Last updated: 2022-12-20

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Knit directory: paed-cf-cite-seq/

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Unstaged changes:
    Modified:   data/macrophage_subcluster_annotation_16.12.22.csv

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File	Version	Author	Date	Message
Rmd	508dee7	Jovana Maksimovic	2022-12-20	wflow_publish(c("analysis/postprocess_.Rmd"))
html	16ace9e	Jovana Maksimovic	2022-12-19	Build site.
Rmd	e799f52	Jovana Maksimovic	2022-12-19	wflow_publish(c("analysis/emptyDrops.Rmd", "analysis/postprocess*.Rmd",
html	63f8ee8	Jovana Maksimovic	2022-12-15	Build site.
Rmd	916bafa	Jovana Maksimovic	2022-12-15	wflow_publish(c("analysis/.emptyDrops.Rmd", "analysis/postprocess_*.Rmd",
Rmd	f3b7b92	Jovana Maksimovic	2022-06-16	Submission version
html	f3b7b92	Jovana Maksimovic	2022-06-16	Submission version

1 Load libraries

2 Load Data

out <- here("data/SCEs/06_COMBO.tcells_clustered.SEU.rds")
seuInt <- readRDS(file = out)

seuInt

An object of class Seurat 
31168 features across 6462 samples within 3 assays 
Active assay: integrated (3000 features, 3000 variable features)
 2 other assays present: RNA, SCT
 2 dimensional reductions calculated: pca, umap

3 Sub-cluster annotation

3.1 Load manual annotations

labels <- read_csv(here("data/TNK_subcluster_annotation_29.05.22.csv"))

seuInt@meta.data %>%
  left_join(labels %>%
              mutate(Annotation = ifelse(is.na(Annotation),
                                         "SUSPECT",
                                         Annotation),
                     Broad = ifelse(is.na(Broad),
                                         "SUSPECT",
                                         Broad)) %>%
              mutate(Cluster = as.factor(Cluster),
                     Annotation = as.factor(Annotation)),
            by = c("integrated_snn_res.1" = "Cluster")) -> ann 

ann %>% dplyr::pull(Annotation) -> seuInt$Annotation
ann %>% dplyr::pull(Broad) -> seuInt$Broad

seuInt$Annotation <- fct_drop(seuInt$Annotation)
seuInt$Broad <- fct_drop(seuInt$Broad)
seuInt

An object of class Seurat 
31168 features across 6462 samples within 3 assays 
Active assay: integrated (3000 features, 3000 variable features)
 2 other assays present: RNA, SCT
 2 dimensional reductions calculated: pca, umap

3.2 Check for doublet enrichment

We have already removed a total of 3826 heterogenic, cross-sample doublets based on vireo and hashedDrops calls. However, those methods cannot detect heterotypic and homotypic within-sample doublets. We have also run scds and scDblFinder to detect putative within-sample doublets.

Load doublet detection results and match up with annotated cells.

e1Doublets <- readRDS(here("data/SCEs/experiment1_doublets.rds"))
e1Doublets$cell <- paste0("A-", e1Doublets$cell)
e2Doublets <- readRDS(here("data/SCEs/experiment2_doublets.rds"))
e2Doublets$cell <- paste0("B-", e2Doublets$cell)

doublets <- rbind(e1Doublets, e2Doublets)
m <- match(colnames(seuInt), doublets$cell)
doublets <- doublets[m,]
all(doublets$cell == colnames(seuInt))

[1] TRUE

No clusters are comprised of >10% putative doublets.

table(doublets$scDblFinder.class == "doublet" & doublets$hybrid_call, 
      seuInt$Annotation) %>%
  data.frame %>% 
  group_by(Var2) %>%
  mutate(prop = Freq/sum(Freq)) %>%
  ungroup() %>%
  ggplot(aes(x = Var2, y = prop, fill = Var1)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  geom_hline(yintercept = 0.1, linetype = "dashed") +
  labs(fill = "Doublet",
       x = "Fine annotation",
       y = "Proportion") -> p1

table(doublets$scDblFinder.class == "doublet" & doublets$hybrid_call, 
      seuInt$Annotation) %>%
  data.frame %>% 
  ggplot(aes(x = Var2, y = Freq, fill = Var1)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  labs(fill = "Doublet",
       x = "Fine annotation",
       y = "Frequency") -> p2

(p2 | p1) + plot_layout(guides = "collect") &
  theme(legend.position = "bottom")

Version	Author	Date
16ace9e	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

Calculate if doublets are statistically over-represented in any clusters using Fisher’s Exact Test. Only the SUSPECT cluster and the proliferating NK/T cells clusters are significantly over-represented for putative doublets. As the SUSPECT cluster will be filtered out and doublet detection tools are known to have issues with correctly calling doublets in proliferating cell types (Neavin et al. 2022), there will be no further cell filtering.

tab <- table(doublets$scDblFinder.class == "doublet" & doublets$hybrid_call, 
      seuInt$Annotation)
dblStats <- table(doublets$scDblFinder.class == "doublet" & doublets$hybrid_call)

apply(tab, 2, function(x){
  dblFreq <- matrix(c(x[2], dblStats[2] - x[2], x[1], dblStats[1] - x[1]), 
                      nrow = 2,
                  dimnames = list(c("In cluster", "Not in cluster"),
                                  c("Doublet", "Singlet")))
  fisher.test(dblFreq, alternative = "greater")$p.value
}) -> pvals

pvals %>% 
  data.frame %>%
  rownames_to_column(var = "cell") %>%
  dplyr::rename("p.value" = ".") %>%
  mutate(FDR = p.adjust(p.value, method = "BH")) %>%
  ggplot(aes(y = -log10(FDR), x = cell,
             fill = FDR < 0.05)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  geom_col()

Version	Author	Date
16ace9e	Jovana Maksimovic	2022-12-19

3.3 Visulalise annotations

options(ggrepel.max.overlaps = Inf)
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE, 
        label.size = 2.5, group.by = "integrated_snn_res.1") + 
  NoLegend() -> p1
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE, 
        label.size = 2.5, group.by = "Annotation") + 
  NoLegend() +
  scale_color_paletteer_d("miscpalettes::pastel") -> p2
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE, 
        label.size = 2.5, group.by = "Broad") + 
  NoLegend() +
  scale_color_paletteer_d("miscpalettes::pastel") -> p3

(p1 | p2 | p3) & theme(text = element_text(size = 8),
                  axis.text = element_text(size = 8))

Version	Author	Date
16ace9e	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

3.3.1 No. cells per cluster

seuInt@meta.data %>%
  ggplot(aes(x = Annotation, fill = Annotation)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count",
            vjust = -0.5, colour = "black", size = 2) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  NoLegend() +
  scale_fill_paletteer_d("miscpalettes::pastel")

Version	Author	Date
16ace9e	Jovana Maksimovic	2022-12-19

seuInt@meta.data %>%
  ggplot(aes(x = Broad, fill = Broad)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count",
            vjust = -0.5, colour = "black", size = 2) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  NoLegend() +
  scale_fill_paletteer_d("miscpalettes::pastel")

