma_b) <- "RNA_snn_res.0.1"
new.cluster.ids <- c("Plasma B cells_1", 
                     "Plasma B cells_2", 
                     "Plasma B cells_3",
                     "Plasma B cells_4")
names(new.cluster.ids) <- levels(plasma_b)
plasma_b <- RenameIdents(plasma_b, new.cluster.ids)
Idents(plasma_b) <- factor(Idents(plasma_b), levels = c("Plasma B cells_1", "Plasma B cells_2",  "Plasma B cells_3",  "Plasma B cells_4"))
plasma_b$cell_labels_l2 <- Idents(plasma_b)
plasma_b$cell_labels_l3 <- Idents(plasma_b)
table(plasma_b$cluster, plasma_b$tissue)

   
    Adenoids  BAL Bronchial_brushings Nasal_brushings Tonsils
  0     2152   10                   3              40    6715
  1     2127   35                  11              41    2261
  2      658   29                  83              77       1
  3        0    0                   0               3     144

DE features of Plasma B cells

The marker genes for this Plasma B cell subclustering can be found here-

PlasmaB_population_subclusters

gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4783018   255.5   10139294   541.5         NA   10139294   541.5
Vcells 4504150502 34364.0 8757678310 66815.8     163840 8757032360 66810.9

paed_sub.markers <- FindAllMarkers(
  plasma_b,
  only.pos = TRUE,
  min.pct = 0.25,
  logfc.threshold = 0.25, features = features.no.mt
)

Calculating cluster Plasma B cells_1

Calculating cluster Plasma B cells_2

Calculating cluster Plasma B cells_3

Calculating cluster Plasma B cells_4

gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4780408   255.4   10139294   541.5         NA   10139294   541.5
Vcells 4504104962 34363.6 8757678310 66815.8     163840 8757032360 66810.9

top10 <- 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: 4 × 2
# Groups:   cluster [4]
  cluster          gene    
  <fct>            <chr>   
1 Plasma B cells_1 AEBP1   
2 Plasma B cells_2 TBC1D9  
3 Plasma B cells_3 HIST1H1B
4 Plasma B cells_4 MAF

FeaturePlot(
  plasma_b,
  features = best.wilcox.gene.per.cluster$gene,
  reduction = "umap.plasmaB",
  raster = FALSE,
  ncol = 1,
  label = TRUE,
  split.by = "tissue"
)

Warning: All cells have the same value (0) of "MAF"

plasma_b <- ScaleData(
  plasma_b,
  features = unique(top10$gene),
  assay = "RNA",
  verbose = FALSE
)

Warning: Different features in new layer data than already exists for
scale.data

DoHeatmap(plasma_b, features = unique(top10$gene), angle = 90) + NoLegend()

## Seurat top markers
cluster_colors <- paletteer::paletteer_d("pals::glasbey")[factor(top10$cluster)]

DotPlot(plasma_b,    
        features = unique(top10$gene),
        group.by = "cell_labels_l2",
        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)")

Warning: Scaling data with a low number of groups may produce misleading
results

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

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 Plasma B cells_3      2935
2 Plasma B cells_4       231
3 Plasma B cells_1       118
4 Plasma B cells_2        30

ggplot(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","B_cell_lineage",
            paste("Marker_genes_Reclustered_PlasmaB.",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)
  }
}

DE markers/gene between Plasma sub-clusters

All Plasma Bcell subsets

pl_ids <- c("Plasma B cells_1", "Plasma B cells_2", "Plasma B cells_3",  "Plasma B cells_4")

res_all <- run_de_and_vln(
  plasma_b,
  pl_ids,
  title_prefix = "Plasma B",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

Plasma B : Plasma B cells_1 vs_rest

Top 50 markers: Plasma B cells_1 vs_rest
	avg_log2FC	pct.1	pct.2
RGS13	1.0173265	0.889	0.241
EML6	1.3656062	0.748	0.234
VPREB3	1.2883790	0.604	0.160
DENND6B	2.2148724	0.670	0.234
LHFPL2	1.3712868	0.527	0.095
VDR	2.2068619	0.693	0.277
PRDM15	1.4456983	0.575	0.172
PLXNB2	1.0773755	0.674	0.273
MME	0.6768171	0.453	0.064
SYNE2	1.1030199	0.903	0.518
EHD1	0.6005142	0.618	0.237
PRKD3	0.6949531	0.756	0.376
MCTP2	0.7241993	0.547	0.168
NLK	0.7708096	0.713	0.337
PIK3CG	1.3952259	0.716	0.343
ALDH5A1	1.4495957	0.585	0.218
PTPRS	1.0652221	0.564	0.199
QPCT	1.2623177	0.529	0.170
RASSF6	1.3403298	0.700	0.342
RNF103	1.2310956	0.738	0.405
AKIRIN2	0.9277216	0.754	0.431
CD9	1.8766360	0.565	0.244
PITRM1	1.2679336	0.618	0.299
LRIG1	1.6176299	0.434	0.117
STK17B	1.1530481	0.888	0.573
PIM1	1.0653966	0.720	0.405
AEBP1	2.6140100	0.428	0.120
OGT	1.4100100	0.905	0.598
REXO2	1.4985958	0.729	0.426
SLCO3A1	1.3166198	0.359	0.064
IQSEC1	1.0376881	0.706	0.418
CD38	1.1270988	0.909	0.622
SLC17A9	1.4278455	0.685	0.398
FAM214A	0.9644738	0.889	0.605
ACSS1	1.3808703	0.655	0.380
GLCCI1	1.3506863	0.873	0.615
ACADVL	1.1716371	0.745	0.491
CYTIP	0.8097559	0.931	0.702
PRDM1	1.4217201	0.890	0.663
ISG20	0.8000618	0.912	0.691
ZBP1	1.0677250	0.810	0.594
CDKN1B	0.8244761	0.895	0.681
TRAM1	0.7986740	0.915	0.758
ST6GAL1	1.0168404	0.956	0.804
CD74	0.5832892	0.994	0.911
MZB1	1.0801846	0.984	0.928
CD79A	1.0127889	0.992	0.944
ADA	0.8370963	0.564	0.222
ITPR2	1.0605146	0.722	0.412
SND1	0.9001496	0.822	0.558

