Supercells Combined with Clustering Can Identify Rare B Cell Subsets
Givanna Putri
2023-05-24
Last updated: 2024-01-31
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Knit directory: SuperCellCyto-analysis/
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Introduction
In this analysis, we seek to verify whether we can faithfully replicate the traditional clustering and annotation process, and identify rare cell populations at the supercell level
The flow cytometry data set we employed were the large flow cytometry dataset profiling more than 8 million B cells in healthy human bone marrow samples (Oetjen et al. 2018).
The annotation for the dataset were not publicly available. However, the authors have provided the following gating strategy within the manuscript’s supplementary material (Oetjen et al. 2018), which can be employed to discern B cells within the data:
- Pre-B cell (CD20-): CD45+ CD19+ CD20- CD10+
- Immature B cells (CD20+): CD45+ CD19+ CD20+ CD10+
- Transitional B cells: CD45+ CD19+ CD20+ CD10+ CD27-
- Mature B cells: CD45+ CD19+ CD20+ CD10-
- Naïve Mature B cells: CD45+ CD19+ CD20+ CD10- CD27- CD21hi
- Exhausted/Tissue-like Memory: CD45+ CD19+ CD20+ CD10- CD27- CD21lo
- Memory B cells: CD45+ CD19+ CD20+ CD10- CD27+
- Activated Mature: CD45+ CD19+ CD20+ CD10- CD27+ CD21lo
- Resting Memory: CD45+ CD19+ CD20+ CD10- CD27+ CD21hi
- CD20- CD10-: CD45+ CD19+ CD20- CD10-
- Plasmablast: CD45+ CD19+ CD20- CD10- CD27+ CD38+
- Plasma Cell: CD45+ CD19+ CD20- CD10- CD27+ CD38+ CD138+
- Plasmablast: CD45+ CD19+ CD20- CD10- CD27+ CD38+
The B cell panel consists of 13 markers, namely:
- CD80
- Live/Dead
- CD27
- CD19
- CD45
- CD10
- CD138
- PD-1
- CD20
- CD38
- CD86
- CD21
- CD40
The process employed in this study comprises the following steps: FCS files were initially downloaded from the FlowRepository website FR-FCM-ZYQ9.
Single live cells were isolated through manual gating using
CytoExplorer (Hammill 2021) (the relevant
code can be found in
code/b_cell_identification/gate_flow_data.R). The following
single live cells were retained.
knitr::include_graphics("assets/oetjen_bcell_single_live_cells.png", error = FALSE)
| Version | Author | Date |
|---|---|---|
| 7f102a5 | Givanna Putri | 2023-07-28 |
Subsequently, the markers were transformed using the arcsinh transformation with a co-factor of 150. This was followed by the application of SuperCellCyto, which produced a set of supercells. These supercells were clustered using Louvain clustering with k parameter set to 3.
All code used to execute these procedures can be found in
code/oetjen_bm_data/cluster_run_supercell.R.
Load libraries
library(scales)
library(SingleCellExperiment)
library(ggforce)
library(here)
library(data.table)
library(cowplot)
library(Spectre)
library(ggplot2)
library(viridis)
library(bluster)
library(pheatmap)
library(ggridges)Load data
root_dir <- here("output", "oetjen_b_cell_panel", "20230511")
supercell_mat <- fread(here(root_dir, "supercellExpMat_clust_umap.csv"))
markers <- names(supercell_mat)[1:12]
cell_type_markers <- paste0(
c("CD19", "CD45", "CD10", "CD20", "CD27", "CD21", "CD38", "CD138"),
"_asinh_cf150"
)Identify B cells
Use pheatmap annotate the clusters.
supercell_mat_forHeatmap <- supercell_mat[, c("louvain_k3", cell_type_markers), with = FALSE]
supercell_mat_forHeatmap <- supercell_mat_forHeatmap[order(louvain_k3)]
supercell_mat_forHeatmap[, cluster := paste0("cluster_", louvain_k3)]
supercell_mat_forHeatmap[, louvain_k3 := NULL]
median_exp <- supercell_mat_forHeatmap[, lapply(.SD, median), by = cluster, .SDcols = cell_type_markers]
median_exp_mat <- as.matrix(median_exp[, cell_type_markers, with = FALSE])
row.names(median_exp_mat) <- median_exp$cluster
colnames(median_exp_mat) <- gsub("_asinh_cf150", "", colnames(median_exp_mat))
pheatmap(
median_exp_mat,
cluster_cols = FALSE,
cluster_rows = FALSE,
display_numbers = TRUE,
number_format = "%.2f",
scale = "column",
main = "Median Expression of Cell Type Markers Across Clusters"
)
| Version | Author | Date |
|---|---|---|
| 366514e | Givanna Putri | 2023-07-28 |
Using the heatmap, we derive the following cell type annotations.
