Last updated: 2022-06-21

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: 14ec446

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 14ec446. 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/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.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_integrated.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/C133_Neeland.CellRanger.SCE.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:   .gitignore
    Modified:   .renvignore
    Modified:   analysis/ref.bib
    Modified:   renv/.gitignore
    Modified:   renv/settings.dcf

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/10_COMBO.cluster_others.Rmd) and HTML (docs/10_COMBO.cluster_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 14ec446 Jovana Maksimovic 2022-06-21 wflow_publish(c("analysis/08_COMBO.cluster_macrophages.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

Load the clustered and labelled CF_BAL_Pilot and C133_Neeland data.

seu1 <- readRDS(file = here("data/SCEs/05_COMBO.clustered_annotated_lung_diet.SEU.rds"))
seu2 <- readRDS(file = here("data/SCEs/05_COMBO.clustered_annotated_others_diet.SEU.rds"))
seu <- merge(seu1, y = seu2)

DefaultAssay(seu) <- "RNA"
entrez <- select(org.Hs.eg.db, columns = c("ENTREZID","SYMBOL"), 
                 keys = keys(org.Hs.eg.db)) 
entrez <- entrez[!is.na(entrez$ENTREZID),]
seu <- seu[alias2SymbolTable(rownames(seu)) %in% entrez$SYMBOL,]
seu
An object of class Seurat 
16001 features across 5967 samples within 1 assay 
Active assay: RNA (16001 features, 0 variable features)

3 Subcluster other cells

Normalise and integrate data.

out <- here("data/SCEs/06_COMBO.others_integrated.SEU.rds")

if(!file.exists(out)){ 
  seuInt <- intDat(seu, type = "RNA", 
                   reference = unique(seu$capture[seu$experiment == 1]),
                   #k.weight = min(table(seu$donor)))
                   k.weight = min(table(seu$donor)) - 1)
  saveRDS(seuInt, file = out)
  
} else {
  seuInt <- readRDS(file = out)
  
}

Visualise the data.

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

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

4 Clustering

4.1 Perform Linear Dimensional Reduction

p1 <- DimPlot(seuInt, reduction = "pca", group.by = "donor")
p2 <- DimPlot(seuInt, reduction = "pca", dims = c(1,3), group.by = "donor")
p3 <- DimPlot(seuInt, reduction = "pca", dims = c(2,3), group.by = "donor")
p4 <- DimPlot(seuInt, reduction = "pca", dims = c(3,4), group.by = "donor")

((p1 | p2) / (p3 | p4)) + plot_layout(guides = "collect") &
  theme(legend.text = element_text(size = 8),
        plot.title = element_text(size = 10),
        axis.title = element_text(size = 9),
        axis.text = element_text(size = 8))

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16 
DimHeatmap(seuInt, dims = 1:30, cells = 500, balanced = TRUE)

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

4.2 Determine the ‘Dimensionality’ of the Dataset

ElbowPlot(seuInt, ndims = 30)

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

5 Cluster the Cells

Examine cluster number and size with respect to resolution.

out <- here("data/SCEs/06_COMBO.others_clustered.SEU.rds")

if(!file.exists(out)){
  seuInt <- FindNeighbors(seuInt, reduction = "pca", dims = 1:30)
  seuInt <- FindClusters(seuInt, algorithm = 3, 
                         resolution = seq(0.1, 1, by = 0.1))
  seuInt <- RunUMAP(seuInt, dims = 1:10)
  saveRDS(seuInt, file = out)
  
} else {
  seuInt <- readRDS(file = out)
  
}

clustree::clustree(seuInt)

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

Choose a resolution. Visualise UMAP.

grp <- "integrated_snn_res.1"
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE, 
        label.size = 2.5, group.by = grp) + NoLegend()

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16 
options(ggrepel.max.overlaps = Inf)
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE, 
        label.size = 2.5, group.by = "predicted.ann_level_3") +
  theme(legend.position = "bottom") + NoLegend()

