Annotate T/NK clusters
Jovana Maksimovic
September 10, 2024
Last updated: 2024-09-10
Checks: 7 0
Knit directory: paed-inflammation-CITEseq/
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Load libraries
Load Data
ambient <- "_decontx"
out <- here("data",
"C133_Neeland_merged",
glue("C133_Neeland_full_clean{ambient}_integrated_clustered_mapped_t_cells.ADT.SEU.rds"))
seuInt <- readRDS(file = out)
seuIntAn object of class Seurat
38612 features across 15511 samples within 4 assays
Active assay: integrated (3000 features, 0 variable features)
3 other assays present: RNA, ADT, SCT
2 dimensional reductions calculated: pca, umapUpdate group labels
seuInt@meta.data %>%
data.frame %>%
mutate(Group = ifelse(str_detect(Treatment, "ivacaftor"),
"CF.IVA",
ifelse(str_detect(Treatment, "orkambi"),
"CF.LUMA_IVA",
ifelse(Treatment == "untreated",
"CF.NO_MOD",
"NON_CF.CTRL"))),
Group_severity = ifelse(!Group %in% "NON_CF.CTRL",
paste(Group,
toupper(substr(Severity, 1, 1)),
sep = "."),
Group),
Severity = tolower(Severity),
Participant = strsplit2(sample.id, ".", fixed = TRUE)[,1]) -> seuInt@meta.dataSub-cluster labelling
Load manual annotations
labels <- read_excel(here("data",
"cluster_annotations",
"T-NK_ambientRNAremoval_21.03.24.xlsx"),
skip = 1)
# set selected cluster resolution
grp <- "wsnn_res.0.6"
seuInt@meta.data %>%
rownames_to_column(var = "cell") %>%
left_join(labels %>%
mutate(Cluster = as.factor(Cluster),
ann_level_3 = as.factor(ann_level_3),
ann_level_2 = as.factor(ann_level_2),
ann_level_1 = as.factor(ann_level_1)),
by = c("wsnn_res.0.6" = "Cluster")) %>%
column_to_rownames(var = "cell") -> seuInt@meta.data
seuInt <- subset(seuInt, cells = which(seuInt$ann_level_3 != "unknown"))
seuInt$ann_level_3 <- fct_drop(seuInt$ann_level_3)
seuInt$ann_level_2 <- fct_drop(seuInt$ann_level_2)
seuInt$ann_level_1 <- fct_drop(seuInt$ann_level_1)
seuIntAn object of class Seurat
38612 features across 15511 samples within 4 assays
Active assay: integrated (3000 features, 0 variable features)
3 other assays present: RNA, ADT, SCT
2 dimensional reductions calculated: pca, umapVisualise annotations
options(ggrepel.max.overlaps = Inf)
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE,
label.size = 3, group.by = grp) +
NoLegend() -> p1
cluster_pal <- "ggsci::category20_d3"
DimPlot(seuInt, reduction = 'umap', label = FALSE, group.by = "ann_level_1") +
scale_color_paletteer_d(cluster_pal) +
theme(text = element_text(size = 9),
axis.text = element_blank(),
axis.ticks = element_blank()) +
NoLegend() -> p2
DimPlot(seuInt, reduction = 'umap', label = FALSE, group.by = "ann_level_3") +
scale_color_paletteer_d(cluster_pal) +
theme(text = element_text(size = 9),
axis.text = element_blank(),
axis.ticks = element_blank()) +
NoLegend() -> p3
p1
LabelClusters(p2, id = "ann_level_1", repel = TRUE,
size = 2.5, box = TRUE, fontfamily = "arial")
LabelClusters(p3, id = "ann_level_3", repel = TRUE,
size = 2.5, box = TRUE, fontfamily = "arial")
No. cells per cluster
seuInt@meta.data %>%
ggplot(aes(x = ann_level_1, fill = ann_level_1)) +
geom_bar() +
geom_text(aes(label = after_stat(count)), stat = "count",
vjust = -0.5, colour = "black", size = 2) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
NoLegend() +
scale_fill_paletteer_d(cluster_pal)
seuInt@meta.data %>%
ggplot(aes(x = ann_level_3, fill = ann_level_3)) +
geom_bar() +
geom_text(aes(label = after_stat(count)), stat = "count",
