Last updated: 2022-12-20

Checks: 7 0

Knit directory: paed-cf-cite-seq/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20210524)

The command set.seed(20210524) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 508dee7

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 508dee7. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/obsolete/
    Ignored:    code/obsolete/
    Ignored:    data/190930_A00152_0150_BHTYCMDSXX/
    Ignored:    data/CellRanger/
    Ignored:    data/GSE127465_RAW/
    Ignored:    data/Homo_sapiens.gene_info
    Ignored:    data/SCEs/02_ZILIONIS.sct_normalised.SEU.rds
    Ignored:    data/SCEs/03_C133_Neeland.demultiplexed.SCE.rds
    Ignored:    data/SCEs/03_C133_Neeland.emptyDrops.SCE.rds
    Ignored:    data/SCEs/03_C133_Neeland.preprocessed.SCE.rds
    Ignored:    data/SCEs/03_CF_BAL_Pilot.CellRanger_v6.SCE.rds
    Ignored:    data/SCEs/03_CF_BAL_Pilot.emptyDrops.SCE.rds
    Ignored:    data/SCEs/03_CF_BAL_Pilot.preprocessed.SCE.rds
    Ignored:    data/SCEs/03_COMBO.clustered.SEU.rds
    Ignored:    data/SCEs/03_COMBO.clustered_annotated_macrophages_diet.SEU.rds
    Ignored:    data/SCEs/03_COMBO.clustered_annotated_others_diet.SEU.rds
    Ignored:    data/SCEs/03_COMBO.clustered_annotated_tcells_diet.SEU.rds
    Ignored:    data/SCEs/03_COMBO.clustered_diet.SEU.rds
    Ignored:    data/SCEs/03_COMBO.integrated.SEU.rds
    Ignored:    data/SCEs/03_COMBO.zilionis_mapped.SEU.rds
    Ignored:    data/SCEs/04_C133_Neeland.adt_dsb_normalised.rds
    Ignored:    data/SCEs/04_C133_Neeland.adt_integrated.rds
    Ignored:    data/SCEs/04_C133_Neeland.all_integrated.SEU.rds
    Ignored:    data/SCEs/04_CF_BAL_Pilot.CellRanger_v6.SCE.rds
    Ignored:    data/SCEs/04_CF_BAL_Pilot.emptyDrops.SCE.rds
    Ignored:    data/SCEs/04_CF_BAL_Pilot.preprocessed.SCE.rds
    Ignored:    data/SCEs/04_CF_BAL_Pilot.transfer_adt.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clean_clustered.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clean_clustered.SEU_bk.rds
    Ignored:    data/SCEs/04_COMBO.clean_integrated.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clean_integrated.SEU_bk.rds
    Ignored:    data/SCEs/04_COMBO.clean_macrophages_diet.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clean_others_diet.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clean_tcells_diet.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clustered.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clustered_annotated_adt_diet.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clustered_annotated_lung_diet.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clustered_annotated_macrophages_diet.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clustered_annotated_others_diet.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clustered_annotated_tcells_diet.SEU.rds
    Ignored:    data/SCEs/04_COMBO.clustered_diet.SEU.rds
    Ignored:    data/SCEs/04_COMBO.integrated.SEU.rds
    Ignored:    data/SCEs/04_COMBO.macrophages_clustered.SEU.rds
    Ignored:    data/SCEs/04_COMBO.macrophages_integrated.SEU.rds