Plasma B : Plasma B cells_2 vs_rest

Top 50 markers: Plasma B cells_2 vs_rest
	p_val	avg_log2FC	pct.1	pct.2	p_val_adj
TBC1D9	0e+00	3.1145894	0.415	0.117	0.0000000
PECAM1	0e+00	2.4904840	0.594	0.320	0.0000000
VIM	0e+00	1.1553429	0.769	0.621	0.0000000
CD44	0e+00	1.7982803	0.594	0.424	0.0000000
CCND2	0e+00	2.0570319	0.309	0.105	0.0000000
SDC1	0e+00	1.6763157	0.563	0.361	0.0000000
ITGA6	0e+00	2.4967445	0.324	0.129	0.0000000
CAPN2	0e+00	2.1224780	0.326	0.134	0.0000000
TXNIP	0e+00	1.5278748	0.676	0.609	0.0000000
EMP3	0e+00	1.7193510	0.504	0.326	0.0000000
PLAC8	0e+00	2.3013425	0.268	0.098	0.0000000
CADM1	0e+00	1.6374711	0.326	0.151	0.0000000
IGHD	0e+00	1.1069648	0.159	0.349	0.0000000
IGLC1	0e+00	0.9139529	0.429	0.646	0.0000000
GAS6	0e+00	1.5716056	0.368	0.213	0.0000000
FCGR2B	0e+00	1.6321345	0.331	0.187	0.0000000
TIMP1	0e+00	2.1947997	0.256	0.125	0.0000000
IGKC	0e+00	0.6527606	0.788	0.758	0.0000000
TSPAN3	0e+00	1.4628911	0.500	0.404	0.0000000
IGHA1	0e+00	1.7855112	0.432	0.282	0.0000000
ITGB7	0e+00	1.1860857	0.397	0.264	0.0000000
MARCKS	0e+00	1.3524682	0.285	0.164	0.0000000
LGALS1	0e+00	0.8214518	0.412	0.274	0.0000000
S100A6	0e+00	1.8912533	0.290	0.180	0.0000000
CELF2	0e+00	1.5206431	0.295	0.198	0.0000000
CASP10	0e+00	1.0624230	0.415	0.316	0.0000000
LY6E	0e+00	1.2134580	0.392	0.320	0.0000000
PDK1	0e+00	0.7893941	0.662	0.694	0.0000000
HIPK2	0e+00	0.7959031	0.481	0.420	0.0000000
NPC2	0e+00	0.8030023	0.657	0.679	0.0000000
PAIP2B	0e+00	1.1888534	0.335	0.261	0.0000000
SLC38A5	0e+00	0.8051600	0.298	0.216	0.0000000
DUSP5	0e+00	0.9254730	0.267	0.191	0.0000000
CD63	0e+00	1.1342348	0.425	0.377	0.0000000
PRDX4	0e+00	0.5915506	0.776	0.794	0.0000000
P2RY10	0e+00	1.1289086	0.267	0.215	0.0000000
MGST3	0e+00	1.0252454	0.269	0.212	0.0000000
ITGA4	0e+00	1.1945061	0.267	0.223	0.0000000
ATXN1	0e+00	0.9101840	0.419	0.377	0.0000000
POLR3GL	0e+00	0.5091750	0.190	0.272	0.0000000
IFI6	0e+00	0.6278550	0.457	0.430	0.0000000
LMNA	0e+00	0.9275475	0.267	0.220	0.0000000
LEPROTL1	0e+00	0.5885393	0.216	0.300	0.0000000
FUCA2	0e+00	0.9138012	0.368	0.332	0.0000002
CXCR4	0e+00	0.6556090	0.529	0.556	0.0000035
RNASE6	0e+00	0.9712013	0.286	0.256	0.0000040
HEXB	0e+00	0.8945233	0.253	0.219	0.0003155
CTSZ	1e-07	0.7849486	0.511	0.559	0.0010188
KIF13B	1e-07	0.6779496	0.269	0.242	0.0018208
SELENOW	4e-07	0.6321063	0.231	0.294	0.0071318

