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/14_COMBO.postprocess_all.Rmd) and HTML (docs/14_COMBO.postprocess_all.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
html	2983a22	Jovana Maksimovic	2022-12-19	Build site.
Rmd	99078c4	Jovana Maksimovic	2022-12-19	wflow_publish(c("analysis/11_COMBO.postprocess_macrophages.Rmd",
html	16ace9e	Jovana Maksimovic	2022-12-19	Build site.
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

suppressPackageStartupMessages(library(BiocStyle))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(here))
suppressPackageStartupMessages(library(glue))
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(patchwork))
suppressPackageStartupMessages(library(paletteer))
suppressPackageStartupMessages(library(tidyHeatmap))
suppressPackageStartupMessages(library(speckle))
suppressPackageStartupMessages(library(Cepo))
suppressPackageStartupMessages(library(glmGamPoi))
suppressPackageStartupMessages(library(BiocParallel))
suppressPackageStartupMessages(library(limma))
source(here("code/utility.R"))
source(here("code/helper_functions.R"))

set.seed(42)
options(scipen=999)
options(future.globals.maxSize = 6500 * 1024^2)

2 Analyse cell proportions

2.1 Load Data

seu1 <- readRDS(here("data/SCEs/07_COMBO.clean_macrophages_diet.SEU.rds"))
seu2 <- readRDS(here("data/SCEs/06_COMBO.clean_tcells_diet.SEU.rds"))
seu3 <- readRDS(here("data/SCEs/06_COMBO.clean_others_diet.SEU.rds"))
seu <- merge(seu1, y = c(seu2, seu3))
seu

An object of class Seurat 
16323 features across 42658 samples within 3 assays 
Active assay: RNA (16001 features, 0 variable features)
 2 other assays present: ADT.dsb, ADT.raw

            used   (Mb) gc trigger    (Mb)   max used   (Mb)
Ncells   9516871  508.3   14700345   785.1   11543062  616.5
Vcells 458772414 3500.2 1520190279 11598.2 1162014597 8865.5

2.2 Visualise combined, filtered data

DefaultAssay(seu) <- "RNA"
seu <- NormalizeData(seu) %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA(verbose = FALSE, dims = 1:30) %>%
  RunUMAP(verbose = FALSE, dims = 1:30)

DimPlot(seu, group.by = "experiment", combine = FALSE)

[[1]]

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

3 Integrate data

Normalise the data using SCTransform and integrate across batches/individuals.

out <- here("data/SCEs/07_COMBO.clean_integrated.SEU.rds")

if(!file.exists(out)) {
  seuInt <- intDat(seu, split = "donor", type = "RNA",
                   reference = unique(as.character(seu$capture[seu$experiment == 1])))
  saveRDS(seuInt, file = out)

} else {
  seuInt <- readRDS(out)

}

             used    (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells    9656686   515.8   14700345   785.1   14700345   785.1
Vcells 1882914276 14365.5 2702067922 20615.2 1882968820 14366.0

3.1 Visualise integrated data

seuInt <- RunPCA(seuInt, npcs = 30, verbose = FALSE)
seuInt <- RunUMAP(seuInt, verbose = FALSE, dims = 1:30)
DimPlot(seuInt, group.by = "experiment", combine = FALSE)

[[1]]

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

3.2 Visualise labels

options(ggrepel.max.overlaps = Inf)
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE,
        label.size = 2.5, group.by = "Annotation") + NoLegend()

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE,
        label.size = 3, group.by = "Broad",
        cols = paletteer::paletteer_d("miscpalettes::pastel", 
                                      length(unique(seuInt$Broad)))) + 
  NoLegend() -> f1b

f1b

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19

3.2.1 APOC2 gene expression

FeaturePlot(seuInt, features = "APOC2")

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

VlnPlot(seuInt, features = "APOC2", 
        group.by = "Broad", pt.size = 0,
        log = TRUE) + 
  NoLegend() +
  paletteer::scale_fill_paletteer_d("miscpalettes::pastel")