annotation <- rep("CD45- and/or CD19-", 46)
annotation[41] <- "Pre B cells"
annotation[18] <- "Immature B cells"
annotation[26] <- "Transitional B cells"
annotation[c(27, 29, 32, 38, 39, 40)] <- "CD20- CD10-"
annotation[37] <- "Plasmablast"
annotation[46] <- "Plasma cells"
annotation[22] <- "Naive Mature B cells"
annotation[15] <- "Exhausted Tissue like Memory"
annotation[30] <- "Resting Memory"
annotation[c(28, 43)] <- "Activated Mature"
annotation_df <- data.table(cluster = paste0("cluster_", seq(1, 46)), cell_type = annotation)Create the heatmap which show median expression and also the annotation for each cluster.
# The order is for ordering the heatmap so the same cell types are together
heatmap_annot_col <- annotation_df[order(cell_type)]
heatmap_annot_col <- heatmap_annot_col[cell_type != "CD45- and/or CD19-"]
heatmap_annot_col <- data.frame(heatmap_annot_col, row.names = "cluster")
annot_cell_types <- unique(heatmap_annot_col$cell_type)
annot_colours <- viridis(n = length(annot_cell_types), option = "turbo")
names(annot_colours) <- annot_cell_types
# Re-order heatmap to match annotation so the same cell types are together
median_exp_mat_noUnassigned <- median_exp_mat[rownames(heatmap_annot_col), ]
pheatmap(
t(median_exp_mat_noUnassigned),
cluster_cols = FALSE,
cluster_rows = FALSE,
annotation_col = heatmap_annot_col,
# scale = "column",
main = "Median Expression of Cell Type Markers Across Clusters",
annotation_colors = list(cell_type = annot_colours),
angle_col = 90
)
| Version | Author | Date |
|---|---|---|
| 366514e | Givanna Putri | 2023-07-28 |
We then embed them to the supercell matrix.
supercell_mat_annot <- supercell_mat
supercell_mat_annot[, cluster := paste0("cluster_", louvain_k3)]
supercell_mat_annot <- merge.data.table(
x = supercell_mat,
y = annotation_df,
by = "cluster"
)Following this, we generate the UMAP plot reflecting these
annotations. The UMAP coordinates were generated using the Spectre
package. The code to generate the UMAP coordinates is available on
code/oetjen_bm_data/cluster_run_supercell.R.
cell_types <- sort(unique(supercell_mat_annot$cell_type))
cell_types <- cell_types[cell_types != "CD45- and/or CD19-"]
colour_scheme <- viridis(n = length(cell_types), option = "turbo")
names(colour_scheme) <- cell_types
colour_scheme["CD45- and/or CD19-"] <- "#D3D3D3"
# So unknown is at the bottom.
supercell_mat_annot[, cell_type := factor(cell_type, levels = names(colour_scheme))]
ggplot(supercell_mat_annot, aes(x = UMAP_X, y = UMAP_Y, colour = cell_type)) +
geom_point(size = 0.2) +
theme_classic() +
guides(colour = guide_legend(override.aes = list(size = 5))) +
scale_colour_manual(values = colour_scheme) +
labs(colour = "Cell type")
To be more sure that our cell type annotation is reasonable, we shall expand our annotated supercells to the single cell level and visualise the distribution of their marker expression. We will exclude those we assigned “unknown” label.