vjust = -0.5, colour = "black", size = 2) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
NoLegend() +
scale_fill_paletteer_d(cluster_pal)
% cells per cluster
seuInt@meta.data %>%
count(ann_level_1) %>%
mutate(perc = round(n/sum(n)*100, 1)) %>%
dplyr::rename(`Cell Label` = "ann_level_1",
`No. Cells` = n,
`% Cells` = perc) %>%
knitr::kable()| Cell Label | No. Cells | % Cells |
|---|---|---|
| CD4 T cells | 5482 | 35.3 |
| CD8 T cells | 7268 | 46.9 |
| gamma delta T cells | 244 | 1.6 |
| innate lymphocyte | 1481 | 9.5 |
| NK cells | 695 | 4.5 |
| NK-T cells | 91 | 0.6 |
| proliferating T/NK | 250 | 1.6 |
seuInt@meta.data %>%
count(ann_level_3) %>%
mutate(perc = round(n/sum(n)*100, 1)) %>%
dplyr::rename(`Cell Label` = "ann_level_3",
`No. Cells` = n,
`% Cells` = perc) %>%
knitr::kable()| Cell Label | No. Cells | % Cells |
|---|---|---|
| CD4 T cells | 3474 | 22.4 |
| CD4 T-IFN | 303 | 2.0 |
| CD4 T-naïve | 547 | 3.5 |
| CD4 T-NFKB | 553 | 3.6 |
| CD4 T-reg | 456 | 2.9 |
| CD4 T-rm | 149 | 1.0 |
| CD8 T-GZMK | 791 | 5.1 |
| CD8 T-inflammasome | 2867 | 18.5 |
| CD8 T-rm | 3610 | 23.3 |
| gamma delta T cells | 244 | 1.6 |
| innate lymphocytes | 1481 | 9.5 |
| NK cells | 695 | 4.5 |
| NK-T cells | 91 | 0.6 |
| proliferating T/NK | 250 | 1.6 |
RNA marker gene analysis
Adapted from Dr. Belinda Phipson’s work for [@Sim2021-cg].
Test for marker genes using limma
# limma-trend for DE
Idents(seuInt) <- "ann_level_3"
out <- here("data",
"C133_Neeland_merged",
glue("C133_Neeland_full_clean{ambient}_TNK_cells_logcounts.SEU.rds"))
if(!file.exists(out)){
logcounts <- normCounts(DGEList(as.matrix(seuInt[["RNA"]]@counts)),
log = TRUE, prior.count = 0.5)
entrez <- AnnotationDbi::mapIds(org.Hs.eg.db,
keys = rownames(logcounts),
column = c("ENTREZID"),
keytype = "SYMBOL",
multiVals = "first")
# remove genes without entrez IDs as these are difficult to interpret biologically
logcounts <- logcounts[!is.na(entrez),]
saveRDS(logcounts, file = out)
} else {
logcounts <- readRDS(out)
}
maxclust <- length(levels(Idents(seuInt))) - 1
clustgrp <- seuInt$ann_level_3
clustgrp <- factor(clustgrp)
donor <- factor(seuInt$sample.id)
batch <- factor(seuInt$Batch)
design <- model.matrix(~ 0 + clustgrp + donor)
colnames(design)[1:(length(levels(clustgrp)))] <- levels(clustgrp)
# Create contrast matrix
mycont <- matrix(NA, ncol = length(levels(clustgrp)),
nrow = length(levels(clustgrp)))
rownames(mycont) <- colnames(mycont) <- levels(clustgrp)
diag(mycont) <- 1
mycont[upper.tri(mycont)] <- -1/(length(levels(factor(clustgrp))) - 1)
mycont[lower.tri(mycont)] <- -1/(length(levels(factor(clustgrp))) - 1)
# Fill out remaining rows with 0s
zero.rows <- matrix(0, ncol = length(levels(clustgrp)),
nrow = (ncol(design) - length(levels(clustgrp))))
fullcont <- rbind(mycont, zero.rows)
rownames(fullcont) <- colnames(design)
fit <- lmFit(logcounts, design)
fit.cont <- contrasts.fit(fit, contrasts = fullcont)
fit.cont <- eBayes(fit.cont, trend = TRUE, robust = TRUE)
summary(decideTests(fit.cont)) CD4 T cells CD4 T-IFN CD4 T-naïve CD4 T-NFKB CD4 T-reg CD4 T-rm
Down 1557 119 1439 430 516 33
NotSig 13406 15380 13814 14693 14073 15672
Up 847 311 557 687 1221 105
CD8 T-GZMK CD8 T-inflammasome CD8 T-rm gamma delta T cells
Down 653 2951 1639 223
NotSig 14721 12620 13493 15364
Up 436 239 678 223
innate lymphocytes NK cells NK-T cells proliferating T/NK
Down 1642 838 73 106
NotSig 13226 14106 15014 12767
Up 942 866 723 2937Test relative to a threshold (TREAT).