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

3.3 `Cepo` cluster marker genes

cepoMarkers <- Cepo(seuInt[["RNA"]]@data, 
                   seuInt$Broad, 
                   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	CD4.T	CD8.T	Dendritic	Endothelial	Epithelial	Gamma.delta.T	Innate.lymphocyte	Macrophages	Mast.cells	Monocytes	Neutrophils	NK.cells	NK.T	Proliferating.macrophages	Proliferating.NK.T
CD79A	KLRB1	CD8B	LGALS2	SPARCL1	AGR2	GZMK	LEF1	CYP27A1	TPSAB1	CLEC10A	FCN1	TRDC	KLRC1	LPL	TYMS
TNFRSF13C	BCL11B	GZMH	CLEC10A	ACKR1	KRT7	TRDC	MAL	FFAR4	CPA3	FPR3	CD300E	XCL1	KLRC2	HP	MKI67
CD19	CD3G	CD8A	CD1E	RAMP2	KRT17	KLRB1	CD27	PPARG	MS4A2	CSF1R	VCAN	XCL2	KIR2DL4	MS4A4A	PCLAF
MS4A1	LINC01943	GZMA	SERPINF1	CLDN5	FXYD3	TRGC1	XCL1	MS4A4A	SLC18A2	FCGR2B	LILRB2	KLRF1	LINC02446	CYP27A1	RRM2
TNFRSF13B	SPOCK2	GZMM	CD1C	NNMT	CLDN4	KLRG1	ZNF683	GPD1	GATA2	MARCH1	FPR1	KLRC1	GNLY	GPNMB	NUSAP1
FCRL5	CTLA4	LINC02446	MARCH1	CLEC14A	KRT19	GZMM	TRDC	FCGR1A	HDC	F13A1	IL1B	KLRB1	CD8B	TREM1	CDT1
BANK1	CD3D	KLRD1	FCER1A	RAMP3	SMIM22	PRF1	TCF7	SLC7A7	TPSB2	FGL2	LILRB3	GNLY	ZNF683	FFAR4	TOP2A
SPIB	MAF	IFNG	FPR3	ADIRF	SLPI	GZMA	LINC02446	PILRA	IL1RL1	MS4A6A	LILRA5	KRT81	IFNG	HNMT	ASF1B
PAX5	CD3E	CD3G	PKIB	TM4SF18	EPCAM	SLC4A10	KLRC2	PARAL1	KIT	LILRB3	C15orf48	TXK	GZMB	SLC7A7	CLSPN
CD22	TRAC	CXCR6	TSPAN33	VWF	S100A2	ZBTB16	NCR3	LRP1	KCNH2	SLC8A1	SMIM25	TNFRSF18	ITGA1	PARAL1	CDCA5
TLR10	CD6	PRF1	CD86	TM4SF1	TACSTD2	CD27	TRGC2	HNMT	SLC45A3	IGSF6	VEGFA	NCAM1	CD160	LRP1	TPX2
LINC00926	TRBC1	ZNF683	AXL	CAV1	LCN2	SAMD3	RTKN2	TREM1	RHEX	TREM2	S100A8	KRT86	CD8A	PHLDA3	CENPM
FCRLA	CXCR6	LAG3	BASP1	NR2F2	CHST9	LAG3	CD7	PHLDA3	GCSAML	AXL	CSF1R	PTGDR	GZMH	PPARG	BIRC5
VPREB3	TNFRSF25	CD3D	PPP1R14A	DNASE1L3	MUC16	DPP4	CD8B	ITIH5	SIGLEC6	CLEC5A	CSF3R	KLRD1	LINC01871	CSTA	ZWINT
LINC02397	LCK	CD3E	PLD4	TIMP3	TSPAN1	SPOCK2	KLRC3	PCOLCE2	LIF	CLEC7A	MARCH1	SAMD3	KLRD1	TCF7L2	CDK1
CD79B	GZMM	BCL11B	RNASE6	CAVIN1	ELF3	CXCR6	CD3D	GPNMB	NTRK1	RAB31	FPR3	SPINK2	SCML4	OLR1	ASPM
IGHD	CD2	GZMB	GAS6	RNASE1	KRT8	IL18RAP	TRBC1	OLR1	TPSD1	CD86	FGL2	IL2RB	PRF1	RETN	EZH2
MEF2C	TRBC2	LINC01871	SLC8A1	PALMD	CLDN3	KLRC1	CD3G	NCF2	RGS13	TGFBI	ANPEP	TNFRSF4	DAPK2	FCGR1A	UBE2C
RALGPS2	CD247	TRAC	P2RY13	FAM167B	DSP	CD247	CD8A	TCF7L2	CDK15	GAS6	TMEM176B	CD7	CSF1	NCF2	MYBL2
BLK	TRAT1	CCL4	MEF2C	CAVIN2	TMC5	CD3G	CD3E	C5AR1	PBX1	FCGR2A	MARCKS	PRF1	CLNK	MNDA	CDCA7