Remove unassigned for heatmap.
supercell_mat_annot_noUnassigned <- supercell_mat_annot[cell_type != "CD45- and/or CD19-"]Check the distribution of the marker expression of the single cells expanded from the annotated supercells.
cell_mat <- fread(here(
"output", "oetjen_b_cell_panel", "20230404_cytoexplorer_gating",
"cell_dat_asinh.csv"
))
cell_map_full <- fread(here(
"output", "oetjen_b_cell_panel", "20230511",
"supercellCellMap.csv"
))
cell_map <- merge.data.table(
x = cell_map_full,
y = supercell_mat_annot_noUnassigned[, c("SuperCellId", "cell_type")],
by.x = "SuperCellID",
by.y = "SuperCellId"
)
cell_map <- cell_map[cell_type != "CD45- and/or CD19-", ]
cell_mat <- merge.data.table(
x = cell_mat,
y = cell_map,
by = "CellId"
)
cell_mat_long <- melt(
data = cell_mat,
id.vars = "cell_type",
measure.vars = cell_type_markers,
variable.name = "marker"
)
cell_mat_long$marker <- gsub("_asinh_cf150", "", cell_mat_long$marker)
# So we can order the y-axis of the plot so the order somewhat resembles the
# hierarchical structure of the gating strategy.
cell_mat_long$marker <- factor(cell_mat_long$marker,
levels = c("CD45", "CD19", "CD20", "CD10", "CD27", "CD21", "CD38", "CD138")
)
cell_mat_long <- cell_mat_long[order(marker)]ggplot(cell_mat_long, aes(x = value, y = marker, colour = marker, fill = marker)) +
geom_density_ridges(alpha = 0.3) +
scale_colour_viridis(option = "turbo", discrete = TRUE, guide = "none") +
scale_fill_viridis(option = "turbo", discrete = TRUE, guide = "none") +
theme_ridges() +
facet_wrap(~cell_type) +
scale_x_continuous(breaks = pretty_breaks(n = 10)) +
labs(
y = "Marker", x = "Marker Expression",
title = "Distribution of Marker Expression of Single Cells"
)
| Version | Author | Date |
|---|---|---|
| 366514e | Givanna Putri | 2023-07-28 |
How many supercells unannotated.
table(supercell_mat_annot$cell_type)["CD45- and/or CD19-"] / nrow(supercell_mat_annot)CD45- and/or CD19-
0.6827868 How many plasma cells we found?
plasma_cell_supercells <- supercell_mat_annot[cell_type == "Plasma cells"]
plasma_cells <- cell_map_full[SuperCellID %in% plasma_cell_supercells$SuperCellId]
nrow(plasma_cells)[1] 162100*nrow(plasma_cells) / nrow(cell_map_full)[1] 0.00194846nrow(plasma_cell_supercells)[1] 14Plot the proportion of each cell type as boxplot.
cell_map <- merge.data.table(
x = cell_map_full,
y = supercell_mat_annot[, c("SuperCellId", "cell_type", "louvain_k3")],
by.x = "SuperCellID",
by.y = "SuperCellId"
)
# Proportion for each cluster and cell type
cell_type_proportion <- cell_map[, .(cnt_per_clust = .N), by = c("cell_type", "Sample")]
cell_type_proportion[, proportion := cnt_per_clust / nrow(cell_map)]
cell_type_proportion_noUnassigned <- cell_type_proportion[cell_type != "CD45- and/or CD19-", ]
# Get the cell type name ordered alphabetically so we match the colour to the heatmap
ordered_cell_name <- sort(cell_type_proportion_noUnassigned$cell_type)
cell_name_color <- turbo(n = length(ordered_cell_name))
names(cell_name_color) <- ordered_cell_name
# Order from highest proportion to low
# cell_type_proportion_noUnassigned <- cell_type_proportion_noUnassigned[order(-proportion)]
# cell_type_proportion_noUnassigned[, cell_type := factor(cell_type, levels = cell_type_proportion_noUnassigned$cell_type)]ggplot(cell_type_proportion_noUnassigned, aes(x = cell_type, y = proportion)) +
geom_boxplot() +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "none") +