Plasma B : Plasma B cells_3 vs_rest

Top 50 markers: Plasma B cells_3 vs_rest
	avg_log2FC	pct.1	pct.2
CFL1	4.351686	0.866	0.621
PFN1	4.322730	0.940	0.748
ACTB	5.748388	0.940	0.789
TMSB4X	2.828437	0.959	0.860
OAZ1	1.281173	0.991	0.944
ARHGDIB	3.355145	0.861	0.707
ATP2A3	2.049959	0.935	0.741
EZR	3.328728	0.847	0.667
RAC2	2.168081	0.912	0.728
CCNI	1.945135	0.906	0.729
H3F3A	2.111595	0.914	0.779
HNRNPA2B1	2.648325	0.829	0.673
FAM107B	2.977415	0.835	0.635
RHOA	2.193012	0.847	0.630
CORO1A	7.228499	0.744	0.507
UBA52	1.706676	0.914	0.742
KLHL6	2.580071	0.857	0.727
YWHAZ	2.246968	0.827	0.603
WIPF1	2.394146	0.829	0.665
CD79B	2.756524	0.849	0.746
HCLS1	2.050737	0.848	0.684
TMSB10	1.945000	0.959	0.749
ARHGEF1	2.391903	0.821	0.568
LRMP	4.544261	0.779	0.646
CD53	2.595863	0.825	0.663
GGA2	3.397341	0.767	0.601
PTPN6	3.134756	0.737	0.472
SYVN1	1.527419	0.945	0.773
ACTG1	5.116043	0.761	0.631
AKNA	2.550374	0.792	0.547
HNRNPC	2.406266	0.782	0.560
UBE2J1	1.640776	0.986	0.903
SASH3	2.123651	0.828	0.628
TUFM	1.762881	0.844	0.623
MIF	1.547801	0.897	0.724
EIF4A1	2.534726	0.752	0.511
SERF2	1.919864	0.820	0.593
LAPTM5	3.385312	0.834	0.774
DBNL	2.540285	0.759	0.554
CTSH	2.215644	0.862	0.687
UCP2	3.519863	0.736	0.567
ATP5MC3	2.499598	0.723	0.456
RBM3	1.939793	0.805	0.548
RACK1	1.515714	0.899	0.742
ARPC2	2.737394	0.719	0.500
PLCG2	1.600726	0.890	0.768
FERMT3	3.195985	0.703	0.477
HSP90AB1	2.711614	0.737	0.510
HNRNPD	2.054283	0.789	0.648
EIF3B	2.040137	0.769	0.534

Plasma B : Plasma B cells_4 vs_rest

Top 50 markers: Plasma B cells_4 vs_rest
	avg_log2FC	pct.1	pct.2	p_val_adj
LTB	6.3059703	0.687	0.174	0.0e+00
TMSB4X	2.6274294	0.993	0.864	0.0e+00
IGHG3	2.8690325	0.993	0.556	0.0e+00
ACTB	1.1708876	0.952	0.796	0.0e+00
MAF	14.1642312	0.293	0.043	0.0e+00
MS4A1	4.1171858	0.660	0.455	0.0e+00
ACTG1	0.9756393	0.707	0.638	0.0e+00
LAPTM5	1.0187708	0.741	0.778	0.0e+00
PFN1	1.0490799	0.728	0.759	0.0e+00
CD22	2.9544901	0.605	0.526	0.0e+00
ARHGDIB	1.4127676	0.687	0.716	0.0e+00
CD37	3.7182421	0.571	0.495	0.0e+00
CORO1A	2.0523409	0.599	0.520	0.0e+00
RASSF5	0.5803877	0.109	0.488	0.0e+00
FGFR1OP2	0.5035503	0.095	0.457	0.0e+00
ZFP36L1	6.6328184	0.415	0.250	0.0e+00
NFX1	0.7231503	0.129	0.526	0.0e+00
RAB8A	0.6047045	0.156	0.570	0.0e+00
EIF4A2	0.8147710	0.129	0.512	0.0e+00
SAFB2	0.7588652	0.109	0.465	0.0e+00
FAM120A	0.9992131	0.143	0.532	0.0e+00
SUMF2	0.5640224	0.116	0.470	1.0e-07
IRF2	0.5365269	0.150	0.535	1.0e-07
PHF1	0.5904803	0.116	0.477	1.0e-07
OGFR	0.5512888	0.068	0.380	1.0e-07
APOL6	0.6482608	0.170	0.577	1.0e-07
AKAP8	0.6109315	0.048	0.340	1.0e-07
KDM5C	1.0122799	0.136	0.515	1.0e-07
NDUFA3	0.6114451	0.184	0.601	1.0e-07
ORAI2	0.6627113	0.170	0.576	1.0e-07
TLK1	0.5467187	0.150	0.529	1.0e-07
IRAK1	0.6397909	0.156	0.544	2.0e-07
CDR2	0.5195778	0.075	0.390	2.0e-07
COX16	0.7477049	0.129	0.492	2.0e-07
HMGN4	0.5396207	0.136	0.498	2.0e-07
IFNAR1	0.9332033	0.163	0.550	3.0e-07
ALG13	1.2081479	0.122	0.474	5.0e-07
TCL1A	4.2382610	0.361	0.205	6.0e-07
EID1	0.5444610	0.163	0.550	7.0e-07
ERCC5	0.5340006	0.054	0.338	7.0e-07
HMG20B	0.5067281	0.075	0.374	9.0e-07
TGOLN2	0.5912260	0.197	0.615	9.0e-07
CCDC88B	0.5886380	0.150	0.521	9.0e-07
UXT	0.6646303	0.184	0.586	1.0e-06
STK40	0.8132764	0.122	0.465	1.1e-06
TMEM109	0.6370203	0.122	0.464	1.1e-06
PHF20L1	0.6453345	0.075	0.368	1.1e-06
ATP6V0D1	0.6244771	0.088	0.396	1.2e-06
CUL5	1.0299525	0.054	0.334	1.3e-06
NCKAP1L	0.7032760	0.136	0.489	1.3e-06