Version	Author	Date
16ace9e	Jovana Maksimovic	2022-12-19

3.3.2 Remove SUSPECT clusters

seuInt <- subset(seuInt, cells = which(seuInt$Annotation != "SUSPECT")) 
seuInt$Annotation <- fct_drop(seuInt$Annotation)
seuInt$Broad <- fct_drop(seuInt$Broad)

DefaultAssay(seuInt) <- "integrated"
seuInt <- RunPCA(seuInt, verbose = FALSE, dims = 1:30) %>%
  RunUMAP(verbose = FALSE, dims = 1:30)

seuInt@meta.data %>% 
  count(Annotation) %>% 
  mutate(perc = round(n/sum(n)*100,1)) %>%
  dplyr::rename(`Cell Label` = "Annotation", 
                `No. Cells` = n,
                `% Cells` = perc) %>%
  knitr::kable()

Cell Label	No. Cells	% Cells
CD4 IFN	145	2.5
CD4 NFKB	155	2.6
CD4 T cells	2080	35.3
CD4 TFH	34	0.6
CD4 Treg	197	3.3
CD8 Trm	1339	22.7
CD8-GZMK	349	5.9
gammadelta T cells	174	3.0
innate lymphocyte	662	11.2
NK cells	346	5.9
NK T cells	328	5.6
proliferating NK/T cells	80	1.4

3.4 Visualise annotations

DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = FALSE, 
        label.size = 3, group.by = "Annotation") + 
  NoLegend() +
  scale_color_paletteer_d("miscpalettes::pastel") +
  xlim(c(-6, 8)) -> p2

DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = FALSE, 
        label.size = 3, group.by = "Broad") + 
  NoLegend() +
  scale_color_paletteer_d("miscpalettes::pastel") -> p1

(p1) & theme(text = element_text(size = 8),
                  axis.text = element_text(size = 8))

(p2) & theme(text = element_text(size = 8),
                  axis.text = element_text(size = 8)) -> f3a
f3a

3.5 `Cepo` cluster marker genes

cepoMarkers <- Cepo(seuInt[["RNA"]]@data, 
                   seuInt$Annotation, 
                   exprsPct = 0.1,
                   logfc = 1)

sapply(1:ncol(cepoMarkers$stats), function(i){
  names(sort(cepoMarkers$stats[,i], decreasing = TRUE))[1:20]
}) -> dat

colnames(dat) <- colnames(cepoMarkers$stats)
dat %>% knitr::kable()

CD4.IFN	CD4.NFKB	CD4.T.cells	CD4.TFH	CD4.Treg	CD8.Trm	CD8.GZMK	gammadelta.T.cells	innate.lymphocyte	NK.cells	NK.T.cells	proliferating.NK.T.cells
IFIT1	IL4I1	CD4	DRAIC	IL2RA	CD8B	GZMK	KLRG1	LEF1	KLRF1	KIR2DL4	TYMS
IFI44L	TNFRSF4	ADAM19	PVALB	CTLA4	CD8A	GZMH	TRDC	MAL	TRDC	KLRC1	MKI67
RSAD2	TNFRSF18	CTSH	POU2AF1	LINC01943	LINC02446	CD8B	GZMK	ZNF683	KRT81	KLRC2	PCLAF
HERC5	IL2RA	IL7R	SCGB3A1	TBC1D4	ZNF683	CD8A	TRGC1	TRDC	XCL1	ITGA1	NUSAP1
OAS1	NFKB2	MAF	ICA1	LAIR2	GZMH	CCL4	ZBTB16	XCL1	NCAM1	ZNF683	RRM2
MX2	RELB	LIME1	GNG4	IL1R2	ITGA1	GZMA	CEBPD	NCR3	XCL2	DAPK2	CDT1
IFIT3	TNFRSF25	LINC01943	IL6R	TNFRSF4	KLRC1	GZMB	SLC4A10	SPINT2	LINC00996	GNLY	ZWINT
CMPK2	MIR155HG	GPR183	CXCR5	FANK1	CCL4	KLRG1	CCL4	KLRC3	KLRC1	CD160	CDK1
OAS3	CD82	ANKRD28	PTPN14	IL1R1	KLRD1	NKG7	GZMA	KLRC2	TXK	LINC02446	ASF1B
MX1	CCR7	PAG1	FZD3	LAYN	KLRC2	LAG3	KLRB1	LINC02446	KRT86	NMUR1	CLSPN
USP18	ZC3H12D	KLRB1	ST8SIA1	F5	IFNG	SAMD3	KLRC1	RTKN2	ITGAX	CSF1	CENPW
IFI44	CSF2	NME2	TOX2	CD4	TRGC2	CD27	CCL3	TCF7	GNLY	SPRY2	TOP2A
DDX60	FURIN	EEF1G	CHI3L2	ICA1	GZMA	AOAH	PRF1	KLF2	PLAC8	CCL4L2	UBE2C
IFIT2	BIRC3	RFLNB	NFIA	MAF	HOPX	ITGA1	NKG7	TRGC2	SPINK2	CLNK	TPX2
DDX58	CCL20	CISH	PDCD1	RTKN2	GZMB	CST7	MATK	PLAC8	CXXC5	GZMB	CDCA5
LGALS9	LINC01943	CXCR6	PASK	TIGIT	XCL2	CCL5	CTSW	IFITM3	PTGDR	CCL3L1	ASPM
HELZ2	MAF	GZMA	ASCL2	TNFRSF18	NKG7	PRF1	NCR3	IL7R	LAT2	CD8B	BIRC5
SAMD9L	NME1	PRDM1	IL6ST	GADD45A	CTSW	TIGIT	LAG3	TXK	FCER1G	IFNG	TK1
EIF2AK2	DNPH1	CD6	ITGB8	ZC2HC1A	LAG3	LYAR	CCL4L2	IKZF2	TYROBP	SRGAP3	CENPM
XAF1	CREM	FKBP11	CDK5R1	PRDM1	CCL5	CCL4L2	SAMD3	CXXC5	AREG	ENTPD1	KIFC1

3.5.1 `Cepo` marker gene dot plot

Genes duplicated between clusters are excluded.