    Ignored:    data/SCEs/04_COMBO.others_clustered.SEU.rds
    Ignored:    data/SCEs/04_COMBO.others_integrated.SEU.rds
    Ignored:    data/SCEs/04_COMBO.tcells_clustered.SEU.rds
    Ignored:    data/SCEs/04_COMBO.tcells_integrated.SEU.rds
    Ignored:    data/SCEs/04_COMBO.zilionis_mapped.SEU.rds
    Ignored:    data/SCEs/05_CF_BAL_Pilot.transfer_adt.SEU.rds
    Ignored:    data/SCEs/05_COMBO.clean_clustered.SEU.rds
    Ignored:    data/SCEs/05_COMBO.clean_integrated.SEU.rds
    Ignored:    data/SCEs/05_COMBO.clean_macrophages_diet.SEU.rds
    Ignored:    data/SCEs/05_COMBO.clean_others_diet.SEU.rds
    Ignored:    data/SCEs/05_COMBO.clean_tcells_diet.SEU.rds
    Ignored:    data/SCEs/05_COMBO.clustered_annotated_adt_diet.SEU.rds
    Ignored:    data/SCEs/05_COMBO.clustered_annotated_lung_diet.SEU.rds
    Ignored:    data/SCEs/05_COMBO.clustered_annotated_macrophages_diet.SEU.rds
    Ignored:    data/SCEs/05_COMBO.clustered_annotated_others_diet.SEU.rds
    Ignored:    data/SCEs/05_COMBO.clustered_annotated_tcells_diet.SEU.rds
    Ignored:    data/SCEs/05_COMBO.macrophages_clustered.SEU.rds
    Ignored:    data/SCEs/05_COMBO.macrophages_integrated.SEU.rds
    Ignored:    data/SCEs/05_COMBO.others_clustered.SEU.rds
    Ignored:    data/SCEs/05_COMBO.others_integrated.SEU.rds
    Ignored:    data/SCEs/05_COMBO.tcells_clustered.SEU.rds
    Ignored:    data/SCEs/05_COMBO.tcells_integrated.SEU.rds
    Ignored:    data/SCEs/06_COMBO.clean_clustered.DIET.rds
    Ignored:    data/SCEs/06_COMBO.clean_clustered.SEU.rds
    Ignored:    data/SCEs/06_COMBO.clean_integrated.SEU.rds
    Ignored:    data/SCEs/06_COMBO.clean_macrophages_diet.SEU.rds
    Ignored:    data/SCEs/06_COMBO.clean_others_diet.SEU.rds
    Ignored:    data/SCEs/06_COMBO.clean_tcells_diet.SEU.rds
    Ignored:    data/SCEs/06_COMBO.macrophages_clustered.SEU.rds
    Ignored:    data/SCEs/06_COMBO.macrophages_clustered_dbl.SEU.rds
    Ignored:    data/SCEs/06_COMBO.macrophages_integrated.SEU.rds
    Ignored:    data/SCEs/06_COMBO.macrophages_integrated_dbl.SEU.rds
    Ignored:    data/SCEs/06_COMBO.others_clustered.SEU.rds
    Ignored:    data/SCEs/06_COMBO.others_integrated.SEU.rds
    Ignored:    data/SCEs/06_COMBO.tcells_clustered.SEU.rds
    Ignored:    data/SCEs/06_COMBO.tcells_integrated.SEU.rds
    Ignored:    data/SCEs/07_COMBO.clean_integrated.SEU.rds
    Ignored:    data/SCEs/07_COMBO.clean_macrophages_diet.SEU.rds
    Ignored:    data/SCEs/07_COMBO.macrophages_clustered.SEU.rds
    Ignored:    data/SCEs/07_COMBO.macrophages_integrated.SEU.rds
    Ignored:    data/SCEs/C133_Neeland.CellRanger.SCE.rds
    Ignored:    data/SCEs/experiment1_doublets.rds
    Ignored:    data/SCEs/experiment2_doublets.rds
    Ignored:    data/SCEs/obsolete/
    Ignored:    data/cellsnp-lite/
    Ignored:    data/emptyDrops/obsolete/
    Ignored:    data/obsolete/
    Ignored:    data/sample_sheets/obsolete/
    Ignored:    output/marker-analysis/obsolete/
    Ignored:    output/obsolete/
    Ignored:    rename_captures.R
    Ignored:    renv/library/
    Ignored:    renv/staging/
    Ignored:    wflow_background.R