tr <- treat(fit.cont, lfc = 0.5)
dt <- decideTests(tr)
summary(dt) CD4 T cells CD4 T-IFN CD4 T-naïve CD4 T-NFKB CD4 T-reg CD4 T-rm
Down 2 0 13 2 4 0
NotSig 15808 15791 15743 15800 15787 15810
Up 0 19 54 8 19 0
CD8 T-GZMK CD8 T-inflammasome CD8 T-rm gamma delta T cells
Down 1 1 1 0
NotSig 15798 15808 15801 15800
Up 11 1 8 10
innate lymphocytes NK cells NK-T cells proliferating T/NK
Down 3 4 1 3
NotSig 15802 15792 15791 15740
Up 5 14 18 67Mean-difference (MD) plots per cluster.
par(mfrow=c(4,3))
par(mar=c(2,3,1,2))
for(i in 1:ncol(mycont)){
plotMD(tr, coef = i, status = dt[,i], hl.cex = 0.5)
abline(h = 0, col = "lightgrey")
lines(lowess(tr$Amean, tr$coefficients[,i]), lwd = 1.5, col = 4)
}
limma marker gene dotplot
DefaultAssay(seuInt) <- "RNA"
contnames <- colnames(mycont)
top_markers <- NULL
n_markers <- 5
for(i in 1:ncol(mycont)){
top <- topTreat(tr, coef = i, n = Inf)
top <- top[top$logFC > 0, ]
top_markers <- c(top_markers,
setNames(rownames(top)[1:n_markers],
rep(contnames[i], n_markers)))
}
top_markers <- top_markers[!is.na(top_markers)]
d <- duplicated(top_markers)
top_markers <- top_markers[!d]
geneCols <- paletteer_d(cluster_pal)[factor(names(top_markers))]
strip <- strip_themed(background_x = elem_list_rect(fill = unique(geneCols)))
DotPlot(seuInt,
features = unname(top_markers),
group.by = "ann_level_3",
cols = c("azure1", "blueviolet"),
dot.scale = 3,
assay = "SCT") +
FontSize(x.text = 9, y.text = 9) +
labs(y = element_blank(), x = element_blank()) +
facet_grid2(~names(top_markers),
scales = "free_x",
space = "free_x",
strip = strip) +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
vjust = 0.5),
legend.text = element_text(size = 8),
legend.title = element_text(size = 9),
strip.text = element_text(size = 0),
text = element_text(family = "arial"),
axis.ticks = element_blank(),
axis.line = element_blank(),
panel.spacing = unit(2, "mm")) 
Test for marker genes using Seurat
DefaultAssay(seuInt) <- "RNA"
Idents(seuInt) <- "ann_level_3"
out <- here("data/cluster_annotations/seurat_markers_TNK_cells.rds")
if(!file.exists(out)){
# restrict genes to same set as for limma analysis
markers <- FindAllMarkers(seuInt, only.pos = TRUE,
features = rownames(logcounts))
saveRDS(markers, file = out)
} else {
markers <- readRDS(out)
}
head(markers) %>% knitr::kable()| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | cluster | gene | |
|---|---|---|---|---|---|---|---|
| MAF | 0 | 10.4131028 | 0.423 | 0.160 | 0 | CD4 T cells | MAF |
| CD2 | 0 | 12.9293179 | 0.764 | 0.663 | 0 | CD4 T cells | CD2 |
| CD28 | 0 | 0.4018968 | 0.135 | 0.048 | 0 | CD4 T cells | CD28 |
| EML4 | 0 | 3.5844246 | 0.426 | 0.284 | 0 | CD4 T cells | EML4 |
| PBXIP1 | 0 | 5.7405767 | 0.339 | 0.204 | 0 | CD4 T cells | PBXIP1 |
| TNFRSF25 | 0 | 1.1253763 | 0.233 | 0.118 | 0 | CD4 T cells | TNFRSF25 |
Seurat marker gene dotplot
DefaultAssay(seuInt) <- "RNA"
maxGenes <- 5
markers %>%
group_by(cluster) %>%
top_n(n = maxGenes, wt = avg_log2FC) -> top5
sig <- top5$gene
d <- duplicated(sig)
geneCols <- paletteer_d(cluster_pal)[top5$cluster][!d]
strip <- strip_themed(background_x = elem_list_rect(fill = unique(geneCols)))
DotPlot(seuInt,
features = sig[!d],
group.by = "ann_level_3",
cols = c("azure1", "blueviolet"),
dot.scale = 3,
assay = "SCT") +
FontSize(x.text = 9, y.text = 9) +
labs(y = element_blank(), x = element_blank()) +
facet_grid2(~top5$cluster[!d],
scales = "free_x",
space = "free_x",
strip = strip) +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
vjust = 0.5),
legend.text = element_text(size = 8),
legend.title = element_text(size = 9),
strip.text = element_text(size = 0),
text = element_text(family = "arial"),
axis.ticks = element_blank(),
axis.line = element_blank(),
panel.spacing = unit(2, "mm")) 
Visualise ADTs
Make data frame of proteins, clusters, expression levels.