Plasma B : Plasma B cells_1 vs Plasma B cells_2

Top 50 markers: Plasma B cells_1 vs Plasma B cells_2
	avg_log2FC	pct.1	pct.2
RGS13	3.7865356	0.889	0.161
EML6	4.0393661	0.748	0.164
CD22	2.3097724	0.713	0.146
MARCKSL1	1.5470821	0.701	0.176
LAPTM5	1.7723599	0.948	0.427
BCL7A	3.4157588	0.582	0.061
RFTN1	2.0287321	0.687	0.170
DTX1	3.0154066	0.590	0.078
VPREB3	2.9769072	0.604	0.094
CLEC2D	2.1953609	0.727	0.223
DENND6B	2.8260213	0.670	0.177
AFF2	1.6400390	0.653	0.161
VDR	3.0785574	0.693	0.209
LHFPL2	3.6537577	0.527	0.045
ETS1	1.5962406	0.705	0.224
MEF2B	1.6714727	0.850	0.379
CCDC88A	2.3955150	0.809	0.339
PRDM15	2.9149842	0.575	0.115
PLXNB2	1.7662419	0.674	0.217
EHD1	2.1595598	0.618	0.163
SYNE2	2.0724160	0.903	0.459
MCTP2	2.2011597	0.547	0.109
LRMP	1.0012055	0.794	0.357
SWAP70	0.8372902	0.613	0.179
PRKD3	1.5051172	0.756	0.324
NLK	1.7455077	0.713	0.281
TNFRSF13C	1.1296370	0.768	0.341
ALDH5A1	2.2996444	0.585	0.158
PIK3CG	1.9454707	0.716	0.291
MME	3.4720978	0.453	0.033
SEPTIN1	1.3501786	0.740	0.321
SEMA4A	1.7007275	0.694	0.275
SUSD3	1.6523345	0.611	0.199
PTPRS	2.2120721	0.564	0.155
GCSAM	2.1855236	0.492	0.084
SLC44A2	1.3244896	0.653	0.246
ADA	2.2002732	0.564	0.161
QPCT	2.0050424	0.529	0.128
PXK	1.0969060	0.612	0.215
IL21R	1.3076292	0.643	0.247
RASSF6	1.9136126	0.700	0.305
HS2ST1	1.7237403	0.649	0.255
LBR	0.7091169	0.627	0.235
EHMT1	1.1839194	0.765	0.375
RNF103	1.8074481	0.738	0.351
LRRK1	1.5611941	0.708	0.322
JCHAIN	0.5536542	0.976	0.592
ARGLU1	1.0651542	0.787	0.405
PITRM1	1.8662956	0.618	0.238
P2RY8	1.1150918	0.702	0.322

Plasma B : Plasma B cells_1 vs Plasma B cells_3

Top 50 markers: Plasma B cells_1 vs Plasma B cells_3
	avg_log2FC	pct.1	pct.2
IGHG1	4.5891876	0.877	0.494
TENT5C	1.5940116	0.915	0.961
ADA2	2.1054920	0.854	0.883
ZBP1	2.5997169	0.810	0.856
SSR4	0.9338155	0.954	0.978
ITM2C	1.4752159	0.838	0.848
PLPP5	2.0103899	0.795	0.810
XBP1	1.3639450	0.967	0.989
CDKN1B	1.0440381	0.895	0.947
DNAJB9	1.5400863	0.840	0.850
IGHG3	5.2793306	0.580	0.268
FNDC3B	1.5069712	0.798	0.777
AFF1	2.5665303	0.681	0.715
IRF4	1.8802685	0.833	0.812
TP53INP1	1.1582316	0.823	0.816
BHLHE41	2.2282800	0.669	0.748
THEMIS2	0.9941127	0.782	0.829
LIMD1	1.8262461	0.665	0.702
PNRC1	0.7530995	0.865	0.876
SLAMF7	1.3846393	0.750	0.779
CCPG1	1.6538777	0.673	0.698
ACSS1	1.5474652	0.655	0.626
IGKC	1.8421626	0.755	0.741
GLCCI1	1.0463069	0.873	0.869
TRAM1	0.7255520	0.915	0.940
SEL1L	0.9176210	0.838	0.817
MANEA	1.0506928	0.742	0.671
CHD2	0.6478989	0.744	0.815
PSAP	0.8377947	0.770	0.874
PLD3	1.0657929	0.746	0.796
CPEB4	1.1849414	0.732	0.788
KLF2	1.6366646	0.528	0.475
REXO2	1.1558007	0.729	0.658
GPR183	2.7816885	0.524	0.517
IQSEC1	0.7628115	0.706	0.696
APOL6	1.3547080	0.618	0.679
OGT	0.5937699	0.905	0.945
TXNIP	1.3778925	0.614	0.623
FNDC3A	0.9482423	0.780	0.864
DUSP1	2.0669786	0.595	0.591
ERN1	1.4894039	0.579	0.577
PRDX4	0.8171075	0.800	0.820
PDK1	0.9387294	0.705	0.676
IFNAR2	0.9329705	0.710	0.791
PIM2	0.7819588	0.913	0.960
METTL7A	0.6789214	0.813	0.757
MAN1A1	0.8853225	0.836	0.858
CALCOCO1	1.3256360	0.557	0.522
NLRP1	0.9709577	0.585	0.613
P2RX1	1.8494494	0.524	0.529