DefaultAssay(seuInt) <- "RNA"

maxGenes <- 5
sigGenes <- lapply(1:ncol(dat), function(i){
  dat[,i][1:maxGenes]
})
sig <- unlist(sigGenes)
geneCols <- c(rep(rep(c("blue","black"), each = maxGenes), 
                  ceiling(ncol(dat)/2)))[1:length(sig)][!duplicated(sig)] 

geneCols <- rep(paletteer_d("miscpalettes::pastel", ncol(dat)), 
                each = maxGenes)[1:length(sig)][!duplicated(sig)] 

pal <- paletteer::paletteer_d("vapoRwave::cool")
DotPlot(seuInt,    
        features = sig[!duplicated(sig)], 
        group.by = "Annotation",
        dot.scale = 2.5) + 
  FontSize(y.text = 10, x.text = 9) + 
  labs(y = element_blank(), x = element_blank()) + 
  theme(axis.text.x = element_text(color = geneCols,
                                   angle = 90,
                                   hjust = 1,
                                   vjust = 0.5,
                                   face = "bold"),
        legend.text = element_text(size = 8),
        legend.title = element_text(size = 10)) +
  scale_color_gradient2(low = pal[1], 
                        mid = pal[3], 
                        high = pal[5]) -> f3c
f3c

Version	Author	Date
16ace9e	Jovana Maksimovic	2022-12-19

3.6 Visualise cytokines of interest

markers <- read_csv(file = here("data",
                                "T-NK_subclusters_cytokines.csv"),
                    col_names = FALSE)
p <- DotPlot(seuInt,
             features = markers$X1,
             cols = c("grey", "red"),
             dot.scale = 5,
             assay = "RNA",
             group.by = "Annotation") +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   vjust = 0.5,
                                   size = 8),
        axis.text.y = element_text(size = 8),
        text = element_text(size = 8)) +
  coord_flip() +
  labs(y = "Label", x = "Cytokine")

p

Version	Author	Date
16ace9e	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

4 Load protein data

4.1 Add to `Seurat` object

seuAdt <- readRDS(here("data",
                       "SCEs",
                       "04_COMBO.clustered_annotated_adt_diet.SEU.rds"))
seuAdt <- subset(seuAdt, cells = colnames(seuInt))
all(colnames(seuAdt) == colnames(seuInt))

[1] TRUE

seuInt[["ADT.dsb"]] <- seuAdt[["ADT.dsb"]]
seuInt[["ADT.raw"]] <- seuAdt[["ADT.raw"]]
seuInt

An object of class Seurat 
31490 features across 5889 samples within 5 assays 
Active assay: RNA (16001 features, 0 variable features)
 4 other assays present: SCT, integrated, ADT.dsb, ADT.raw
 2 dimensional reductions calculated: pca, umap

rm(seuAdt)
gc()

            used   (Mb) gc trigger   (Mb)  max used   (Mb)
Ncells   9901811  528.9   14637655  781.8  14637655  781.8
Vcells 251164227 1916.3  683378461 5213.8 807636520 6161.8

4.2 Load protein annotations

prots <- read_csv(file = here("data",
                              "sample_sheets",
                              "TotalSeq-A_Universal_Cocktail_v1.0.csv")) %>%
  dplyr::filter(grepl("^A0", id)) %>%
  dplyr::filter(!grepl("[Ii]sotype", name))

4.3 Visualise all ADTs

Normalised with DSB. C133_Neeland ADT data was transferred to CF_BAL_Pilot using reference mapping and transfer.

cbind(seuInt@meta.data, 
      as.data.frame(t(seuInt@assays$ADT.dsb@data))) %>% 
  dplyr::group_by(Annotation, experiment) %>% 
  dplyr::summarize_at(.vars = prots$id, .funs = median) %>%
  pivot_longer(c(-Annotation, -experiment), names_to = "ADT",
               values_to = "ADT Exp.")  %>%
  left_join(prots, by = c("ADT" = "id")) %>%
  mutate(`Cell Label` = Annotation) %>%
  dplyr::rename(Protein = name) |> 
  dplyr::filter(experiment == 2) |>
  ungroup() -> dat

plot(density(dat$`ADT Exp.`))
topMax <- 8
abline(v = topMax, lty = 2, col = "grey")

Version	Author	Date
16ace9e	Jovana Maksimovic	2022-12-19

dat |> heatmap(
  .column = `Cell Label`,
  .row = Protein,
  .value = `ADT Exp.`,
  scale = "none",
  rect_gp = grid::gpar(col = "white", lwd = 1),
  show_row_names = TRUE,
  column_names_gp = grid::gpar(fontsize = 10),
  column_title_gp = grid::gpar(fontsize = 12),
  row_names_gp = grid::gpar(fontsize = 8),
  row_title_gp = grid::gpar(fontsize = 12),
  column_title_side = "top",
    palette_value = circlize::colorRamp2(seq(-1, topMax, length.out = 256),
                                         viridis::magma(256)),
  heatmap_legend_param = list(direction = "vertical"))

Version	Author	Date
16ace9e	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

4.4 Visualise ADTs of interest

adt <- read_csv(file = here("data/Proteins_T-NK_22.04.22.csv"))
adt <- adt[!duplicated(adt$DNA_ID),]
  
dat |>
  dplyr::inner_join(adt, by = c("ADT" = "DNA_ID")) |>
  mutate(Protein = `Name for heatmap`) |>
  heatmap(
    .column = Protein,
    .row = `Cell Label`,
    .value = `ADT Exp.`,
    scale = "none",
    rect_gp = grid::gpar(col = "white", lwd = 1),
    show_row_names = TRUE,
    column_names_gp = grid::gpar(fontsize = 10),
    column_title_gp = grid::gpar(fontsize = 12),
    row_names_gp = grid::gpar(fontsize = 10),
    row_title_gp = grid::gpar(fontsize = 12),
    column_title_side = "bottom",
    heatmap_legend_param = list(direction = "vertical"),
    palette_value = circlize::colorRamp2(seq(-1, topMax, length.out = 256),
                                         viridis::magma(256)),
    column_title_side = "bottom") |>
  add_tile(`Cell Label`, show_legend = FALSE,
           show_annotation_name = FALSE,
           palette = paletteer_d("miscpalettes::pastel", 
                                 length(levels(seuInt$Annotation)))) -> f3d

wrap_heatmap(f3d)

Version	Author	Date
63f8ee8	Jovana Maksimovic	2022-12-15
f3b7b92	Jovana Maksimovic	2022-06-16

5 Proportions analysis

5.1 Load clinical information

Import clinical characteristics and patient information and associate with genetic_donor IDs.

info <- read.csv(file = here("data/sample_sheets/Sample_information.csv"))
tab <- table(seuInt$HTO, seuInt$donor)
tab <- tab[rownames(tab) != "Unknown",]

apply(tab, 2, function(x){
  names(which(x == max(x)))
}) %>% data.frame %>%
  dplyr::rename("HTO" = ".") %>%
  rownames_to_column(var = "donor") %>%
  inner_join(info, by = c("HTO" = "Sample")) %>%
  mutate(Batch = factor(Batch)) -> info

info %>% knitr::kable()

donor	HTO	Participant	Sex	Age	Disease	Batch
A	A	B1_CF	M	2.99	CF	1
B	B	C1_CF	M	2.99	CF	1
C	C	A1_Ctrl	M	3.00	Ctrl	1
D	D	D1_CF	M	3.03	CF	1
donor_A	Human_HTO_8	L2_CF	M	5.95	CF	2
donor_B	Human_HTO_1	E2_CF	F	5.99	CF	2
donor_C	Human_HTO_4	H2_CF	F	5.89	CF	2
donor_D	Human_HTO_6	J2_CF	M	5.05	CF	2
donor_E	Human_HTO_3	G2_CF	F	4.91	CF	2
donor_F	Human_HTO_5	I2_CF	F	5.93	CF	2
donor_G	Human_HTO_2	F2_CF	F	6.02	CF	2
donor_H	Human_HTO_7	K2_CF	M	4.92	CF	2