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

5.1 Examine clusters

Visualise quality metrics by cluster.

seuInt@meta.data %>%
  ggplot(aes(x = integrated_snn_res.1,
             y = predicted.annotation.l1.score,
             fill = integrated_snn_res.1)) +
  geom_violin(scale = "width") +
  NoLegend() -> p1

seuInt@meta.data %>%
  ggplot(aes(x = integrated_snn_res.1,
             y = nCount_RNA,
             fill = integrated_snn_res.1)) +
  geom_violin(scale = "area") +
  scale_y_log10() +
  NoLegend() -> p2

seuInt@meta.data %>%
  ggplot(aes(x = integrated_snn_res.1,
             y = nFeature_RNA,
             fill = integrated_snn_res.1)) +
  geom_violin(scale = "area") +
  scale_y_log10() +
  NoLegend() -> p3

seuInt@meta.data %>%
  ggplot(aes(x = integrated_snn_res.1,
             y = predicted.ann_level_3.score,
             fill = integrated_snn_res.1)) +
  geom_violin(scale = "area") +
  scale_y_log10() +
  NoLegend() -> p4

((p1 | p2) / (p3 | p4)) & theme(text = element_text(size = 8))

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

6 Identify Cluster Marker Genes

Adapted from Dr. Belinda Phipson’s work for (Sim et al. 2021).

6.1 Test for Marker Genes using limma

# limma-trend for DE
Idents(seuInt) <- grp
counts <- as.matrix(seuInt[["RNA"]]@counts)

y.org <- DGEList(counts)
logcounts <- normCounts(y.org, log = TRUE, prior.count = 0.5)

maxclust <- length(levels(Idents(seuInt))) - 1

clustgrp <- paste0("c", Idents(seuInt))
clustgrp <- factor(clustgrp, levels = paste0("c", 0:maxclust))
donor <- seuInt$donor

design <- model.matrix(~ 0 + clustgrp + donor)
colnames(design)[1:(length(levels(clustgrp)))] <- levels(clustgrp)

# Create contrast matrix
mycont <- matrix(NA, ncol = length(levels(clustgrp)), 
                 nrow = length(levels(clustgrp)))
rownames(mycont) <- colnames(mycont) <- levels(clustgrp)
diag(mycont) <- 1
mycont[upper.tri(mycont)] <- -1/(length(levels(factor(clustgrp))) - 1)
mycont[lower.tri(mycont)] <- -1/(length(levels(factor(clustgrp))) - 1)

# Fill out remaining rows with 0s
zero.rows <- matrix(0, ncol = length(levels(clustgrp)),
                    nrow = (ncol(design) - length(levels(clustgrp))))
fullcont <- rbind(mycont, zero.rows)
rownames(fullcont) <- colnames(design)

fit <- lmFit(logcounts, design)
fit.cont <- contrasts.fit(fit, contrasts = fullcont)
fit.cont <- eBayes(fit.cont, trend = TRUE, robust = TRUE)

summary(decideTests(fit.cont))
          c0    c1    c2    c3    c4    c5    c6    c7    c8    c9   c10   c11
Down    5349 12063  2813 11268 11366 11279  2549  2024  2881  1924  2881  2725
NotSig  7460  3650  8310  4417  4374  4263 10614  6258  8641 10425  9244 11103
Up      3192   288  4878   316   261   459  2838  7719  4479  3652  3876  2173
         c12   c13   c14   c15   c16   c17   c18   c19   c20   c21   c22   c23
Down    1993  3383  1947  1176  1487  1937   862  1033  2611   802    61   621
NotSig 12900 10470 10466 11504 12316 12064 11756 11385 12102 13718 12680 14939
Up      1108  2148  3588  3321  2198  2000  3383  3583  1288  1481  3260   441