Plasma B : Plasma B cells_1 vs Plasma B cells_4

Top 50 markers: Plasma B cells_1 vs Plasma B cells_4
	avg_log2FC	pct.1	pct.2
CD79A	2.546782	0.992	0.741
RGS13	4.943948	0.889	0.299
JCHAIN	3.236284	0.976	0.429
POU2AF1	2.703790	0.947	0.449
MEF2B	6.200937	0.850	0.354
CCDC88A	5.484404	0.809	0.136
TENT5C	2.875848	0.915	0.143
MZB1	2.487068	0.984	0.537
UBE2J1	2.142972	0.958	0.558
ST6GAL1	2.216638	0.956	0.388
OGT	2.562777	0.905	0.272
PRDM1	3.237326	0.890	0.129
RBM6	4.186298	0.773	0.122
KLHL6	2.316421	0.826	0.259
AKIRIN2	4.460355	0.754	0.088
TXNDC11	2.386468	0.925	0.197
CCDC69	2.772370	0.837	0.245
FBXW7	2.460957	0.884	0.156
ZBP1	2.892775	0.810	0.082
BTG2	2.512329	0.935	0.401
MAPKAPK2	3.038940	0.821	0.150
EML6	5.543655	0.748	0.156
EDEM1	2.530871	0.906	0.422
EIF2AK3	5.549734	0.716	0.075
LRRK1	5.511936	0.708	0.054
CCM2	2.683243	0.784	0.095
LRMP	3.184814	0.794	0.442
CD38	2.242477	0.909	0.299
HYOU1	2.817348	0.817	0.116
TP53INP1	2.524600	0.823	0.116
REEP5	2.151555	0.841	0.116
SEMA4A	6.525759	0.694	0.088
SF3B5	3.987870	0.702	0.088
TUT7	2.435941	0.847	0.224
TXNDC5	2.353114	0.972	0.456
SEL1L3	1.751997	0.953	0.415
GSTP1	3.016667	0.810	0.231
TNFRSF17	2.649153	0.815	0.129
VDR	4.919369	0.693	0.048
UBA5	6.689569	0.667	0.048
SEC14L1	2.132668	0.883	0.279
FAM214A	2.059008	0.889	0.231
DERL3	2.225560	0.880	0.177
NLK	3.002123	0.713	0.082
MGAT2	2.219769	0.814	0.116
ISG20	1.941068	0.912	0.265
DYNLRB1	5.040090	0.677	0.088
LITAF	3.892324	0.705	0.156
SPATS2	2.038529	0.831	0.136
METTL7A	2.146070	0.813	0.122

Plasma B : Plasma B cells_2 vs Plasma B cells_3

Top 50 markers: Plasma B cells_2 vs Plasma B cells_3
	avg_log2FC	pct.1	pct.2	p_val_adj
IGHG1	4.6088653	0.840	0.494	0e+00
ITM2C	1.8708475	0.857	0.848	0e+00
PECAM1	3.9957929	0.594	0.330	0e+00
ADA2	1.9121072	0.781	0.883	0e+00
IGHG3	5.4863256	0.561	0.268	0e+00
TXNIP	2.8123406	0.676	0.623	0e+00
IGKC	2.4049321	0.788	0.741	0e+00
SDC1	3.6097670	0.563	0.368	0e+00
TENT5C	1.2360914	0.792	0.961	0e+00
XBP1	1.0184824	0.926	0.989	0e+00
PLPP5	2.1167920	0.684	0.810	0e+00
KLK1	0.5312858	0.101	0.312	0e+00
TBC1D9	3.6995162	0.415	0.233	0e+00
PRDX4	1.3508995	0.776	0.820	0e+00
SSR4	0.8785987	0.913	0.978	0e+00
ITGA6	4.8713741	0.324	0.139	0e+00
PDK1	1.6566101	0.662	0.676	0e+00
FOS	0.7163121	0.337	0.702	0e+00
CD44	1.4603506	0.594	0.583	0e+00
TSPAN3	1.9064752	0.500	0.388	0e+00
GAS6	3.1686924	0.368	0.244	0e+00
CTSD	0.8114894	0.270	0.564	0e+00
SEL1L	1.1250956	0.751	0.817	0e+00
NPC2	1.5641469	0.657	0.791	0e+00
CFAP54	2.2754972	0.198	0.433	0e+00
SLAMF7	1.4829819	0.667	0.779	0e+00
P2RX1	0.6957675	0.262	0.529	0e+00
B9D1	1.0329859	0.226	0.459	0e+00
S100A11	0.7233804	0.251	0.499	0e+00
PSAP	1.1897219	0.686	0.874	0e+00
SAMD9L	0.6599331	0.324	0.652	0e+00
DNAJB9	1.2473348	0.702	0.850	0e+00
CREB3L2	0.6919015	0.414	0.791	0e+00
RBM47	1.5993293	0.232	0.467	0e+00
CXCR4	0.9746345	0.529	0.441	0e+00
ULK1	0.6623173	0.192	0.370	0e+00
CCPG1	1.7398020	0.572	0.698	0e+00
WFS1	0.8608077	0.256	0.465	0e+00
FCGR2B	2.2418062	0.331	0.256	0e+00
FOSB	1.0468143	0.301	0.540	0e+00
ODF3B	0.7164995	0.249	0.448	0e+00
VPS37B	0.5731839	0.206	0.373	0e+00
RAB3D	0.6705100	0.186	0.335	0e+00
APOL1	0.7291712	0.206	0.373	0e+00
FNDC3B	1.2906977	0.623	0.777	1e-07
LIMD1	0.7047063	0.377	0.702	1e-07
ATP2B4	0.9335047	0.190	0.346	2e-07
EMP3	0.9697197	0.504	0.513	2e-07
FAM174A	0.7739225	0.159	0.288	2e-07
IL10RB	1.1312105	0.243	0.445	2e-07