5.2 Sub-cluster proportions (Broad)

# Differences in cell type proportions
props <- getTransformedProps(clusters = seuInt$Broad,
                             sample = seuInt$donor, transform="asin")
props$Proportions %>% knitr::kable()

	A	B	C	D	donor_A	donor_B	donor_C	donor_D	donor_E	donor_F	donor_G	donor_H
CD4 T	0.4761905	0.4452174	0.3103721	0.3611442	0.6331570	0.4000000	0.5053763	0.4307692	0.5000000	0.5744681	0.5216908	0.4245283
CD8 T	0.2329472	0.1495652	0.4726841	0.2586412	0.2345679	0.3172414	0.3225806	0.3307692	0.2786885	0.3297872	0.1846496	0.3207547
Gamma delta T	0.0592021	0.0278261	0.0126683	0.0226460	0.0035273	0.0000000	0.0322581	0.0000000	0.0000000	0.0106383	0.0667408	0.0188679
Innate lymphocyte	0.0772201	0.0991304	0.0815519	0.2526818	0.0476190	0.0482759	0.0645161	0.2000000	0.1393443	0.0106383	0.1279199	0.1226415
NK cells	0.0978121	0.0921739	0.0522565	0.0381406	0.0370370	0.0620690	0.0430108	0.0153846	0.0737705	0.0531915	0.0611791	0.0188679
NK T	0.0463320	0.1773913	0.0633413	0.0262217	0.0405644	0.1724138	0.0295699	0.0230769	0.0000000	0.0106383	0.0177976	0.0849057
Proliferating NK/T	0.0102960	0.0086957	0.0071259	0.0405244	0.0035273	0.0000000	0.0026882	0.0000000	0.0081967	0.0106383	0.0200222	0.0094340

5.2.1 Cell proportions by donor

props$Proportions %>%
  data.frame %>%
  inner_join(info, by = c("sample" = "donor")) %>%
ggplot(aes(x = Participant, y = Freq, fill = clusters)) +
  geom_bar(stat = "identity") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90,
                                   vjust = 0.5,
                                   hjust = 1),
        legend.text = element_text(size = 8)) +
  labs( y = "Proportion", fill = "Cell label") +
  scale_fill_paletteer_d("miscpalettes::pastel")

Version	Author	Date
16ace9e	Jovana Maksimovic	2022-12-19
63f8ee8	Jovana Maksimovic	2022-12-15
f3b7b92	Jovana Maksimovic	2022-06-16

5.3 Sub-cluster proportions (Fine)

# Differences in cell type proportions
props <- getTransformedProps(clusters = seuInt$Annotation,
                             sample = seuInt$donor, transform="asin")
props$Proportions %>% knitr::kable()

	A	B	C	D	donor_A	donor_B	donor_C	donor_D	donor_E	donor_F	donor_G	donor_H
CD4 IFN	0.0154440	0.0400000	0.0253365	0.0321812	0.0246914	0.0000000	0.0241935	0.0153846	0.0163934	0.0319149	0.0211346	0.0188679
CD4 NFKB	0.0411840	0.0521739	0.0110847	0.0202622	0.0582011	0.0620690	0.0188172	0.0153846	0.0245902	0.0319149	0.0044494	0.0094340
CD4 T cells	0.3191763	0.3373913	0.2549485	0.2836710	0.4991182	0.3310345	0.4247312	0.3615385	0.3934426	0.4680851	0.4560623	0.3773585
CD4 TFH	0.0437580	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000
CD4 Treg	0.0566281	0.0156522	0.0190024	0.0250298	0.0511464	0.0068966	0.0376344	0.0384615	0.0655738	0.0425532	0.0400445	0.0188679
CD8 Trm	0.1042471	0.1252174	0.4148852	0.2288439	0.1975309	0.2482759	0.2688172	0.2615385	0.1639344	0.3297872	0.1190211	0.2830189
CD8-GZMK	0.1287001	0.0243478	0.0577989	0.0297974	0.0370370	0.0689655	0.0537634	0.0692308	0.1147541	0.0000000	0.0656285	0.0377358
gammadelta T cells	0.0592021	0.0278261	0.0126683	0.0226460	0.0035273	0.0000000	0.0322581	0.0000000	0.0000000	0.0106383	0.0667408	0.0188679
innate lymphocyte	0.0772201	0.0991304	0.0815519	0.2526818	0.0476190	0.0482759	0.0645161	0.2000000	0.1393443	0.0106383	0.1279199	0.1226415
NK cells	0.0978121	0.0921739	0.0522565	0.0381406	0.0370370	0.0620690	0.0430108	0.0153846	0.0737705	0.0531915	0.0611791	0.0188679
NK T cells	0.0463320	0.1773913	0.0633413	0.0262217	0.0405644	0.1724138	0.0295699	0.0230769	0.0000000	0.0106383	0.0177976	0.0849057
proliferating NK/T cells	0.0102960	0.0086957	0.0071259	0.0405244	0.0035273	0.0000000	0.0026882	0.0000000	0.0081967	0.0106383	0.0200222	0.0094340

5.3.1 Cell proportions by donor

props$Proportions %>%
  data.frame %>%
  inner_join(info, by = c("sample" = "donor")) %>%
ggplot(aes(x = Participant, y = Freq, fill = clusters)) +
  geom_bar(stat = "identity") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90,
                                   vjust = 0.5,
                                   hjust = 1),
        legend.text = element_text(size = 8)) +
  labs(y = "Proportion", fill = "Cell label") +
  scale_fill_paletteer_d("miscpalettes::pastel") -> f3b
f3b

Version	Author	Date
63f8ee8	Jovana Maksimovic	2022-12-15
f3b7b92	Jovana Maksimovic	2022-06-16

6 Save data

out <- here(glue("data/SCEs/06_COMBO.clean_tcells_diet.SEU.rds"))
if(!file.exists(out)){
  DefaultAssay(seuInt) <- "RNA"
  saveRDS(DietSeurat(seuInt,
                     assays = c("RNA", "ADT.dsb", "ADT.raw"),
                     dimreducs = NULL,
                     graphs = NULL), out)

}

7 Panel figures

layout = "AAAB
          AAAB
          AAAB
          CCCC
          CCCC
          DDDD
          DDDD
          DDDD"
((f3a + ggtitle("")) + 
   f3b + 
   f3c +
   wrap_heatmap(f3d)) + 
  plot_layout(design = layout) +
  plot_annotation(tag_levels = "A") &
  theme(plot.tag = element_text(size = 14, face = "bold"))

8 Session info

The analysis and this document were prepared using the following software (click triangle to expand)