6.2 Test relative to a threshold (TREAT)

tr <- treat(fit.cont, fc = 1.5)
dt <- decideTests(tr)
summary(dt)
          c0    c1    c2    c3    c4    c5    c6    c7    c8    c9   c10   c11
Down      64   766     7   707   744   790     6   113    34     7    36     7
NotSig 15724 15224 15670 15273 15240 15177 15725 14663 15624 15692 15757 15748
Up       213    11   324    21    17    34   270  1225   343   302   208   246
         c12   c13   c14   c15   c16   c17   c18   c19   c20   c21   c22   c23
Down       5    38    25     5    12    37    59    17   148    22     0    30
NotSig 15945 15619 15667 15773 15732 15878 15668 15709 15734 15791 15779 15920
Up        51   344   309   223   257    86   274   275   119   188   222    51

6.2.1 Mean-difference Plots per Cluster

par(mfrow=c(3,3))

for(i in 1:ncol(mycont)){
  plotMD(tr, coef = i, status = dt[,i], hl.cex = 0.5)
  abline(h = 0, col = "lightgrey")
  lines(lowess(tr$Amean, tr$coefficients[,i]), lwd = 1.5, col = 4)
}

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

6.2.2 Export Marker Genes per cluster

options(scipen=-1, digits = 6)
contnames <- colnames(mycont)
dirName <- here("output/marker-analysis/05-COMBO-others")
if(!dir.exists(dirName)) dir.create(dirName)

getCols <- setNames(c("SYMBOL","ENTREZID"),c("SYMBOL","ENTREZID"))
tr$genes <- data.frame(
  lapply(getCols, function(column) {
    mapIds(
      x = org.Hs.eg.db,
      keys = rownames(tr),
      keytype = "SYMBOL",
      column = column)
  }),
  row.names = rownames(tr))

gsAnnots <- buildIdx(entrezIDs = tr$genes$ENTREZID, species = "human",
                     msigdb.gsets = c("c2","c5"))
[1] "Loading MSigDB Gene Sets ... "
[1] "Loaded gene sets for the collection c2 ..."
[1] "Indexed the collection c2 ..."
[1] "Created annotation for the collection c2 ..."
[1] "Loaded gene sets for the collection c5 ..."
[1] "Indexed the collection c5 ..."
[1] "Created annotation for the collection c5 ..."
[1] "Building KEGG pathways annotation object ... "
reactomeIdx <-gsAnnots$c2@idx[grep("REACTOME", 
                                   names(gsAnnots$c2@idx))]

for(i in 1:length(contnames)){
  top <- topTreat(tr, coef = i, n = Inf)
  top <- top[top$logFC > 0, ]
  
  write.csv(top[1:100, ],
            file = glue("{dirName}/up-cluster-{contnames[i]}.csv"))
  
  cameraPR(tr$t[,i], reactomeIdx) %>%
    rownames_to_column(var = "Pathway") %>%
    slice_head(n = 20) %>%
    write_csv(file = here(glue("{dirName}/REACTOME-cluster-{contnames[i]}.csv")))
}

6.2.3 Cluster marker gene dot plot

Genes duplicated between clusters are excluded.

sig.genes <- vector("list", ncol(tr))
p <- vector("list",length(sig.genes))
DefaultAssay(seuInt) <- "RNA"

for(i in 1:length(sig.genes)){
  top <- topTreat(tr, coef = i, n = Inf)
  sig.genes[[i]] <- rownames(top)[top$logFC > 0][1:10]
}

sig <- unlist(sig.genes)
geneCols <- c(rep(rep(c("grey","black"), each = 10), ncol(tr)/2), 
              rep("grey", 10))[!duplicated(sig)] 

DotPlot(seuInt,    features = sig[!duplicated(sig)], 
                    group.by = "integrated_snn_res.1",
                    cols = c("lightgrey", "red"),
                    dot.scale = 3) + 
    RotatedAxis() + 
    FontSize(y.text = 8, x.text = 12) + 
    labs(y = element_blank(), x = element_blank()) + 
    coord_flip() + 
  theme(axis.text.y = element_text(color = geneCols)) +
  ggtitle("Top 10 cluster marker genes without duplicates")