out <- here("data",
"C133_Neeland_merged",
glue("C133_Neeland_full_clean{ambient}_TNK_cells_adt_dsb.SEU.rds"))
if(!file.exists(out)){
read_csv(file = here("data",
"C133_Neeland_batch1",
"data",
"sample_sheets",
"ADT_features.csv")) -> adt_data
pattern <- "anti-human/mouse |anti-human/mouse/rat |anti-mouse/human |anti-human "
adt_data$name <- gsub(pattern, "", adt_data$name)
adt <- seuInt[["ADT"]]@counts
if(all(rownames(seuInt[["ADT"]]@counts) == adt_data$id)) rownames(adt) <- adt_data$name
adt_data %>%
dplyr::filter(grepl("[Ii]sotype", name)) %>%
pull(name) -> isotype_controls
# normalise ADT using DSB normalisation
adt_dsb <- ModelNegativeADTnorm(cell_protein_matrix = adt,
denoise.counts = TRUE,
use.isotype.control = TRUE,
isotype.control.name.vec = isotype_controls)
saveRDS(adt_dsb, file = out)
} else {
adt_dsb <- readRDS(out)
}
m <- match(colnames(seuInt), colnames(adt_dsb)) # remove cells not present in Seurat obj
seuInt[["ADT.dsb"]] <- CreateAssayObject(data = adt_dsb[,m])ADTs <- read_csv(file = here("data",
"Proteins_T-NK_22.04.22.csv"))
pattern <- "anti-human/mouse |anti-human/mouse/rat |anti-mouse/human |anti-human "
ADTs$Description <- gsub(pattern, "", ADTs$Description)
DotPlot(seuInt,
features = ADTs$Description,
group.by = "ann_level_3",
cols = c("azure1", "blueviolet"),
dot.scale = 2.5,
assay = "ADT.dsb") +
FontSize(x.text = 9, y.text = 9) +
labs(y = element_blank(), x = element_blank()) +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
vjust = 0.5),
legend.text = element_text(size = 8),
legend.title = element_text(size = 9),
strip.text = element_text(size = 0),
text = element_text(family = "arial"),
axis.ticks = element_blank(),
axis.line = element_blank(),
panel.spacing = unit(2, "mm")) 
Save data
out <- here("data",
"C133_Neeland_merged",
glue("C133_Neeland_full_clean{ambient}_t_cells_annotated_diet.SEU.rds"))
if(!file.exists(out)){
DefaultAssay(seuInt) <- "RNA"
saveRDS(DietSeurat(seuInt, assays = "RNA"), out)
}
out <- here("data",
"C133_Neeland_merged",
glue("C133_Neeland_full_clean{ambient}_t_cells_annotated_full.SEU.rds"))
if(!file.exists(out)){
DefaultAssay(seuInt) <- "RNA"
saveRDS(seuInt, out)
}Session info
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] dsb_1.0.3 ggh4x_0.2.8
[3] speckle_1.2.0 org.Hs.eg.db_3.18.0
[5] AnnotationDbi_1.64.1 readxl_1.4.3
[7] tidyHeatmap_1.8.1 paletteer_1.6.0
[9] patchwork_1.2.0 glue_1.7.0
[11] here_1.0.1 dittoSeq_1.14.2
[13] SeuratObject_4.1.4 Seurat_4.4.0
[15] lubridate_1.9.3 forcats_1.0.0
[17] stringr_1.5.1 dplyr_1.1.4
[19] purrr_1.0.2 readr_2.1.5
[21] tidyr_1.3.1 tibble_3.2.1
[23] ggplot2_3.5.0 tidyverse_2.0.0
[25] edgeR_4.0.15 limma_3.58.1
[27] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