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.1.0 (2021-05-18)
 os       CentOS Linux 7 (Core)
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_AU.UTF-8
 ctype    en_AU.UTF-8
 tz       Australia/Melbourne
 date     2022-12-20
 pandoc   2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 ! package              * version   date (UTC) lib source
 P abind                  1.4-5     2016-07-21 [?] CRAN (R 4.1.0)
 P annotate             * 1.72.0    2021-10-26 [?] Bioconductor
 P AnnotationDbi        * 1.56.2    2021-11-09 [?] Bioconductor
 P assertthat             0.2.1     2019-03-21 [?] CRAN (R 4.1.0)
 P backports              1.4.1     2021-12-13 [?] CRAN (R 4.1.0)
 P beachmat               2.10.0    2021-10-26 [?] Bioconductor
 P Biobase              * 2.54.0    2021-10-26 [?] Bioconductor
 P BiocGenerics         * 0.40.0    2021-10-26 [?] Bioconductor
 P BiocManager            1.30.16   2021-06-15 [?] CRAN (R 4.1.0)
 P BiocParallel           1.28.3    2021-12-09 [?] Bioconductor
 P BiocStyle            * 2.22.0    2021-10-26 [?] Bioconductor
 P Biostrings             2.62.0    2021-10-26 [?] Bioconductor
 P bit                    4.0.4     2020-08-04 [?] CRAN (R 4.1.0)
 P bit64                  4.0.5     2020-08-30 [?] CRAN (R 4.0.2)
 P bitops                 1.0-7     2021-04-24 [?] CRAN (R 4.0.2)
 P blob                   1.2.2     2021-07-23 [?] CRAN (R 4.1.0)
 P bookdown               0.24      2021-09-02 [?] CRAN (R 4.1.0)
 P broom                  0.7.11    2022-01-03 [?] CRAN (R 4.1.0)
 P bslib                  0.3.1     2021-10-06 [?] CRAN (R 4.1.0)
 P cachem                 1.0.6     2021-08-19 [?] CRAN (R 4.1.0)
 P callr                  3.7.0     2021-04-20 [?] CRAN (R 4.1.0)
 P cellranger             1.1.0     2016-07-27 [?] CRAN (R 4.1.0)
 P Cepo                 * 1.0.0     2021-10-26 [?] Bioconductor
 P circlize               0.4.13    2021-06-09 [?] CRAN (R 4.1.0)
 P cli                    3.1.0     2021-10-27 [?] CRAN (R 4.1.0)
 P clue                   0.3-60    2021-10-11 [?] CRAN (R 4.1.0)
 P cluster                2.1.2     2021-04-17 [?] CRAN (R 4.1.0)
 P codetools              0.2-18    2020-11-04 [?] CRAN (R 4.1.0)
 P colorspace             2.0-2     2021-06-24 [?] CRAN (R 4.0.2)
 P ComplexHeatmap         2.10.0    2021-10-26 [?] Bioconductor
 P cowplot                1.1.1     2020-12-30 [?] CRAN (R 4.0.2)
 P crayon                 1.4.2     2021-10-29 [?] CRAN (R 4.1.0)
 P data.table             1.14.2    2021-09-27 [?] CRAN (R 4.1.0)
 P DBI                    1.1.2     2021-12-20 [?] CRAN (R 4.1.0)
 P dbplyr                 2.1.1     2021-04-06 [?] CRAN (R 4.1.0)
 P DelayedArray           0.20.0    2021-10-26 [?] Bioconductor
 P DelayedMatrixStats     1.16.0    2021-10-26 [?] Bioconductor
 P deldir                 1.0-6     2021-10-23 [?] CRAN (R 4.1.0)
 P dendextend             1.15.2    2021-10-28 [?] CRAN (R 4.1.0)
 P digest                 0.6.29    2021-12-01 [?] CRAN (R 4.1.0)
 P doParallel             1.0.16    2020-10-16 [?] CRAN (R 4.0.2)
 P dplyr                * 1.0.7     2021-06-18 [?] CRAN (R 4.1.0)
 P edgeR                  3.36.0    2021-10-26 [?] Bioconductor
 P ellipsis               0.3.2     2021-04-29 [?] CRAN (R 4.0.2)
 P evaluate               0.14      2019-05-28 [?] CRAN (R 4.0.2)
 P fansi                  1.0.0     2022-01-10 [?] CRAN (R 4.1.0)
 P farver                 2.1.0     2021-02-28 [?] CRAN (R 4.0.2)
 P fastmap                1.1.0     2021-01-25 [?] CRAN (R 4.1.0)
 P fitdistrplus           1.1-6     2021-09-28 [?] CRAN (R 4.1.0)
 P forcats              * 0.5.1     2021-01-27 [?] CRAN (R 4.1.0)
 P foreach                1.5.1     2020-10-15 [?] CRAN (R 4.0.2)
 P fs                     1.5.2     2021-12-08 [?] CRAN (R 4.1.0)
 P future                 1.23.0    2021-10-31 [?] CRAN (R 4.1.0)
 P future.apply           1.8.1     2021-08-10 [?] CRAN (R 4.1.0)
 P generics               0.1.1     2021-10-25 [?] CRAN (R 4.1.0)
   GenomeInfoDb           1.30.1    2022-01-30 [1] Bioconductor
 P GenomeInfoDbData       1.2.7     2021-12-21 [?] Bioconductor
 P GenomicRanges          1.46.1    2021-11-18 [?] Bioconductor
 P GetoptLong             1.0.5     2020-12-15 [?] CRAN (R 4.0.2)
 P getPass                0.2-2     2017-07-21 [?] CRAN (R 4.0.2)
 P ggplot2              * 3.3.5     2021-06-25 [?] CRAN (R 4.0.2)
 P ggrepel                0.9.1     2021-01-15 [?] CRAN (R 4.1.0)
 P ggridges               0.5.3     2021-01-08 [?] CRAN (R 4.1.0)
 P git2r                  0.29.0    2021-11-22 [?] CRAN (R 4.1.0)
 P GlobalOptions          0.1.2     2020-06-10 [?] CRAN (R 4.1.0)
 P globals                0.14.0    2020-11-22 [?] CRAN (R 4.0.2)
 P glue                 * 1.6.0     2021-12-17 [?] CRAN (R 4.1.0)
 P goftest                1.2-3     2021-10-07 [?] CRAN (R 4.1.0)
 P graph                * 1.72.0    2021-10-26 [?] Bioconductor
 P gridExtra              2.3       2017-09-09 [?] CRAN (R 4.1.0)
 P GSEABase             * 1.56.0    2021-10-26 [?] Bioconductor
 P gtable                 0.3.0     2019-03-25 [?] CRAN (R 4.1.0)
 P haven                  2.4.3     2021-08-04 [?] CRAN (R 4.1.0)
 P HDF5Array              1.22.1    2021-11-14 [?] Bioconductor
 P here                 * 1.0.1     2020-12-13 [?] CRAN (R 4.0.2)
 P highr                  0.9       2021-04-16 [?] CRAN (R 4.1.0)
 P hms                    1.1.1     2021-09-26 [?] CRAN (R 4.1.0)
 P htmltools              0.5.2     2021-08-25 [?] CRAN (R 4.1.0)
 P htmlwidgets            1.5.4     2021-09-08 [?] CRAN (R 4.1.0)
 P httpuv                 1.6.5     2022-01-05 [?] CRAN (R 4.1.0)
 P httr                   1.4.2     2020-07-20 [?] CRAN (R 4.1.0)
 P ica                    1.0-2     2018-05-24 [?] CRAN (R 4.1.0)
 P igraph                 1.2.11    2022-01-04 [?] CRAN (R 4.1.0)
 P IRanges              * 2.28.0    2021-10-26 [?] Bioconductor
 P irlba                  2.3.5     2021-12-06 [?] CRAN (R 4.1.0)
 P iterators              1.0.13    2020-10-15 [?] CRAN (R 4.0.2)
 P jquerylib              0.1.4     2021-04-26 [?] CRAN (R 4.1.0)
 P jsonlite               1.7.2     2020-12-09 [?] CRAN (R 4.0.2)
 P KEGGREST               1.34.0    2021-10-26 [?] Bioconductor
 P KernSmooth             2.23-20   2021-05-03 [?] CRAN (R 4.1.0)
 P knitr                  1.37      2021-12-16 [?] CRAN (R 4.1.0)
 P labeling               0.4.2     2020-10-20 [?] CRAN (R 4.0.2)
 P later                  1.3.0     2021-08-18 [?] CRAN (R 4.1.0)
 P lattice                0.20-45   2021-09-22 [?] CRAN (R 4.1.0)
 P lazyeval               0.2.2     2019-03-15 [?] CRAN (R 4.1.0)
 P leiden                 0.3.9     2021-07-27 [?] CRAN (R 4.1.0)
 P lifecycle              1.0.1     2021-09-24 [?] CRAN (R 4.1.0)
 P limma                  3.50.0    2021-10-26 [?] Bioconductor
 P listenv                0.8.0     2019-12-05 [?] CRAN (R 4.1.0)
 P lmtest                 0.9-39    2021-11-07 [?] CRAN (R 4.1.0)