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

6.2.4 Neutrophil relevant gene expression

neuMarkers <- c("CSF3R","FPR1","FCGR3B","NAMPT","MNDA","S100A8","FUT4","CEACAM8",
                "PLAUR","APOBEC3A","SRGN","AIF1","IL1RN","IF1B","SOD2","FCN1")

DoHeatmap(seuInt,
          group.by = "integrated_snn_res.1", size = 2.5,
          features = neuMarkers, assay = "RNA", slot = "data") + 
  NoLegend() +
  theme(axis.text = element_text(size = 6))

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

6.2.5 No. cells per cluster

seuInt@meta.data %>%
  ggplot(aes(x = integrated_snn_res.1, fill = integrated_snn_res.1)) +
  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()

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

7 Load protein data

7.1 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 5967 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  12122745  647.5   21529730 1149.9   21529730 1149.9
Vcells 436269269 3328.5 1153155568 8797.9 1002700595 7650.0

7.2 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))

7.3 Visualise all ADTs

Normalised with DSB. CITE-seq ADT data was transferred to scRNA-seq using reference mapping and transfer.

cbind(seuInt@meta.data, 
      as.data.frame(t(seuInt@assays$ADT.dsb@data))) %>% 
  dplyr::group_by(integrated_snn_res.1, experiment) %>% 
  dplyr::summarize_at(.vars = prots$id, .funs = median) %>%
  pivot_longer(c(-integrated_snn_res.1, -experiment), names_to = "ADT",
               values_to = "ADT Exp.") %>%
  left_join(prots, by = c("ADT" = "id")) %>%
  mutate(Cluster = as.character(integrated_snn_res.1)) %>%
  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
f3b7b92 Jovana Maksimovic 2022-06-16 
dat |>
  heatmap(
    .column = Cluster,
    .row = Protein,
    .value = `ADT Exp.`,
    scale = "none",
    palette_value = circlize::colorRamp2(seq(-1, topMax, length.out = 256),
                                         viridis::magma(256)),
    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",
    heatmap_legend_param = list(direction = "vertical"))

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

7.4 Visualise ADTs of interest

adt <- read_csv(file = here("data/Proteins_other_22.04.22.csv"))
adt <- adt[!duplicated(adt$DNA_ID),]

dat %>%
  inner_join(adt, by = c("ADT" = "DNA_ID")) %>%
  dplyr::mutate(Protein = `Name for heatmap`) |> 
  heatmap(
    .column = Cluster,
    .row = Protein,
    .value = `ADT Exp.`,
    scale = "none",
    palette_value = circlize::colorRamp2(seq(-1, topMax, length.out = 256),
                                         viridis::magma(256)),
    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",
    heatmap_legend_param = list(direction = "vertical"))

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

7.5 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 = "integrated_snn_res.1") +
  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 = "Cluster", x = "Cytokine")