3.3.1 `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 = "Broad",
        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),
        legend.text = element_text(size = 8),
        legend.title = element_text(size = 10)) +
  scale_color_gradient2(low = pal[1], 
                        mid = pal[3], 
                        high = pal[5]) -> f1d
f1d

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

4 Analyse cell type proportions

4.1 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)

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

4.2 Manual annotation 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	0.1408250	0.0200811	0.0476004	0.0104379	0.0646275	0.0939457	0.0058170	0.0150781	0.0057394	0.0044978	0.0072669	0.0036215
CD4 T	0.0877193	0.0519270	0.0512619	0.0363527	0.1771090	0.0403619	0.0994183	0.0301562	0.0205942	0.0202399	0.1893419	0.0203712
CD8 T	0.0429113	0.0174442	0.0780698	0.0260348	0.0656142	0.0320111	0.0634585	0.0231556	0.0114787	0.0116192	0.0670166	0.0153916
Dendritic	0.0106686	0.0373225	0.0646005	0.0243551	0.0814011	0.0431454	0.0190375	0.0312332	0.0202566	0.0123688	0.0545014	0.0194658
Endothelial	0.0386439	0.0312373	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000
Epithelial	0.1704599	0.1527383	0.0119001	0.0007199	0.0256537	0.0556715	0.0021153	0.0037695	0.0027009	0.0000000	0.0020186	0.0285197
Gamma delta T	0.0109056	0.0032454	0.0020923	0.0022795	0.0009867	0.0000000	0.0063458	0.0000000	0.0000000	0.0003748	0.0242229	0.0009054
Innate lymphocyte	0.0142248	0.0115619	0.0134693	0.0254349	0.0133202	0.0048713	0.0126917	0.0140011	0.0057394	0.0003748	0.0464271	0.0058850
Macrophages	0.4388336	0.5977688	0.6478358	0.8267546	0.4740997	0.6638831	0.7176097	0.8260635	0.8646185	0.8845577	0.5349213	0.8266184
Mast cells	0.0021337	0.0006085	0.0007846	0.0000000	0.0103601	0.0041754	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0004527
Monocytes	0.0028450	0.0060852	0.0244540	0.0081584	0.0138135	0.0104384	0.0058170	0.0113086	0.0087779	0.0153673	0.0141300	0.0104120
Neutrophils	0.0064011	0.0036511	0.0096770	0.0022795	0.0330538	0.0097425	0.0031729	0.0037695	0.0010128	0.0011244	0.0048446	0.0009054
NK cells	0.0180180	0.0107505	0.0086308	0.0038392	0.0103601	0.0062630	0.0084611	0.0010770	0.0030385	0.0018741	0.0222043	0.0009054
NK T	0.0085349	0.0206897	0.0104616	0.0026395	0.0113468	0.0173974	0.0058170	0.0016155	0.0000000	0.0003748	0.0064594	0.0040742
Proliferating macrophages	0.0049787	0.0338742	0.0279848	0.0266347	0.0172669	0.0180932	0.0497091	0.0387722	0.0557056	0.0468516	0.0193783	0.0620190
Proliferating NK/T	0.0018966	0.0010142	0.0011769	0.0040792	0.0009867	0.0000000	0.0005288	0.0000000	0.0003376	0.0003748	0.0072669	0.0004527

4.2.1 Cell proportions by donor (Broad)

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 = 10)) +
  labs(y = "Proportion", fill = "Cell Label") + 
  paletteer::scale_fill_paletteer_d("miscpalettes::pastel") -> f1c
f1c