 P locfit                 1.5-9.4   2020-03-25 [?] CRAN (R 4.1.0)
 P lubridate              1.8.0     2021-10-07 [?] CRAN (R 4.1.0)
 P magrittr               2.0.1     2020-11-17 [?] CRAN (R 4.0.2)
 P MASS                   7.3-53.1  2021-02-12 [?] CRAN (R 4.0.2)
 P Matrix                 1.4-0     2021-12-08 [?] CRAN (R 4.1.0)
 P MatrixGenerics         1.6.0     2021-10-26 [?] Bioconductor
 P matrixStats            0.61.0    2021-09-17 [?] CRAN (R 4.1.0)
 P memoise                2.0.1     2021-11-26 [?] CRAN (R 4.1.0)
 P mgcv                   1.8-38    2021-10-06 [?] CRAN (R 4.1.0)
 P mime                   0.12      2021-09-28 [?] CRAN (R 4.1.0)
 P miniUI                 0.1.1.1   2018-05-18 [?] CRAN (R 4.1.0)
 P modelr                 0.1.8     2020-05-19 [?] CRAN (R 4.0.2)
 P munsell                0.5.0     2018-06-12 [?] CRAN (R 4.1.0)
 P nlme                   3.1-153   2021-09-07 [?] CRAN (R 4.1.0)
 P org.Hs.eg.db           3.14.0    2021-12-21 [?] Bioconductor
 P org.Mm.eg.db           3.14.0    2022-01-24 [?] Bioconductor
 P paletteer            * 1.4.0     2021-07-20 [?] CRAN (R 4.1.0)
 P parallelly             1.30.0    2021-12-17 [?] CRAN (R 4.1.0)
 P patchwork            * 1.1.1     2020-12-17 [?] CRAN (R 4.0.2)
 P pbapply                1.5-0     2021-09-16 [?] CRAN (R 4.1.0)
 P pillar                 1.6.4     2021-10-18 [?] CRAN (R 4.1.0)
 P pkgconfig              2.0.3     2019-09-22 [?] CRAN (R 4.1.0)
 P plotly                 4.10.0    2021-10-09 [?] CRAN (R 4.1.0)
 P plyr                   1.8.6     2020-03-03 [?] CRAN (R 4.0.2)
 P png                    0.1-7     2013-12-03 [?] CRAN (R 4.1.0)
 P polyclip               1.10-0    2019-03-14 [?] CRAN (R 4.1.0)
 P prismatic              1.1.0     2021-10-17 [?] CRAN (R 4.1.0)
 P processx               3.5.2     2021-04-30 [?] CRAN (R 4.1.0)
 P promises               1.2.0.1   2021-02-11 [?] CRAN (R 4.0.2)
 P ps                     1.6.0     2021-02-28 [?] CRAN (R 4.1.0)
 P purrr                * 0.3.4     2020-04-17 [?] CRAN (R 4.0.2)
 P R6                     2.5.1     2021-08-19 [?] CRAN (R 4.1.0)
 P RANN                   2.6.1     2019-01-08 [?] CRAN (R 4.1.0)
 P RColorBrewer           1.1-2     2014-12-07 [?] CRAN (R 4.0.2)
 P Rcpp                   1.0.7     2021-07-07 [?] CRAN (R 4.1.0)
 P RcppAnnoy              0.0.19    2021-07-30 [?] CRAN (R 4.1.0)
   RCurl                  1.98-1.6  2022-02-08 [1] CRAN (R 4.1.0)
 P readr                * 2.1.1     2021-11-30 [?] CRAN (R 4.1.0)
 P readxl                 1.3.1     2019-03-13 [?] CRAN (R 4.1.0)
 P rematch2               2.1.2     2020-05-01 [?] CRAN (R 4.1.0)
 P renv                   0.15.0-14 2022-01-10 [?] Github (rstudio/renv@a3b90eb)
 P reprex                 2.0.1     2021-08-05 [?] CRAN (R 4.1.0)
 P reshape2               1.4.4     2020-04-09 [?] CRAN (R 4.1.0)
 P reticulate             1.22      2021-09-17 [?] CRAN (R 4.1.0)
 P rhdf5                  2.38.0    2021-10-26 [?] Bioconductor
 P rhdf5filters           1.6.0     2021-10-26 [?] Bioconductor
 P Rhdf5lib               1.16.0    2021-10-26 [?] Bioconductor
 P rjson                  0.2.21    2022-01-09 [?] CRAN (R 4.1.0)
 P rlang                  0.4.12    2021-10-18 [?] CRAN (R 4.1.0)
 P rmarkdown              2.11      2021-09-14 [?] CRAN (R 4.1.0)
 P ROCR                   1.0-11    2020-05-02 [?] CRAN (R 4.1.0)
 P rpart                  4.1-15    2019-04-12 [?] CRAN (R 4.1.0)
 P rprojroot              2.0.2     2020-11-15 [?] CRAN (R 4.0.2)
 P RSpectra               0.16-0    2019-12-01 [?] CRAN (R 4.1.0)
 P RSQLite                2.2.9     2021-12-06 [?] CRAN (R 4.1.0)
 P rstudioapi             0.13      2020-11-12 [?] CRAN (R 4.0.2)
 P Rtsne                  0.15      2018-11-10 [?] CRAN (R 4.1.0)
 P rvest                  1.0.2     2021-10-16 [?] CRAN (R 4.1.0)
 P S4Vectors            * 0.32.3    2021-11-21 [?] Bioconductor
 P sass                   0.4.0     2021-05-12 [?] CRAN (R 4.1.0)
 P scales                 1.1.1     2020-05-11 [?] CRAN (R 4.0.2)
 P scattermore            0.7       2020-11-24 [?] CRAN (R 4.1.0)
 P sctransform            0.3.3     2022-01-13 [?] CRAN (R 4.1.0)
 P scuttle                1.4.0     2021-10-26 [?] Bioconductor
 P sessioninfo            1.2.2     2021-12-06 [?] CRAN (R 4.1.0)
 P Seurat               * 4.0.6     2021-12-16 [?] CRAN (R 4.1.0)
 P SeuratObject         * 4.0.4     2021-11-23 [?] CRAN (R 4.1.0)
 P shape                  1.4.6     2021-05-19 [?] CRAN (R 4.1.0)
 P shiny                  1.7.1     2021-10-02 [?] CRAN (R 4.1.0)
 P SingleCellExperiment   1.16.0    2021-10-26 [?] Bioconductor
 P sparseMatrixStats      1.6.0     2021-10-26 [?] Bioconductor
 P spatstat.core          2.3-2     2021-11-26 [?] CRAN (R 4.1.0)
 P spatstat.data          2.1-2     2021-12-17 [?] CRAN (R 4.1.0)
 P spatstat.geom          2.3-1     2021-12-10 [?] CRAN (R 4.1.0)
 P spatstat.sparse        2.1-0     2021-12-17 [?] CRAN (R 4.1.0)
 P spatstat.utils         2.3-0     2021-12-12 [?] CRAN (R 4.1.0)
 P speckle              * 0.0.3     2022-03-09 [?] Github (Oshlack/speckle@fc07773)
 P stringi                1.7.6     2021-11-29 [?] CRAN (R 4.1.0)
 P stringr              * 1.4.0     2019-02-10 [?] CRAN (R 4.0.2)
 P SummarizedExperiment   1.24.0    2021-10-26 [?] Bioconductor
 P survival               3.2-13    2021-08-24 [?] CRAN (R 4.1.0)
 P tensor                 1.5       2012-05-05 [?] CRAN (R 4.1.0)
 P tibble               * 3.1.6     2021-11-07 [?] CRAN (R 4.1.0)
 P tidyHeatmap          * 1.7.0     2022-05-13 [?] Github (stemangiola/tidyHeatmap@241aec2)
 P tidyr                * 1.1.4     2021-09-27 [?] CRAN (R 4.1.0)
 P tidyselect             1.1.1     2021-04-30 [?] CRAN (R 4.1.0)
 P tidyverse            * 1.3.1     2021-04-15 [?] CRAN (R 4.1.0)
 P tzdb                   0.2.0     2021-10-27 [?] CRAN (R 4.1.0)
 P utf8                   1.2.2     2021-07-24 [?] CRAN (R 4.1.0)
 P uwot                   0.1.11    2021-12-02 [?] CRAN (R 4.1.0)
 P vctrs                  0.3.8     2021-04-29 [?] CRAN (R 4.0.2)
 P viridis                0.6.2     2021-10-13 [?] CRAN (R 4.1.0)
 P viridisLite            0.4.0     2021-04-13 [?] CRAN (R 4.0.2)
 P vroom                  1.5.7     2021-11-30 [?] CRAN (R 4.1.0)
 P whisker                0.4       2019-08-28 [?] CRAN (R 4.0.2)
 P withr                  2.4.3     2021-11-30 [?] CRAN (R 4.1.0)
 P workflowr            * 1.7.0     2021-12-21 [?] CRAN (R 4.1.0)
 P xfun                   0.29      2021-12-14 [?] CRAN (R 4.1.0)
 P XML                  * 3.99-0.8  2021-09-17 [?] CRAN (R 4.1.0)
 P xml2                   1.3.3     2021-11-30 [?] CRAN (R 4.1.0)
 P xtable                 1.8-4     2019-04-21 [?] CRAN (R 4.1.0)
 P XVector                0.34.0    2021-10-26 [?] Bioconductor
 P yaml                   2.2.1     2020-02-01 [?] CRAN (R 4.0.2)
 P zlibbioc               1.40.0    2021-10-26 [?] Bioconductor
 P zoo                    1.8-9     2021-03-09 [?] CRAN (R 4.1.0)