Plasma B : Plasma B cells_2 vs Plasma B cells_4

Top 50 markers: Plasma B cells_2 vs Plasma B cells_4
	avg_log2FC	pct.1	pct.2
TENT5C	2.5179273	0.792	0.143
FBXW7	2.5859633	0.761	0.156
DERL3	2.3431184	0.799	0.177
TXNDC5	2.2311104	0.914	0.456
TXNIP	3.0571412	0.676	0.245
ARSA	6.3324878	0.502	0.041
SLAMF7	1.6369323	0.667	0.122
PDK1	1.7124770	0.662	0.129
TXNDC11	1.6457073	0.741	0.197
NPC2	1.5619893	0.657	0.122
POU2AF1	1.9646899	0.757	0.449
HYOU1	1.8870557	0.631	0.116
CD79A	1.2749216	0.942	0.741
PLD3	1.5489503	0.556	0.075
ZBP1	2.0238003	0.561	0.082
FNDC3B	1.2034469	0.623	0.109
ADA2	1.0914005	0.781	0.218
PSAP	1.4486276	0.686	0.163
PCMTD1	1.6785533	0.617	0.129
CCPG1	0.7046137	0.572	0.082
TSPAN3	2.3578972	0.500	0.048
SLC35B1	4.0244595	0.455	0.027
SELENOK	1.1746082	0.624	0.122
PRDM1	1.7527325	0.627	0.129
HIPK2	2.0165316	0.481	0.048
ERLEC1	1.1635314	0.687	0.170
PLPP5	1.0526405	0.684	0.163
TBC1D9	4.5805435	0.415	0.014
IGHA1	16.6962836	0.432	0.048
DYNLRB1	4.8647568	0.466	0.088
REEP5	1.4421832	0.595	0.116
MAPKAPK2	2.5200541	0.571	0.150
TP53INP1	1.7818256	0.577	0.116
RRBP1	1.4359914	0.526	0.082
HDLBP	1.1966229	0.583	0.116
CRELD2	0.9641654	0.615	0.136
RPN1	1.1471368	0.530	0.088
RPN2	1.1247379	0.595	0.122
SEC24A	1.3285041	0.512	0.075
TMEM59	1.0337425	0.563	0.102
IFI6	2.9357088	0.457	0.054
FKBP11	0.8579383	0.832	0.279
CTSS	1.5527149	0.537	0.095
KLHL6	2.1915235	0.545	0.259
ERGIC3	1.3970431	0.636	0.156
XBP1	1.4436010	0.926	0.565
MYDGF	1.0861289	0.740	0.224
DERL1	0.7576779	0.639	0.156
GALNT2	1.2284700	0.478	0.068
EIF2AK3	4.5007512	0.427	0.075

Plasma B : Plasma B cells_3 vs Plasma B cells_4

Top 50 markers: Plasma B cells_3 vs Plasma B cells_4
	avg_log2FC	pct.1	pct.2
POU2AF1	3.720737	0.971	0.449
UBE2J1	3.503080	0.986	0.558
EDEM1	3.293960	0.971	0.422
SYVN1	3.466081	0.945	0.252
CCDC69	4.127556	0.887	0.245
MAPKAPK2	3.547553	0.927	0.150
CD79A	2.520211	0.988	0.741
TXNDC5	2.562139	0.994	0.456
GSTP1	4.072814	0.890	0.231
KLHL6	4.843316	0.857	0.259
SEC14L1	3.196859	0.926	0.279
EEF2	1.944405	0.991	0.721
CALR	2.785263	0.934	0.143
CHPF2	3.615526	0.817	0.075
SLC35B1	4.858978	0.805	0.027
BTG2	2.472853	0.979	0.401
JCHAIN	3.233184	0.975	0.429
ARF1	2.942833	0.892	0.150
SF3B5	4.757657	0.789	0.088
CTSH	3.461705	0.862	0.259
TNFRSF17	3.340275	0.873	0.129
HYOU1	2.519024	0.922	0.116
FAM107B	4.249232	0.835	0.320
CD38	2.829048	0.926	0.299
EZR	6.237012	0.847	0.524
EIF2AK3	5.756637	0.764	0.075
PLCG2	2.819621	0.890	0.272
MZB1	1.892044	0.996	0.537
UBA5	6.674189	0.755	0.048
SEL1L3	2.369162	0.959	0.415
PRDM1	2.248052	0.946	0.129
CCNI	2.553032	0.906	0.327
TUFM	3.100025	0.844	0.197
USP48	3.033673	0.848	0.122
SEMA4A	8.278419	0.754	0.088
HNRNPC	6.455939	0.782	0.197
TXNDC11	2.063737	0.980	0.197
TOP1	3.593476	0.817	0.224
LITAF	3.545522	0.782	0.156
BAG6	8.198896	0.748	0.109
TMEM184B	2.226170	0.833	0.068
CCM2	3.309842	0.801	0.095
TRIB1	2.161479	0.815	0.054
CCDC88A	7.528682	0.754	0.136
ISG20	2.213006	0.953	0.265
RBM6	5.044023	0.751	0.122
BST2	3.087783	0.855	0.143
TENT5C	1.281836	0.961	0.143
BLNK	2.878099	0.837	0.156
PPIB	2.033483	0.958	0.177