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19

4.2.2 Cell proportions of control sample relative to CF samples (Broad)

props$Proportions %>%
  data.frame %>%
  inner_join(info, by = c("sample" = "donor")) -> dat

ggplot(dat[dat$Participant != "A1_Ctrl",], 
       aes(x = clusters, y = Freq)) +
  geom_boxplot() +
  geom_point(data = dat[dat$Participant == "A1_Ctrl", ], 
             aes(x = clusters, y = Freq),
             color = "red") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90,
                                   vjust = 0.5,
                                   hjust = 1),
        legend.text = element_text(size = 8)) +
  labs(y = "Proportion") +
  NoLegend() -> p1
p1

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19
63f8ee8	Jovana Maksimovic	2022-12-15

4.2.3 Cell proportions of control sample relative to CF samples (Fine)

macrophages <- c("Macrophages")
tcells <- c("T cell lineage", "Innate lymphoid cell NK")
lung <- c("AT1", "EC arterial", "Rare", "Secretory", "Basal",
          "EC venous", "Multiciliated lineage", "EC capillary", 
          "Lymphatic EC mature", "AT2")

seuInt$grouping <- ifelse(seuInt$predicted.ann_level_3 %in% macrophages, "Macrophages",
                          ifelse(seuInt$predicted.ann_level_3 %in% tcells, "T\\NK Cells", "Other Cells"))

propsFine <- getTransformedProps(clusters = seuInt$Annotation, 
                             sample = seuInt$donor, transform="asin")

propsFine$Proportions %>%
  data.frame %>%
  inner_join(info, by = c("sample" = "donor")) -> dat

dat %>% 
  left_join(seuInt@meta.data %>%
              dplyr::select(Annotation, grouping) %>%
              distinct, by = c("clusters" = "Annotation")) -> dat

lapply(unique(dat$grouping), function(grp) {
ggplot(dat[dat$grouping == grp & dat$Participant != "A1_Ctrl",], 
       aes(x = clusters, y = Freq)) +
  geom_boxplot() +
  geom_point(data = dat[dat$grouping == grp & dat$Participant == "A1_Ctrl", ], 
             aes(x = clusters, y = Freq),
             color = "red") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90,
                                   vjust = 0.5,
                                   hjust = 1),
        legend.text = element_text(size = 8)) +
  labs(y = "Proportion") +
  NoLegend()
}) -> p

c(list(p1), p) -> p

((p[[1]] + theme(axis.title.x = element_blank())) / 
  ((p[[2]] + theme(axis.title.x = element_blank())) +
     (p[[3]] + theme(axis.title.y = element_blank(),
                     axis.title.x = element_blank())) + 
     (p[[4]] + theme(axis.title.y = element_blank(),
                     axis.title.x = element_blank())))) +
  plot_annotation(tag_levels = "A") &
  theme(plot.tag = element_text(size = 14, face = "bold"))

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19
63f8ee8	Jovana Maksimovic	2022-12-15
f3b7b92	Jovana Maksimovic	2022-06-16

5 Compare flow cytometry and scRNA-seq proportions

5.1 Format proportions for comparison

5.1.1 Flow

flow <- read_csv(file = here("data/CITE-seq_pilot_proportions_final.csv"))

flow %>%
  dplyr::rename(HTO = "...1") %>%
  pivot_longer(cols = -HTO,
               names_to = "cell") %>%
  mutate(value = value / 100) -> flowProps

flowProps %>% head(n = 10) %>% knitr::kable()

HTO	cell	value
Human_HTO_2	B cells	0.0076608
Human_HTO_2	CD4 T	0.1901783
Human_HTO_2	CD8 T	0.0791989
Human_HTO_2	Dendritic	0.0157482
Human_HTO_2	Macrophages	0.5330048
Human_HTO_2	Monocytes	0.0164748
Human_HTO_2	Neutrophils	0.0227456
Human_HTO_2	Epithelial	0.0006160
Human_HTO_3	B cells	0.0032860
Human_HTO_3	CD4 T	0.0269643