 [1] /oshlack_lab/jovana.maksimovic/projects/MCRI/melanie.neeland/paed-cf-cite-seq/renv/library/R-4.1/x86_64-pc-linux-gnu
 [2] /config/binaries/R/4.1.0/lib64/R/library

 P ── Loaded and on-disk path mismatch.

──────────────────────────────────────────────────────────────────────────────

9 References

Neavin, Drew, Anne Senabouth, Jimmy Tsz Hang Lee, Aida Ripoll, sc-eQTLGen Consortium, Lude Franke, Shyam Prabhakar, et al. 2022. “Demuxafy: Improvement in Droplet Assignment by Integrating Multiple Single-Cell Demultiplexing and Doublet Detection Methods.” bioRxiv.

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /config/binaries/R/4.1.0/lib64/R/lib/libRblas.so
LAPACK: /config/binaries/R/4.1.0/lib64/R/lib/libRlapack.so

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       

attached base packages:
[1] stats4    stats     graphics  grDevices datasets  utils     methods  
[8] base     

other attached packages:
 [1] Cepo_1.0.0           GSEABase_1.56.0      graph_1.72.0        
 [4] annotate_1.72.0      XML_3.99-0.8         AnnotationDbi_1.56.2
 [7] IRanges_2.28.0       S4Vectors_0.32.3     Biobase_2.54.0      
[10] BiocGenerics_0.40.0  speckle_0.0.3        tidyHeatmap_1.7.0   
[13] paletteer_1.4.0      patchwork_1.1.1      SeuratObject_4.0.4  
[16] Seurat_4.0.6         glue_1.6.0           here_1.0.1          
[19] forcats_0.5.1        stringr_1.4.0        dplyr_1.0.7         
[22] purrr_0.3.4          readr_2.1.1          tidyr_1.1.4         
[25] tibble_3.1.6         ggplot2_3.3.5        tidyverse_1.3.1     
[28] BiocStyle_2.22.0     workflowr_1.7.0     