p

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

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     2022-06-21
 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 beeswarm               0.4.0      2021-06-01 [?] CRAN (R 4.1.0)
 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 BiocNeighbors          1.12.0     2021-10-26 [?] Bioconductor
 P BiocParallel         * 1.28.3     2021-12-09 [?] Bioconductor
 P BiocSingular           1.10.0     2021-10-26 [?] 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 bluster                1.4.0      2021-10-26 [?] Bioconductor
 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 caTools                1.18.2     2021-03-28 [?] CRAN (R 4.1.0)
 P cellranger             1.1.0      2016-07-27 [?] CRAN (R 4.1.0)
 P checkmate              2.0.0      2020-02-06 [?] CRAN (R 4.0.2)
 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 clustree             * 0.4.4      2021-11-08 [?] 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 doRNG                  1.8.2      2020-01-27 [?] CRAN (R 4.1.0)
 P dplyr                * 1.0.7      2021-06-18 [?] CRAN (R 4.1.0)
 P dqrng                  0.3.0      2021-05-01 [?] CRAN (R 4.1.0)
 P DropletUtils         * 1.14.1     2021-11-08 [?] Bioconductor
 P DT                     0.20       2021-11-15 [?] CRAN (R 4.1.0)
 P edgeR                * 3.36.0     2021-10-26 [?] Bioconductor
 P EGSEA                * 1.22.0     2021-10-26 [?] Bioconductor
 P EGSEAdata              1.22.0     2021-10-30 [?] 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 gage                 * 2.44.0     2021-10-26 [?] Bioconductor
 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 ggbeeswarm             0.6.0      2017-08-07 [?] CRAN (R 4.1.0)
 P ggforce                0.3.3      2021-03-05 [?] CRAN (R 4.1.0)
 P ggplot2              * 3.3.5      2021-06-25 [?] CRAN (R 4.0.2)
 P ggraph               * 2.0.5      2021-02-23 [?] CRAN (R 4.1.0)
 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 globaltest             5.48.0     2021-10-26 [?] Bioconductor
 P glue                 * 1.6.0      2021-12-17 [?] CRAN (R 4.1.0)
 P GO.db                * 3.14.0     2021-12-21 [?] Bioconductor
 P goftest                1.2-3      2021-10-07 [?] CRAN (R 4.1.0)
 P gplots                 3.1.1      2020-11-28 [?] CRAN (R 4.0.2)
 P graph                * 1.72.0     2021-10-26 [?] Bioconductor
 P graphlayouts           0.8.0      2022-01-03 [?] CRAN (R 4.1.0)
 P gridExtra              2.3        2017-09-09 [?] CRAN (R 4.1.0)
 P GSA                    1.03.1     2019-01-31 [?] CRAN (R 4.1.0)
 P GSEABase               1.56.0     2021-10-26 [?] Bioconductor
 P GSVA                   1.42.0     2021-10-26 [?] Bioconductor
 P gtable                 0.3.0      2019-03-25 [?] CRAN (R 4.1.0)
 P gtools                 3.9.2      2021-06-06 [?] 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 hgu133a.db             3.13.0     2022-01-24 [?] Bioconductor
 P hgu133plus2.db         3.13.0     2022-01-24 [?] Bioconductor
 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 HTMLUtils              0.1.7      2015-01-17 [?] 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 hwriter                1.3.2      2014-09-10 [?] 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 KEGGdzPathwaysGEO      1.32.0     2021-10-30 [?] Bioconductor
 P KEGGgraph              1.54.0     2021-10-26 [?] Bioconductor
 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 mathjaxr               1.4-0      2021-03-01 [?] CRAN (R 4.1.0)
 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 metap                  1.7        2021-12-16 [?] CRAN (R 4.1.0)
 P metapod                1.2.0      2021-10-26 [?] Bioconductor
 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 mnormt                 2.0.2      2020-09-01 [?] CRAN (R 4.0.2)
 P modelr                 0.1.8      2020-05-19 [?] CRAN (R 4.0.2)
 P multcomp               1.4-18     2022-01-04 [?] CRAN (R 4.1.0)
 P multtest               2.50.0     2021-10-26 [?] Bioconductor
 P munsell                0.5.0      2018-06-12 [?] CRAN (R 4.1.0)