scale_y_continuous(breaks = pretty_breaks(n = 10)) +
scale_color_manual(values = cell_name_color, breaks = names(cell_name_color)) +
labs(
y = "Proportion of single cells", x = "Cell type", color = "Cell type",
title = "Proportion of B cell subsets across all samples"
)
References
sessionInfo()R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 14.0
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggridges_0.5.4 pheatmap_1.0.12
[3] bluster_1.8.0 viridis_0.6.2
[5] viridisLite_0.4.1 Spectre_1.0.0-0
[7] cowplot_1.1.1 data.table_1.14.10
[9] here_1.0.1 ggforce_0.4.1
[11] ggplot2_3.4.1 SingleCellExperiment_1.20.0
[13] SummarizedExperiment_1.28.0 Biobase_2.58.0
[15] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[17] IRanges_2.32.0 S4Vectors_0.36.1
[19] BiocGenerics_0.44.0 MatrixGenerics_1.10.0
[21] matrixStats_0.63.0 scales_1.2.1
[23] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] spam_2.10-0 plyr_1.8.8 igraph_1.4.0
[4] lazyeval_0.2.2 sp_1.6-0 splines_4.2.3
[7] flowCore_2.10.0 RcppHNSW_0.4.1 BiocParallel_1.32.6
[10] listenv_0.9.0 scattermore_1.2 digest_0.6.31
[13] htmltools_0.5.4 fansi_1.0.4 magrittr_2.0.3
[16] tensor_1.5 cluster_2.1.4 ROCR_1.0-11
[19] limma_3.54.1 globals_0.16.2 cytolib_2.10.0
[22] spatstat.sparse_3.0-0 colorspace_2.1-0 ggrepel_0.9.3
[25] xfun_0.39 dplyr_1.1.0 callr_3.7.3
[28] RCurl_1.98-1.10 jsonlite_1.8.4 progressr_0.13.0
[31] spatstat.data_3.0-0 survival_3.5-3 zoo_1.8-11
[34] glue_1.6.2 polyclip_1.10-4 gtable_0.3.1
[37] zlibbioc_1.44.0 XVector_0.38.0 leiden_0.4.3
[40] DelayedArray_0.24.0 future.apply_1.10.0 abind_1.4-5
[43] spatstat.random_3.1-3 miniUI_0.1.1.1 Rcpp_1.0.10
[46] xtable_1.8-4 reticulate_1.28 rsvd_1.0.5
[49] dotCall64_1.1-1 htmlwidgets_1.6.1 httr_1.4.4
[52] RColorBrewer_1.1-3 ellipsis_0.3.2 Seurat_5.0.1
[55] ica_1.0-3 pkgconfig_2.0.3 farver_2.1.1
[58] uwot_0.1.14 deldir_1.0-6 sass_0.4.5
[61] utf8_1.2.3 labeling_0.4.2 tidyselect_1.2.0
[64] rlang_1.0.6 reshape2_1.4.4 later_1.3.0
[67] munsell_0.5.0 tools_4.2.3 cachem_1.0.6
[70] cli_3.6.1 generics_0.1.3 evaluate_0.20
[73] stringr_1.5.0 fastmap_1.1.0 goftest_1.2-3
[76] yaml_2.3.7 processx_3.8.0 knitr_1.42
[79] fs_1.6.1 fitdistrplus_1.1-8 purrr_1.0.1
[82] RANN_2.6.1 nlme_3.1-162 pbapply_1.7-0
[85] future_1.33.1 whisker_0.4.1 mime_0.12
[88] compiler_4.2.3 rstudioapi_0.14 plotly_4.10.1
[91] png_0.1-8 spatstat.utils_3.0-1 tibble_3.1.8
[94] tweenr_2.0.2 bslib_0.4.2 stringi_1.7.12
[97] highr_0.10 ps_1.7.2 RSpectra_0.16-1
[100] lattice_0.20-45 Matrix_1.6-4 vctrs_0.5.2
[103] pillar_1.8.1 lifecycle_1.0.3 spatstat.geom_3.0-6
[106] lmtest_0.9-40 jquerylib_0.1.4 BiocNeighbors_1.16.0
[109] RcppAnnoy_0.0.20 bitops_1.0-7 irlba_2.3.5.1
[112] httpuv_1.6.9 patchwork_1.1.2 R6_2.5.1
[115] promises_1.2.0.1 RProtoBufLib_2.10.0 KernSmooth_2.23-20
[118] gridExtra_2.3 parallelly_1.34.0 codetools_0.2-19
[121] fastDummies_1.7.3 MASS_7.3-58.2 rprojroot_2.0.3
[124] withr_2.5.0 SeuratObject_5.0.1 sctransform_0.4.1
[127] GenomeInfoDbData_1.2.9 parallel_4.2.3 grid_4.2.3
[130] tidyr_1.3.0 rmarkdown_2.20 Rtsne_0.16
[133] git2r_0.31.0 getPass_0.2-2 spatstat.explore_3.0-6
[136] shiny_1.7.4