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

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.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/13_COMBO.postprocess_others.Rmd) and HTML (docs/13_COMBO.postprocess_others.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

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.others_clustered.SEU.rds")
seuInt <- readRDS(file = out)

seuInt

An object of class Seurat 
33706 features across 5967 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/others_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 
33706 features across 5967 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 neutrophil-like clusters are significantly over-represented for putative doublets. As the SUSPECT cluster will be filtered out and the neutrophil-like cluster has <10% putative doublets, 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
B cells	1399	23.6
basal cell	1033	17.4
cDC1	129	2.2
cDC2	1055	17.8
ciliated cell	339	5.7
dividing B cells	65	1.1
endothelial cell	317	5.4
goblet/club cell	344	5.8
mast cells	46	0.8
migratory DC	192	3.2
monocytes	497	8.4
neutrophil-like	252	4.3
plasma B cells	40	0.7
plasmacytoid DC	142	2.4
tracheobronchial goblet/club cells	72	1.2

3.4 Visualise annotations

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

DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE, 
        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)) -> f4a
f4a

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()

B.cells	basal.cell	cDC1	cDC2	ciliated.cell	dividing.B.cells	endothelial.cell	goblet.club.cell	mast.cells	migratory.DC	monocytes	neutrophil.like	plasma.B.cells	plasmacytoid.DC	tracheobronchial.goblet.club.cells
MS4A1	KRT6A	CLEC9A	CLEC10A	DNAH9	MS4A1	ACKR1	TFF1	TPSAB1	CCR7	CLEC10A	FCN1	TNFRSF17	SCT	TSPAN8
TNFRSF13C	KRT17	XCR1	FPR3	MORN5	PAX5	RAMP3	AZGP1	MS4A2	LAMP3	FPR3	CD300E	MZB1	GZMB	FAM3D
CD19	KRT5	LGALS2	CD1E	C11orf88	FCRLA	NNMT	FST	CPA3	LAD1	CSF1R	FPR1	ZBP1	CLEC4C	KLK11
LINC00926	PI3	CADM1	CD1C	DYNLRB2	TNFRSF13C	CLDN5	MUC5AC	SLC18A2	CCL22	LILRB3	LILRB2	DERL3	LILRA4	SERPINB3
TNFRSF13B	SFN	C1orf54	FCER1A	CAPSL	CD79A	SPARCL1	SYT8	HDC	IDO1	F13A1	VCAN	JCHAIN	MAP1A	CYP2F1
CD79A	AZGP1	ENPP1	LGALS2	C11orf97	CD19	TM4SF18	KRT17	TPSB2	SLCO5A1	TREM2	IL1B	CD27	PTCRA	CHST9
PAX5	S100A2	GNAO1	AXL	SNTN	VPREB3	VWF	TM4SF1	GATA2	TNFRSF11A	FCGR2B	LILRB3	TNFRSF13B	MZB1	CCNO
IGHD	TCN1	S100B	SLC8A1	TSPAN19	TCL1A	RAMP2	KRT7	IL1RL1	GPR157	CLEC5A	S100A8	POU2AF1	LRRC26	ADH1C
CD79B	SYT8	RAB7B	CSF1R	C1orf194	POU2AF1	TIMP3	PI3	KCNH2	CST7	FPR1	LILRA5	CD79A	PACSIN1	CXCL17
CD22	KRT7	P2RY6	CST7	ENKUR	BCAS4	CLEC14A	IGFBP3	KIT	ANKRD33B	SLC8A1	ITGAM	TENT5C	FAM129C	SOX2
FCRL5	S100A14	SNX22	IGSF6	TMEM190	CD79B	LDB2	S100A2	NTRK1	TREML1	PLXDC2	OSM	FCRL5	LAMP5	AQP5
VPREB3	FST	DNASE1L3	FCGR2B	DYDC2	BIRC5	FAM167B	PTGES	TPSD1	CCL19	IGSF6	CSF1R	IGHA1	PLD4	TMPRSS4
FCMR	S100A16	CLNK	PKIB	SPAG6	PCLAF	PLVAP	KRT6A	GCSAML	IL7R	FGL2	AQP9	PRDM1	JCHAIN	MUC4
BLK	RHOV	FLT3	JAML	TEKT1	CD22	CCL14	CLDN10	LIF	IL4I1	C3AR1	CLEC4E	CD79B	SMPD3	CRYM
LINC02397	LYPD3	FBXO27	P2RY13	EFCAB1	MYBL2	PCDH17	STEAP4	SLC45A3	TVP23A	FCGR2A	CCL3	CPNE5	DRD4	SLC44A4
FCRLA	TM4SF1	IDO1	CPVL	C9orf135	TLR10	RBP7	AQP5	KRT1	HMSD	ITGAM	APOBEC3A	SLAMF7	SHD	UPK1B
FCER2	KRT6B	SLAMF8	CLEC5A	FAM216B	AURKB	ESAM	MSLN	SIGLEC6	FLT3	MS4A6A	SEMA6B	TXNDC5	DERL3	KRT7
BANK1	PITX1	CST7	FGL2	CFAP53	ACY3	NUAK1	RARRES1	CDK15	PTGIR	CLEC7A	CCR1	GLCCI1	SELL	CYP4B1
FCRL2	FAM83A	BATF3	SUCNR1	FAM183A	MKI67	CAVIN2	AGR2	GRAP2	LGALS2	TNFSF13B	CSF3R	NUGGC	LILRB4	CHL1
SPIB	TFF1	WDFY4	FPR1	RSPH1	ASF1B	NR2F2	GPRC5A	TMEM233	NR4A3	AXL	FYB1	FKBP11	SPIB	F3

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 = 8) + 
  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]) -> f4c
f4c

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

3.6 Visualise cytokines of interest

markers <- read_csv(file = here("data",
                                "other_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",
                       "05_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 
34028 features across 5922 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   9904609  529.0   14642867  782.1  14642867  782.1
Vcells 262468134 2002.5  868076594 6622.9 834347238 6365.6

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_other_22.04.22.csv"))
adt <- adt[!duplicated(adt$DNA_ID),]

pal <- paletteer_d("miscpalettes::pastel", length(levels(seuInt$Annotation)))
names(pal) <- levels(seuInt$Annotation)
pal <- pal[names(pal) %in% unique(as.character(seuInt$Annotation[seuInt$experiment == 2]))]