 P mutoss                 0.1-12     2017-12-04 [?] CRAN (R 4.1.0)
 P mvtnorm                1.1-3      2021-10-08 [?] CRAN (R 4.1.0)
 P nlme                   3.1-153    2021-09-07 [?] CRAN (R 4.1.0)
 P numDeriv               2016.8-1.1 2019-06-06 [?] 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 org.Rn.eg.db           3.14.0     2022-01-24 [?] Bioconductor
 P PADOG                  1.36.0     2021-10-26 [?] 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 pathview             * 1.34.0     2021-10-26 [?] Bioconductor
 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 plotrix                3.8-2      2021-09-08 [?] 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 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 R.methodsS3            1.8.1      2020-08-26 [?] CRAN (R 4.0.2)
 P R.oo                   1.24.0     2020-08-26 [?] CRAN (R 4.0.2)
 P R.utils                2.11.0     2021-09-26 [?] CRAN (R 4.1.0)
 P R2HTML                 2.3.2      2016-06-23 [?] CRAN (R 4.1.0)
 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 rbibutils              2.2.7      2021-12-07 [?] 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 Rdpack                 2.1.3      2021-12-08 [?] 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 Rgraphviz              2.38.0     2021-10-26 [?] Bioconductor
 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 rngtools               1.5.2      2021-09-20 [?] 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 rsvd                   1.0.5      2021-04-16 [?] CRAN (R 4.1.0)
 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 safe                   3.34.0     2021-10-26 [?] Bioconductor
 P sandwich               3.0-1      2021-05-18 [?] CRAN (R 4.1.0)
 P sass                   0.4.0      2021-05-12 [?] CRAN (R 4.1.0)
 P ScaledMatrix           1.2.0      2021-10-26 [?] Bioconductor
 P scales                 1.1.1      2020-05-11 [?] CRAN (R 4.0.2)
 P scater               * 1.22.0     2021-10-26 [?] Bioconductor
 P scattermore            0.7        2020-11-24 [?] CRAN (R 4.1.0)
 P scran                * 1.22.1     2021-11-14 [?] Bioconductor
 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 sn                     2.0.1      2021-11-26 [?] CRAN (R 4.1.0)
 P SparseM              * 1.81       2021-02-18 [?] CRAN (R 4.1.0)
 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 statmod                1.4.36     2021-05-10 [?] CRAN (R 4.1.0)
 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 TFisher                0.2.0      2018-03-21 [?] CRAN (R 4.1.0)
 P TH.data                1.1-0      2021-09-27 [?] CRAN (R 4.1.0)
 P tibble               * 3.1.6      2021-11-07 [?] CRAN (R 4.1.0)
 P tidygraph              1.2.0      2020-05-12 [?] CRAN (R 4.0.2)
 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 tmvnsim                1.0-2      2016-12-15 [?] CRAN (R 4.1.0)
 P topGO                * 2.46.0     2021-10-26 [?] Bioconductor
 P tweenr                 1.0.2      2021-03-23 [?] 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 vipor                  0.4.5      2017-03-22 [?] CRAN (R 4.1.0)
 P viridis                0.6.2      2021-10-13 [?] CRAN (R 4.1.0)
 P viridisLite            0.4.0      2021-04-13 [?] CRAN (R 4.0.2)
 P vroom                  1.5.7      2021-11-30 [?] CRAN (R 4.1.0)
 P whisker                0.4        2019-08-28 [?] CRAN (R 4.0.2)
 P withr                  2.4.3      2021-11-30 [?] CRAN (R 4.1.0)
 P workflowr            * 1.7.0      2021-12-21 [?] CRAN (R 4.1.0)
 P xfun                   0.29       2021-12-14 [?] CRAN (R 4.1.0)
 P XML                    3.99-0.8   2021-09-17 [?] CRAN (R 4.1.0)
 P xml2                   1.3.3      2021-11-30 [?] CRAN (R 4.1.0)
 P xtable                 1.8-4      2019-04-21 [?] CRAN (R 4.1.0)
 P XVector                0.34.0     2021-10-26 [?] Bioconductor
 P yaml                   2.2.1      2020-02-01 [?] CRAN (R 4.0.2)
 P zlibbioc               1.40.0     2021-10-26 [?] Bioconductor
 P zoo                    1.8-9      2021-03-09 [?] CRAN (R 4.1.0)