5.1.2 scRNA-seq

props <- getTransformedProps(clusters = ifelse(seuInt$Broad == "Proliferating macrophages", 
                                               "Macrophages", 
                                               seuInt$Broad), 
                             sample = seuInt$donor, transform="asin")

props$Proportions %>%
  as.data.frame.matrix %>%
  data.frame %>% 
  rownames_to_column(var = "cell") %>%
  pivot_longer(cols = -cell) %>%
  inner_join(dplyr::select(info, donor, HTO), by = c("name" = "donor")) %>%
  dplyr::select(-name) -> scProps

scProps %>% head(n = 10) %>% knitr::kable()

cell	value	HTO
B cells	0.1408250	A
B cells	0.0200811	B
B cells	0.0476004	C
B cells	0.0104379	D
B cells	0.0646275	Human_HTO_8
B cells	0.0939457	Human_HTO_1
B cells	0.0058170	Human_HTO_4
B cells	0.0150781	Human_HTO_6
B cells	0.0057394	Human_HTO_3
B cells	0.0044978	Human_HTO_5

5.2 Test flow cytometry vs. scRNA-seq proportions

Prepare the data for comparison.

flowProps %>% left_join(scProps, by = c("HTO", "cell")) %>%
  left_join(info) -> dat

dat %>%
  dplyr::select(Participant, cell, value.x, value.y) %>%
  pivot_wider(names_from = Participant,
                    names_sep = ".",
                    values_from = c(value.x, value.y)) %>% 
  column_to_rownames(var = "cell") -> datw

Setup design matrix etc. for propeller style comparison using arcsin transformation and limma. Test for difference between flow cytometry and scRNA-seq proportions taking individual into account.

tech <- factor(substr(colnames(datw), 1, 7),
               labels = c("flow", "sc"))
participant <- factor(substr(colnames(datw), 9, 13))

design <- model.matrix(~ 0 + tech + participant)
colnames(design)[1:2] <- levels(tech)

mycontr <- makeContrasts(FvSC = flow-sc,
                         levels = design)

fit <- lmFit(asin(sqrt(datw)), design)
fit.cont <- contrasts.fit(fit, contrasts = mycontr)
fit.cont <- eBayes(fit.cont, robust = TRUE, trend = FALSE)

top <- topTable(fit.cont, coef = 1)    
top %>% knitr::kable()

	logFC	AveExpr	t	P.Value	adj.P.Val	B
Neutrophils	0.0936506	0.1133596	5.1504648	0.0007631	0.0061050	-0.1641039
Dendritic	-0.0755565	0.1386891	-3.9665266	0.0037965	0.0151861	-1.7848679
Macrophages	-0.1087933	1.0455078	-2.3693605	0.0440176	0.1173803	-4.2269994
CD8 T	0.0339028	0.1981006	1.1544616	0.2802708	0.4633173	-5.8930122
Epithelial	0.0124804	0.0826590	1.0527742	0.3219457	0.4633173	-6.0002471
B cells	-0.0116431	0.1006070	-0.9954292	0.3474880	0.4633173	-6.0574042
CD4 T	0.0147809	0.2659411	0.5366039	0.6055356	0.6920406	-6.4155994
Monocytes	0.0066650	0.1090712	0.2818535	0.7849138	0.7849138	-6.5275829

Neutrophils and dendritic cells show statistically significant differences in cell type proportions between technologies.

5.3 Visualise flow cytometry vs. scRNA-seq proportions

A statistically significant difference in cell type proportions between technologies is indicated with an asterisk (*).

p <- vector("list", length(unique(dat$cell)))

for(i in 1:length(p)) {
  tmp <- dat %>%
    dplyr::filter(cell == unique(dat$cell)[i])
  
  # cors <- cor.test(tmp$value.x, tmp$value.y,
  #                  method = "spearman")
  sig <- ifelse(top[unique(dat$cell)[i], ]$adj.P.Val < 0.05, "*", "")
  lim <- max(c(tmp$value.x, tmp$value.y)) 
  
  p[[i]] <- ggplot(tmp,
                   aes(x = value.x, y = value.y, colour = Participant)) +
    geom_point() +
    geom_smooth(method = "lm",
                alpha = 0.15,
                size = 0.5,
                colour = "grey") +
    # annotate("text", -Inf, Inf,  
    #          label = glue("rho: {round(cors$estimate, 2)}
    #                        p-value: {scales::scientific(cors$p.value)}"),
    #          hjust = 0, vjust = 1, size = 3) +
    labs(x = "Proportion (Flow Cytometry)",
         y = "Proportion (scRNA-seq)") +
    ylim(c(0, lim)) +
    xlim(c(0, lim)) +
    theme_classic() +
    ggtitle(glue("{unique(dat$cell)[i]}{sig}"))
  