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",
    palette_value = circlize::colorRamp2(seq(-1, topMax, length.out = 256),
                                         viridis::magma(256)),
    heatmap_legend_param = list(direction = "vertical"),
    column_title_side = "bottom") |>
  add_tile(`Cell Label`, show_legend = FALSE,
           show_annotation_name = FALSE,
           palette = pal) -> f4d
f4d

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
B cells	0.3785851	0.0797744	0.2993421	0.2271540	0.2823276	0.4326923	0.1617647	0.2314050	0.1491228	0.1348315	0.0878049	0.0571429
Dendritic	0.0286807	0.1482675	0.4062500	0.5300261	0.3556034	0.1987179	0.5294118	0.4793388	0.5263158	0.3707865	0.6585366	0.3071429
Endothelial	0.1038878	0.1240935	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000
Epithelial	0.4582537	0.6067687	0.0748355	0.0156658	0.1120690	0.2564103	0.0588235	0.0578512	0.0701754	0.0000000	0.0243902	0.4500000
Mast cells	0.0057361	0.0024174	0.0049342	0.0000000	0.0452586	0.0192308	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0071429
Monocytes	0.0076482	0.0241741	0.1537829	0.1775457	0.0603448	0.0480769	0.1617647	0.1735537	0.2280702	0.4606742	0.1707317	0.1642857
Neutrophils	0.0172084	0.0145044	0.0608553	0.0496084	0.1443966	0.0448718	0.0882353	0.0578512	0.0263158	0.0337079	0.0585366	0.0142857

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
B cells	0.3473550	0.0709106	0.2746711	0.2219321	0.2737069	0.4262821	0.1617647	0.2231405	0.1315789	0.1235955	0.0878049	0.0357143
basal cell	0.3091141	0.4190169	0.0049342	0.0052219	0.0129310	0.0352564	0.0000000	0.0000000	0.0000000	0.0000000	0.0097561	0.0071429
cDC1	0.0012747	0.0032232	0.0328947	0.0626632	0.0280172	0.0032051	0.0147059	0.0247934	0.0087719	0.0112360	0.1853659	0.0071429
cDC2	0.0210325	0.1095890	0.2828947	0.3681462	0.2500000	0.1698718	0.4264706	0.3966942	0.4035088	0.3033708	0.2243902	0.2571429
ciliated cell	0.0337795	0.0443191	0.0460526	0.0078329	0.0840517	0.2051282	0.0588235	0.0413223	0.0701754	0.0000000	0.0146341	0.3500000
dividing B cells	0.0229446	0.0048348	0.0139803	0.0052219	0.0021552	0.0032051	0.0000000	0.0000000	0.0087719	0.0112360	0.0000000	0.0000000
endothelial cell	0.1038878	0.1240935	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000
goblet/club cell	0.1102613	0.1369863	0.0000000	0.0000000	0.0021552	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000
mast cells	0.0057361	0.0024174	0.0049342	0.0000000	0.0452586	0.0192308	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0071429
migratory DC	0.0012747	0.0161160	0.0633224	0.0731070	0.0474138	0.0096154	0.0588235	0.0413223	0.0964912	0.0337079	0.0634146	0.0285714
monocytes	0.0076482	0.0241741	0.1537829	0.1775457	0.0603448	0.0480769	0.1617647	0.1735537	0.2280702	0.4606742	0.1707317	0.1642857
neutrophil-like	0.0172084	0.0145044	0.0608553	0.0496084	0.1443966	0.0448718	0.0882353	0.0578512	0.0263158	0.0337079	0.0585366	0.0142857
plasma B cells	0.0082855	0.0040290	0.0106908	0.0000000	0.0064655	0.0032051	0.0000000	0.0082645	0.0087719	0.0000000	0.0000000	0.0214286
plasmacytoid DC	0.0050988	0.0193392	0.0271382	0.0261097	0.0301724	0.0160256	0.0294118	0.0165289	0.0175439	0.0224719	0.1853659	0.0142857
tracheobronchial goblet/club cells	0.0050988	0.0064464	0.0238487	0.0026110	0.0129310	0.0160256	0.0000000	0.0165289	0.0000000	0.0000000	0.0000000	0.0928571

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") -> f4b

6 Save data

out <- here(glue("data/SCEs/06_COMBO.clean_others_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"
((f4a + ggtitle("")) + 
   f4b + 
   f4c +
   wrap_heatmap(f4d)) + 
  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.

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

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 Other 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

Analysis of scRNA-seq & CITE-seq Data Combined

Post-process Other 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

3.5 `Cepo` cluster marker genes

3.5.1 `Cepo` marker gene dot plot

4.1 Add to `Seurat` object