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

 P ── Loaded and on-disk path mismatch.

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

9 References

Sim, Choon Boon, Belinda Phipson, Mark Ziemann, Haloom Rafehi, Richard J Mills, Kevin I Watt, Kwaku D Abu-Bonsrah, et al. 2021. “Sex-Specific Control of Human Heart Maturation by the Progesterone Receptor.” Circulation, March. 

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] EGSEA_1.22.0                pathview_1.34.0            
 [3] topGO_2.46.0                SparseM_1.81               
 [5] GO.db_3.14.0                graph_1.72.0               
 [7] gage_2.44.0                 org.Hs.eg.db_3.14.0        
 [9] AnnotationDbi_1.56.2        edgeR_3.36.0               
[11] limma_3.50.0                tidyHeatmap_1.7.0          
[13] paletteer_1.4.0             BiocParallel_1.28.3        
[15] glmGamPoi_1.6.0             clustree_0.4.4             
[17] ggraph_2.0.5                patchwork_1.1.1            
[19] SeuratObject_4.0.4          Seurat_4.0.6               
[21] scater_1.22.0               scran_1.22.1               
[23] scuttle_1.4.0               DropletUtils_1.14.1        
[25] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[27] Biobase_2.54.0              GenomicRanges_1.46.1       
[29] GenomeInfoDb_1.30.1         IRanges_2.28.0             
[31] S4Vectors_0.32.3            BiocGenerics_0.40.0        
[33] MatrixGenerics_1.6.0        matrixStats_0.61.0         
[35] glue_1.6.0                  here_1.0.1                 
[37] forcats_0.5.1               stringr_1.4.0              
[39] dplyr_1.0.7                 purrr_0.3.4                
[41] readr_2.1.1                 tidyr_1.1.4                
[43] tibble_3.1.6                ggplot2_3.3.5              
[45] tidyverse_1.3.1             BiocStyle_2.22.0           
[47] workflowr_1.7.0            