[29] Biobase_2.62.0 GenomicRanges_1.54.1
[31] GenomeInfoDb_1.38.6 IRanges_2.36.0
[33] S4Vectors_0.40.2 BiocGenerics_0.48.1
[35] MatrixGenerics_1.14.0 matrixStats_1.2.0
[37] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.3.3 later_1.3.2
[4] prismatic_1.1.1 bitops_1.0-7 cellranger_1.1.0
[7] polyclip_1.10-6 lifecycle_1.0.4 doParallel_1.0.17
[10] rprojroot_2.0.4 vroom_1.6.5 globals_0.16.2
[13] processx_3.8.3 lattice_0.22-5 MASS_7.3-60.0.1
[16] dendextend_1.17.1 magrittr_2.0.3 plotly_4.10.4
[19] sass_0.4.8 rmarkdown_2.25 jquerylib_0.1.4
[22] yaml_2.3.8 httpuv_1.6.14 sctransform_0.4.1
[25] sp_2.1-3 spatstat.sparse_3.0-3 reticulate_1.35.0
[28] DBI_1.2.1 cowplot_1.1.3 pbapply_1.7-2
[31] RColorBrewer_1.1-3 abind_1.4-5 zlibbioc_1.48.0
[34] Rtsne_0.17 RCurl_1.98-1.14 git2r_0.33.0
[37] circlize_0.4.15 GenomeInfoDbData_1.2.11 ggrepel_0.9.5
[40] irlba_2.3.5.1 listenv_0.9.1 spatstat.utils_3.0-4
[43] pheatmap_1.0.12 goftest_1.2-3 spatstat.random_3.2-2
[46] fitdistrplus_1.1-11 parallelly_1.37.0 leiden_0.4.3.1
[49] codetools_0.2-19 DelayedArray_0.28.0 shape_1.4.6
[52] tidyselect_1.2.0 farver_2.1.1 viridis_0.6.5
[55] spatstat.explore_3.2-6 jsonlite_1.8.8 GetoptLong_1.0.5
[58] ellipsis_0.3.2 progressr_0.14.0 iterators_1.0.14
[61] ggridges_0.5.6 survival_3.7-0 foreach_1.5.2
[64] tools_4.3.3 ica_1.0-3 Rcpp_1.0.12
[67] gridExtra_2.3 SparseArray_1.2.4 xfun_0.42
[70] withr_3.0.0 BiocManager_1.30.22 fastmap_1.1.1
[73] fansi_1.0.6 callr_3.7.3 digest_0.6.34
[76] timechange_0.3.0 R6_2.5.1 mime_0.12
[79] colorspace_2.1-0 scattermore_1.2 tensor_1.5
[82] RSQLite_2.3.5 spatstat.data_3.0-4 utf8_1.2.4
[85] generics_0.1.3 renv_1.0.3 data.table_1.15.0
[88] httr_1.4.7 htmlwidgets_1.6.4 S4Arrays_1.2.0
[91] whisker_0.4.1 uwot_0.1.16 pkgconfig_2.0.3
[94] gtable_0.3.4 blob_1.2.4 ComplexHeatmap_2.18.0
[97] lmtest_0.9-40 XVector_0.42.0 htmltools_0.5.7
[100] clue_0.3-65 scales_1.3.0 png_0.1-8
[103] knitr_1.45 rstudioapi_0.15.0 rjson_0.2.21
[106] tzdb_0.4.0 reshape2_1.4.4 nlme_3.1-164
[109] GlobalOptions_0.1.2 cachem_1.0.8 zoo_1.8-12
[112] KernSmooth_2.23-24 parallel_4.3.3 miniUI_0.1.1.1
[115] pillar_1.9.0 grid_4.3.3 vctrs_0.6.5
[118] RANN_2.6.1 promises_1.2.1 xtable_1.8-4
[121] cluster_2.1.6 evaluate_0.23 cli_3.6.2
[124] locfit_1.5-9.8 compiler_4.3.3 rlang_1.1.3
[127] crayon_1.5.2 future.apply_1.11.1 labeling_0.4.3
[130] mclust_6.1 rematch2_2.1.2 ps_1.7.6
[133] getPass_0.2-4 plyr_1.8.9 fs_1.6.3
[136] stringi_1.8.3 viridisLite_0.4.2 deldir_2.0-2
[139] Biostrings_2.70.2 munsell_0.5.0 lazyeval_0.2.2
[142] spatstat.geom_3.2-8 Matrix_1.6-5 hms_1.1.3
[145] bit64_4.0.5 future_1.33.1 KEGGREST_1.42.0
[148] statmod_1.5.0 shiny_1.8.0 highr_0.10
[151] ROCR_1.0-11 memoise_2.0.1 igraph_2.0.1.1
[154] bslib_0.6.1 bit_4.0.5