loaded via a namespace (and not attached):
  [1] scattermore_0.7             bit64_4.0.5                
  [3] knitr_1.37                  irlba_2.3.5                
  [5] DelayedArray_0.20.0         data.table_1.14.2          
  [7] rpart_4.1-15                KEGGREST_1.34.0            
  [9] RCurl_1.98-1.6              doParallel_1.0.16          
 [11] generics_0.1.1              org.Mm.eg.db_3.14.0        
 [13] callr_3.7.0                 cowplot_1.1.1              
 [15] RSQLite_2.2.9               RANN_2.6.1                 
 [17] future_1.23.0               bit_4.0.4                  
 [19] tzdb_0.2.0                  spatstat.data_2.1-2        
 [21] xml2_1.3.3                  lubridate_1.8.0            
 [23] httpuv_1.6.5                SummarizedExperiment_1.24.0
 [25] assertthat_0.2.1            viridis_0.6.2              
 [27] xfun_0.29                   hms_1.1.1                  
 [29] jquerylib_0.1.4             evaluate_0.14              
 [31] promises_1.2.0.1            fansi_1.0.0                
 [33] dendextend_1.15.2           dbplyr_2.1.1               
 [35] readxl_1.3.1                igraph_1.2.11              
 [37] DBI_1.1.2                   htmlwidgets_1.5.4          
 [39] spatstat.geom_2.3-1         ellipsis_0.3.2             
 [41] RSpectra_0.16-0             backports_1.4.1            
 [43] bookdown_0.24               prismatic_1.1.0            
 [45] deldir_1.0-6                sparseMatrixStats_1.6.0    
 [47] MatrixGenerics_1.6.0        vctrs_0.3.8                
 [49] SingleCellExperiment_1.16.0 ROCR_1.0-11                
 [51] abind_1.4-5                 cachem_1.0.6               
 [53] withr_2.4.3                 vroom_1.5.7                
 [55] sctransform_0.3.3           goftest_1.2-3              
 [57] cluster_2.1.2               lazyeval_0.2.2             
 [59] crayon_1.4.2                labeling_0.4.2             
 [61] edgeR_3.36.0                pkgconfig_2.0.3            
 [63] GenomeInfoDb_1.30.1         nlme_3.1-153               
 [65] rlang_0.4.12                globals_0.14.0             
 [67] lifecycle_1.0.1             miniUI_0.1.1.1             
 [69] modelr_0.1.8                cellranger_1.1.0           
 [71] rprojroot_2.0.2             polyclip_1.10-0            
 [73] matrixStats_0.61.0          lmtest_0.9-39              
 [75] Matrix_1.4-0                Rhdf5lib_1.16.0            
 [77] zoo_1.8-9                   reprex_2.0.1               
 [79] whisker_0.4                 ggridges_0.5.3             
 [81] GlobalOptions_0.1.2         processx_3.5.2             
 [83] png_0.1-7                   viridisLite_0.4.0          
 [85] rjson_0.2.21                bitops_1.0-7               
 [87] getPass_0.2-2               KernSmooth_2.23-20         
 [89] rhdf5filters_1.6.0          Biostrings_2.62.0          
 [91] blob_1.2.2                  DelayedMatrixStats_1.16.0  
 [93] shape_1.4.6                 parallelly_1.30.0          
 [95] beachmat_2.10.0             scales_1.1.1               
 [97] memoise_2.0.1               magrittr_2.0.1             
 [99] plyr_1.8.6                  ica_1.0-2                  
[101] zlibbioc_1.40.0             compiler_4.1.0             
[103] RColorBrewer_1.1-2          clue_0.3-60                
[105] fitdistrplus_1.1-6          cli_3.1.0                  
[107] XVector_0.34.0              listenv_0.8.0              
[109] pbapply_1.5-0               ps_1.6.0                   
[111] MASS_7.3-53.1               mgcv_1.8-38                
[113] tidyselect_1.1.1            stringi_1.7.6              
[115] highr_0.9                   yaml_2.2.1                 
[117] locfit_1.5-9.4              ggrepel_0.9.1              
[119] grid_4.1.0                  sass_0.4.0                 
[121] tools_4.1.0                 future.apply_1.8.1         
[123] parallel_4.1.0              circlize_0.4.13            
[125] rstudioapi_0.13             foreach_1.5.1              
[127] git2r_0.29.0                gridExtra_2.3              
[129] farver_2.1.0                Rtsne_0.15                 
[131] digest_0.6.29               BiocManager_1.30.16        
[133] shiny_1.7.1                 Rcpp_1.0.7                 
[135] GenomicRanges_1.46.1        broom_0.7.11               
[137] scuttle_1.4.0               later_1.3.0                
[139] RcppAnnoy_0.0.19            org.Hs.eg.db_3.14.0        
[141] httr_1.4.2                  ComplexHeatmap_2.10.0      
[143] colorspace_2.0-2            rvest_1.0.2                
[145] fs_1.5.2                    tensor_1.5                 
[147] reticulate_1.22             splines_4.1.0              
[149] uwot_0.1.11                 rematch2_2.1.2             
[151] spatstat.utils_2.3-0        renv_0.15.0-14             
[153] sessioninfo_1.2.2           plotly_4.10.0              
[155] xtable_1.8-4                jsonlite_1.7.2             
[157] R6_2.5.1                    pillar_1.6.4               
[159] htmltools_0.5.2             mime_0.12                  
[161] fastmap_1.1.0               BiocParallel_1.28.3        
[163] codetools_0.2-18            utf8_1.2.2                 
[165] lattice_0.20-45             bslib_0.3.1                
[167] spatstat.sparse_2.1-0       leiden_0.3.9               
[169] survival_3.2-13             limma_3.50.0               
[171] rmarkdown_2.11              munsell_0.5.0              
[173] GetoptLong_1.0.5            rhdf5_2.38.0               
[175] GenomeInfoDbData_1.2.7      iterators_1.0.13           
[177] HDF5Array_1.22.1            haven_2.4.3                
[179] reshape2_1.4.4              gtable_0.3.0               
[181] spatstat.core_2.3-2

Analysis of scRNA-seq & CITE-seq Data Combined

Post-process T/NK Cells Sub-clustering Results

Jovana Maksimovic

December 20, 2022

1 Load libraries

2 Load Data

3 Sub-cluster annotation

3.1 Load manual annotations

3.2 Check for doublet enrichment

3.3 Visulalise annotations

3.3.1 No. cells per cluster

3.3.2 Remove SUSPECT clusters

3.4 Visualise annotations

3.5 `Cepo` cluster marker genes

3.5.1 `Cepo` marker gene dot plot

3.6 Visualise cytokines of interest

4 Load protein data

4.1 Add to `Seurat` object

4.2 Load protein annotations

4.3 Visualise all ADTs

4.4 Visualise ADTs of interest

5 Proportions analysis

5.1 Load clinical information

5.2 Sub-cluster proportions (Broad)

5.2.1 Cell proportions by donor

5.3 Sub-cluster proportions (Fine)

5.3.1 Cell proportions by donor

6 Save data

7 Panel figures

8 Session info

9 References

Analysis of scRNA-seq & CITE-seq Data Combined

Post-process T/NK Cells Sub-clustering Results

Jovana Maksimovic

December 20, 2022

1 Load libraries

2 Load Data

3 Sub-cluster annotation

3.1 Load manual annotations

3.2 Check for doublet enrichment

3.3 Visulalise annotations

3.3.1 No. cells per cluster

3.3.2 Remove SUSPECT clusters

3.4 Visualise annotations

3.5 Cepo cluster marker genes

3.5.1 Cepo marker gene dot plot

3.6 Visualise cytokines of interest

4 Load protein data

4.1 Add to Seurat object

4.2 Load protein annotations

4.3 Visualise all ADTs

4.4 Visualise ADTs of interest

5 Proportions analysis

5.1 Load clinical information

5.2 Sub-cluster proportions (Broad)

5.2.1 Cell proportions by donor

5.3 Sub-cluster proportions (Fine)

5.3.1 Cell proportions by donor

6 Save data

7 Panel figures

8 Session info

9 References

3.5 `Cepo` cluster marker genes

3.5.1 `Cepo` marker gene dot plot

4.1 Add to `Seurat` object