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(naive_b,memory_b,gc_b,plasma_b)) {
  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.bcells", raster = F, repel = T, label = T, group.by = "lineage")

DimPlot(paed_sub, reduction = "umap.bcells", raster = F, repel = T, label = T, group.by = "cell_labels_l1")

DimPlot(paed_sub, reduction = "umap.bcells", raster = F, repel = T, label = T, group.by = "cell_labels_l1", split.by = "tissue")

bcell_l2_order <- c(
  "Pre B",
  "Naïve B",
  "Naïve B-IFN",
  "Naïve B activated",
  "Early MBC",
  "csMBC",
  "ncsMBC",
  "FCRL4or5",
  "Non-Cycling GCB_0",
  "Non-Cycling GCB_1",
  "Non-Cycling GCB_2",
  "Non-Cycling GCB_3",
  "Cycling GCB_0",
  "Cycling GCB_1",
  "Cycling GCB_2",
  "Cycling GCB_3",
  "Plasma B cells_1",
  "Plasma B cells_2",
  "Plasma B cells_3",
  "Plasma B cells_4"
)

paed_sub$cell_labels_l2 <- factor(
  paed_sub$cell_labels_l2,
  levels = bcell_l2_order
)
DimPlot(paed_sub, reduction = "umap.bcells", raster = F, repel = T, label = T, group.by = "cell_labels_l2")

bcell_l3_order <- c(
  "Pre B",

  "Naïve B_0",
  "Naïve B_1",
  "Naïve B_2",
  "Naïve B_3",

  "Naïve B-IFN_0",
  "Naïve B-IFN_1",

  "Naïve B activated_0",
  "Naïve B activated_1",

  "Early MBC",

  "csMBC_0",
  "csMBC_1",

  "ncsMBC_0",
  "ncsMBC_1",

  "FCRL4or5_0",
  "FCRL4or5_1",
  "FCRL4or5_2",
  "FCRL4or5_3",

  "Non-Cycling GCB_0",
  "Non-Cycling GCB_1",
  "Non-Cycling GCB_2",
  "Non-Cycling GCB_3",

  "Cycling GCB_0",
  "Cycling GCB_1",
  "Cycling GCB_2",
  "Cycling GCB_3",

  "Plasma B cells_1",
  "Plasma B cells_2",
  "Plasma B cells_3",
  "Plasma B cells_4"
)

paed_sub$cell_labels_l3 <- factor(
  paed_sub$cell_labels_l3,
  levels = bcell_l3_order
)
DimPlot(paed_sub, reduction = "umap.bcells", raster = F, repel = T, label = T, group.by = "cell_labels_l3")

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"
    )

Save RDS

saveRDS(paed_sub, "../EarlyAir_paper/output/RDS/Annotated_lineages/SEU_B_Cells_annotated.rds")