}

wrap_plots(p, ncol = 3) + 
  plot_layout(guides = "collect") &
  theme(legend.position = "bottom",
        axis.title = element_text(size = 10))

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19

p <- vector("list", length(unique(dat$cell)))

for(i in 1:length(p)) {
  tmp <- dat %>%
    dplyr::filter(cell == unique(dat$cell)[i]) %>%
    rowwise() %>%
    mutate(avg = mean(c(value.x, value.y)),
           diff = value.x - value.y)
  
  sig <- ifelse(top[unique(dat$cell)[i], ]$adj.P.Val < 0.05, "*", "")
  lim <- max(abs(tmp$diff)) 
  if (lim < 0.05) lim <- 0.05
  
  p[[i]] <- ggplot(tmp,
                   aes(x = avg, y = diff, colour = Participant)) +
    geom_point() +
    geom_hline(yintercept = 0, linetype = "dashed", colour = "black") +
    labs(x = "Mean",
         y = "Difference (F - SC)") +
    ylim(c(-lim, lim)) +
    theme_classic() +
    ggtitle(glue("{unique(dat$cell)[i]}{sig}"))
  
}

wrap_plots(p, ncol = 3) + 
  plot_layout(guides = "collect") &
  theme(legend.position = "bottom",
        axis.title = element_text(size = 10),
        axis.text = element_text(size = 8))

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19

6 Load protein data

6.1 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 
35263 features across 42658 samples within 5 assays 
Active assay: RNA (16001 features, 0 variable features)
 4 other assays present: ADT.dsb, ADT.raw, SCT, integrated
 2 dimensional reductions calculated: pca, umap

rm(seuAdt)
gc()

             used    (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells    9985089   533.3   14700345   785.1   14700345   785.1
Vcells 1385115786 10567.6 3242561506 24738.8 3240825692 24725.6

6.2 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))

6.2.1 Visualise ADTs of interest

adt <- read_csv(file = here("data/Proteins_broad_22.04.22.csv"))
adt <- adt[!duplicated(adt$DNA_ID),]
  
cbind(seuInt@meta.data, 
      as.data.frame(t(seuInt@assays$ADT.dsb@data))) %>% 
  dplyr::group_by(Broad, experiment) %>% 
  dplyr::summarize_at(.vars = adt$DNA_ID, .funs = median) %>%
  pivot_longer(c(-Broad, -experiment), names_to = "ADT",
               values_to = "ADT Exp.") %>%
  left_join(adt, by = c("ADT" = "DNA_ID")) %>%
  dplyr::rename(`Cell Label` = Broad,
                Protein = `Name for heatmap`) |> 
  dplyr::filter(experiment == 2) |>
  ungroup() %>%
    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),
        row_names_gp = grid::gpar(fontsize = 10),
        palette_value = circlize::colorRamp2(seq(-1, 8, length.out = 256),
                                         viridis::magma(256)),
        column_title_side = "bottom") |>
  add_tile(`Cell Label`, show_annotation_name = FALSE, show_legend = FALSE,
           palette = paletteer_d("miscpalettes::pastel", ncol(dat)))-> f1e

wrap_heatmap(f1e)

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19
f3b7b92	Jovana Maksimovic	2022-06-16

7 Panel figures

layout = "AAAA
          AAAA
          BBBC
          BBBC
          DDDD
          DDDD
          EEEE
          EEEE
          EEEE"
((ggplot(data.frame(x = 1, y = 1), aes(x, y)) + 
    geom_point(colour = "white") +
    theme_void()) +
 (f1b + ggtitle("")) + 
   f1c + 
   f1d +
   wrap_heatmap(f1e)) + 
  plot_layout(design = layout) +
  plot_annotation(tag_levels = "A") &
  theme(plot.tag = element_text(size = 14, face = "bold"))