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] presto_1.0.0        circlize_0.4.16     BiocParallel_1.36.0
 [4] harmony_1.2.3       Rcpp_1.0.14         patchwork_1.3.0    
 [7] cowplot_1.1.3       data.table_1.17.2   RColorBrewer_1.1-3 
[10] kableExtra_1.4.0    ggridges_0.5.6      ggforce_0.4.2      
[13] viridis_0.6.5       viridisLite_0.4.2   paletteer_1.6.0    
[16] clustree_0.5.1      ggraph_2.2.1        Seurat_5.0.3       
[19] SeuratObject_5.1.0  sp_2.2-0            here_1.0.1         
[22] lubridate_1.9.4     forcats_1.0.0       stringr_1.5.1      
[25] dplyr_1.1.4         purrr_1.0.4         readr_2.1.5        
[28] tidyr_1.3.1         tibble_3.2.1        ggplot2_3.5.2      
[31] tidyverse_2.0.0     BiocStyle_2.30.0    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   goftest_1.2-3          RSpectra_0.16-2       
 [37] spatstat.random_3.4-1  fitdistrplus_1.2-2     parallelly_1.44.0     
 [40] svglite_2.2.1          leiden_0.4.3.1         codetools_0.2-19      
 [43] xml2_1.3.8             tidyselect_1.2.1       shape_1.4.6.1         
 [46] farver_2.1.2           matrixStats_1.5.0      spatstat.explore_3.4-3
 [49] jsonlite_2.0.0         tidygraph_1.3.1        progressr_0.15.1      
 [52] survival_3.5-8         systemfonts_1.2.3      tools_4.3.3           
 [55] ica_1.0-3              glue_1.8.0             gridExtra_2.3         
 [58] xfun_0.52              withr_3.0.2            BiocManager_1.30.25   
 [61] fastmap_1.2.0          callr_3.7.6            digest_0.6.37         
 [64] timechange_0.3.0       R6_2.6.1               mime_0.13             
 [67] textshaping_1.0.1      colorspace_2.1-1       scattermore_1.2       
 [70] tensor_1.5             spatstat.data_3.1-6    utf8_1.2.5            
 [73] generics_0.1.4         graphlayouts_1.2.2     httr_1.4.7            
 [76] htmlwidgets_1.6.4      whisker_0.4.1          uwot_0.2.3            
 [79] pkgconfig_2.0.3        gtable_0.3.6           lmtest_0.9-40         
 [82] htmltools_0.5.8.1      dotCall64_1.2          scales_1.4.0          
 [85] png_0.1-8              spatstat.univar_3.1-3  knitr_1.50            
 [88] rstudioapi_0.17.1      tzdb_0.5.0             reshape2_1.4.4        
 [91] checkmate_2.3.2        nlme_3.1-164           cachem_1.1.0          
 [94] zoo_1.8-14             GlobalOptions_0.1.2    KernSmooth_2.23-22    
 [97] vipor_0.4.7            parallel_4.3.3         miniUI_0.1.2          
[100] ggrastr_1.0.2          pillar_1.10.2          grid_4.3.3            
[103] vctrs_0.6.5            RANN_2.6.2             promises_1.3.2        
[106] xtable_1.8-4           cluster_2.1.6          beeswarm_0.4.0        
[109] evaluate_1.0.3         cli_3.6.5              compiler_4.3.3        
[112] rlang_1.1.6            crayon_1.5.3           future.apply_1.11.3   
[115] labeling_0.4.3         rematch2_2.1.2         ps_1.9.1              
[118] ggbeeswarm_0.7.2       getPass_0.2-4          plyr_1.8.9            
[121] fs_1.6.6               stringi_1.8.7          deldir_2.0-4          
[124] lazyeval_0.2.2         spatstat.geom_3.4-1    Matrix_1.6-5          
[127] RcppHNSW_0.6.0         hms_1.1.3              future_1.40.0         
[130] statmod_1.5.0          shiny_1.10.0           ROCR_1.0-11           
[133] igraph_2.1.4           memoise_2.0.1          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] presto_1.0.0        circlize_0.4.16     BiocParallel_1.36.0
 [4] harmony_1.2.3       Rcpp_1.0.14         patchwork_1.3.0    
 [7] cowplot_1.1.3       data.table_1.17.2   RColorBrewer_1.1-3 
[10] kableExtra_1.4.0    ggridges_0.5.6      ggforce_0.4.2      
[13] viridis_0.6.5       viridisLite_0.4.2   paletteer_1.6.0    
[16] clustree_0.5.1      ggraph_2.2.1        Seurat_5.0.3       
[19] SeuratObject_5.1.0  sp_2.2-0            here_1.0.1         
[22] lubridate_1.9.4     forcats_1.0.0       stringr_1.5.1      
[25] dplyr_1.1.4         purrr_1.0.4         readr_2.1.5        
[28] tidyr_1.3.1         tibble_3.2.1        ggplot2_3.5.2      
[31] tidyverse_2.0.0     BiocStyle_2.30.0    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   goftest_1.2-3          RSpectra_0.16-2       
 [37] spatstat.random_3.4-1  fitdistrplus_1.2-2     parallelly_1.44.0     
 [40] svglite_2.2.1          leiden_0.4.3.1         codetools_0.2-19      
 [43] xml2_1.3.8             tidyselect_1.2.1       shape_1.4.6.1         
 [46] farver_2.1.2           matrixStats_1.5.0      spatstat.explore_3.4-3
 [49] jsonlite_2.0.0         tidygraph_1.3.1        progressr_0.15.1      
 [52] survival_3.5-8         systemfonts_1.2.3      tools_4.3.3           
 [55] ica_1.0-3              glue_1.8.0             gridExtra_2.3         
 [58] xfun_0.52              withr_3.0.2            BiocManager_1.30.25   
 [61] fastmap_1.2.0          callr_3.7.6            digest_0.6.37         
 [64] timechange_0.3.0       R6_2.6.1               mime_0.13             
 [67] textshaping_1.0.1      colorspace_2.1-1       scattermore_1.2       
 [70] tensor_1.5             spatstat.data_3.1-6    utf8_1.2.5            
 [73] generics_0.1.4         graphlayouts_1.2.2     httr_1.4.7            
 [76] htmlwidgets_1.6.4      whisker_0.4.1          uwot_0.2.3            
 [79] pkgconfig_2.0.3        gtable_0.3.6           lmtest_0.9-40         
 [82] htmltools_0.5.8.1      dotCall64_1.2          scales_1.4.0          
 [85] png_0.1-8              spatstat.univar_3.1-3  knitr_1.50            
 [88] rstudioapi_0.17.1      tzdb_0.5.0             reshape2_1.4.4        
 [91] checkmate_2.3.2        nlme_3.1-164           cachem_1.1.0          
 [94] zoo_1.8-14             GlobalOptions_0.1.2    KernSmooth_2.23-22    
 [97] vipor_0.4.7            parallel_4.3.3         miniUI_0.1.2          
[100] ggrastr_1.0.2          pillar_1.10.2          grid_4.3.3            
[103] vctrs_0.6.5            RANN_2.6.2             promises_1.3.2        
[106] xtable_1.8-4           cluster_2.1.6          beeswarm_0.4.0        
[109] evaluate_1.0.3         cli_3.6.5              compiler_4.3.3        
[112] rlang_1.1.6            crayon_1.5.3           future.apply_1.11.3   
[115] labeling_0.4.3         rematch2_2.1.2         ps_1.9.1              
[118] ggbeeswarm_0.7.2       getPass_0.2-4          plyr_1.8.9            
[121] fs_1.6.6               stringi_1.8.7          deldir_2.0-4          
[124] lazyeval_0.2.2         spatstat.geom_3.4-1    Matrix_1.6-5          
[127] RcppHNSW_0.6.0         hms_1.1.3              future_1.40.0         
[130] statmod_1.5.0          shiny_1.10.0           ROCR_1.0-11           
[133] igraph_2.1.4           memoise_2.0.1          bslib_0.9.0

Lineages subclustering- B Cells

Combined plots for all batches

Gunjan Dixit

January 27, 2026