Version	Author	Date
2983a22	Jovana Maksimovic	2022-12-19

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 glmGamPoi            * 1.6.0     2021-10-26 [?] Bioconductor
 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] limma_3.50.0         BiocParallel_1.28.3  glmGamPoi_1.6.0     
 [4] Cepo_1.0.0           GSEABase_1.56.0      graph_1.72.0        
 [7] annotate_1.72.0      XML_3.99-0.8         AnnotationDbi_1.56.2
[10] IRanges_2.28.0       S4Vectors_0.32.3     Biobase_2.54.0      
[13] BiocGenerics_0.40.0  speckle_0.0.3        tidyHeatmap_1.7.0   
[16] paletteer_1.4.0      patchwork_1.1.1      SeuratObject_4.0.4  
[19] Seurat_4.0.6         glue_1.6.0           here_1.0.1          
[22] forcats_0.5.1        stringr_1.4.0        dplyr_1.0.7         
[25] purrr_0.3.4          readr_2.1.1          tidyr_1.1.4         
[28] tibble_3.1.6         ggplot2_3.3.5        tidyverse_1.3.1     
[31] 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               codetools_0.2-18           
[163] utf8_1.2.2                  lattice_0.20-45            
[165] bslib_0.3.1                 spatstat.sparse_2.1-0      
[167] leiden_0.3.9                survival_3.2-13            
[169] rmarkdown_2.11              munsell_0.5.0              
[171] GetoptLong_1.0.5            rhdf5_2.38.0               
[173] GenomeInfoDbData_1.2.7      iterators_1.0.13           
[175] HDF5Array_1.22.1            haven_2.4.3                
[177] reshape2_1.4.4              gtable_0.3.0               
[179] spatstat.core_2.3-2

Analysis of scRNA-seq & CITE-seq Data Combined

Cell Type Proportions Analysis

Jovana Maksimovic

December 20, 2022

1 Load libraries

2 Analyse cell proportions

2.1 Load Data

2.2 Visualise combined, filtered data

3 Integrate data

3.1 Visualise integrated data

3.2 Visualise labels

3.2.1 APOC2 gene expression

3.3 `Cepo` cluster marker genes

3.3.1 `Cepo` marker gene dot plot

4 Analyse cell type proportions

4.1 Load clinical information

4.2 Manual annotation proportions (Broad)

4.2.1 Cell proportions by donor (Broad)

4.2.2 Cell proportions of control sample relative to CF samples (Broad)

4.2.3 Cell proportions of control sample relative to CF samples (Fine)

5 Compare flow cytometry and scRNA-seq proportions

5.1 Format proportions for comparison

5.1.1 Flow

5.1.2 scRNA-seq

5.2 Test flow cytometry vs. scRNA-seq proportions

5.3 Visualise flow cytometry vs. scRNA-seq proportions

6 Load protein data

6.1 Add to `Seurat` object

6.2 Load protein annotations

6.2.1 Visualise ADTs of interest

7 Panel figures

8 Session info

Analysis of scRNA-seq & CITE-seq Data Combined

Cell Type Proportions Analysis

Jovana Maksimovic

December 20, 2022

1 Load libraries

2 Analyse cell proportions

2.1 Load Data

2.2 Visualise combined, filtered data

3 Integrate data

3.1 Visualise integrated data

3.2 Visualise labels

3.2.1 APOC2 gene expression

3.3 Cepo cluster marker genes

3.3.1 Cepo marker gene dot plot

4 Analyse cell type proportions

4.1 Load clinical information

4.2 Manual annotation proportions (Broad)

4.2.1 Cell proportions by donor (Broad)

4.2.2 Cell proportions of control sample relative to CF samples (Broad)

4.2.3 Cell proportions of control sample relative to CF samples (Fine)

5 Compare flow cytometry and scRNA-seq proportions

5.1 Format proportions for comparison

5.1.1 Flow

5.1.2 scRNA-seq

5.2 Test flow cytometry vs. scRNA-seq proportions

5.3 Visualise flow cytometry vs. scRNA-seq proportions

6 Load protein data

6.1 Add to Seurat object

6.2 Load protein annotations

6.2.1 Visualise ADTs of interest

7 Panel figures

8 Session info

3.3 `Cepo` cluster marker genes

3.3.1 `Cepo` marker gene dot plot

6.1 Add to `Seurat` object