loaded via a namespace (and not attached):
  [1] rsvd_1.0.5                ica_1.0-2                
  [3] ps_1.6.0                  foreach_1.5.1            
  [5] lmtest_0.9-39             rprojroot_2.0.2          
  [7] crayon_1.4.2              rbibutils_2.2.7          
  [9] spatstat.core_2.3-2       MASS_7.3-53.1            
 [11] rhdf5filters_1.6.0        nlme_3.1-153             
 [13] backports_1.4.1           reprex_2.0.1             
 [15] rlang_0.4.12              XVector_0.34.0           
 [17] ROCR_1.0-11               readxl_1.3.1             
 [19] irlba_2.3.5               callr_3.7.0              
 [21] rjson_0.2.21              globaltest_5.48.0        
 [23] bit64_4.0.5               rngtools_1.5.2           
 [25] sctransform_0.3.3         parallel_4.1.0           
 [27] processx_3.5.2            vipor_0.4.5              
 [29] spatstat.sparse_2.1-0     R2HTML_2.3.2             
 [31] spatstat.geom_2.3-1       haven_2.4.3              
 [33] tidyselect_1.1.1          fitdistrplus_1.1-6       
 [35] XML_3.99-0.8              zoo_1.8-9                
 [37] org.Mm.eg.db_3.14.0       xtable_1.8-4             
 [39] magrittr_2.0.1            evaluate_0.14            
 [41] Rdpack_2.1.3              cli_3.1.0                
 [43] zlibbioc_1.40.0           sn_2.0.1                 
 [45] hwriter_1.3.2             doRNG_1.8.2              
 [47] rstudioapi_0.13           miniUI_0.1.1.1           
 [49] whisker_0.4               bslib_0.3.1              
 [51] rpart_4.1-15              mathjaxr_1.4-0           
 [53] GSA_1.03.1                KEGGdzPathwaysGEO_1.32.0 
 [55] shiny_1.7.1               GSVA_1.42.0              
 [57] BiocSingular_1.10.0       xfun_0.29                
 [59] clue_0.3-60               org.Rn.eg.db_3.14.0      
 [61] multtest_2.50.0           cluster_2.1.2            
 [63] caTools_1.18.2            tidygraph_1.2.0          
 [65] KEGGREST_1.34.0           ggrepel_0.9.1            
 [67] listenv_0.8.0             dendextend_1.15.2        
 [69] Biostrings_2.62.0         png_0.1-7                
 [71] future_1.23.0             withr_2.4.3              
 [73] bitops_1.0-7              ggforce_0.3.3            
 [75] plyr_1.8.6                cellranger_1.1.0         
 [77] PADOG_1.36.0              GSEABase_1.56.0          
 [79] dqrng_0.3.0               pillar_1.6.4             
 [81] gplots_3.1.1              GlobalOptions_0.1.2      
 [83] cachem_1.0.6              multcomp_1.4-18          
 [85] fs_1.5.2                  GetoptLong_1.0.5         
 [87] DelayedMatrixStats_1.16.0 vctrs_0.3.8              
 [89] ellipsis_0.3.2            generics_0.1.1           
 [91] metap_1.7                 tools_4.1.0              
 [93] beeswarm_0.4.0            munsell_0.5.0            
 [95] tweenr_1.0.2              DelayedArray_0.20.0      
 [97] fastmap_1.1.0             compiler_4.1.0           
 [99] abind_1.4-5               httpuv_1.6.5             
[101] sessioninfo_1.2.2         plotly_4.10.0            
[103] GenomeInfoDbData_1.2.7    gridExtra_2.3            
[105] lattice_0.20-45           deldir_1.0-6             
[107] mutoss_0.1-12             utf8_1.2.2               
[109] later_1.3.0               jsonlite_1.7.2           
[111] scales_1.1.1              ScaledMatrix_1.2.0       
[113] pbapply_1.5-0             sparseMatrixStats_1.6.0  
[115] renv_0.15.0-14            lazyeval_0.2.2           
[117] promises_1.2.0.1          doParallel_1.0.16        
[119] R.utils_2.11.0            goftest_1.2-3            
[121] checkmate_2.0.0           spatstat.utils_2.3-0     
[123] reticulate_1.22           sandwich_3.0-1           
[125] rmarkdown_2.11            cowplot_1.1.1            
[127] statmod_1.4.36            Rtsne_0.15               
[129] EGSEAdata_1.22.0          uwot_0.1.11              
[131] igraph_1.2.11             HDF5Array_1.22.1         
[133] plotrix_3.8-2             numDeriv_2016.8-1.1      
[135] survival_3.2-13           yaml_2.2.1               
[137] htmltools_0.5.2           memoise_2.0.1            
[139] locfit_1.5-9.4            graphlayouts_0.8.0       
[141] viridisLite_0.4.0         digest_0.6.29            
[143] assertthat_0.2.1          mime_0.12                
[145] RSQLite_2.2.9             future.apply_1.8.1       
[147] data.table_1.14.2         blob_1.2.2               
[149] R.oo_1.24.0               labeling_0.4.2           
[151] splines_4.1.0             rematch2_2.1.2           
[153] Rhdf5lib_1.16.0           RCurl_1.98-1.6           
[155] broom_0.7.11              hms_1.1.1                
[157] modelr_0.1.8              rhdf5_2.38.0             
[159] colorspace_2.0-2          mnormt_2.0.2             
[161] BiocManager_1.30.16       tmvnsim_1.0-2            
[163] ggbeeswarm_0.6.0          shape_1.4.6              
[165] sass_0.4.0                Rcpp_1.0.7               
[167] bookdown_0.24             RANN_2.6.1               
[169] mvtnorm_1.1-3             circlize_0.4.13          
[171] fansi_1.0.0               tzdb_0.2.0               
[173] parallelly_1.30.0         R6_2.5.1                 
[175] grid_4.1.0                ggridges_0.5.3           
[177] lifecycle_1.0.1           TFisher_0.2.0            
[179] bluster_1.4.0             leiden_0.3.9             
[181] jquerylib_0.1.4           safe_3.34.0              
[183] Matrix_1.4-0              TH.data_1.1-0            
[185] RcppAnnoy_0.0.19          RColorBrewer_1.1-2       
[187] iterators_1.0.13          htmlwidgets_1.5.4        
[189] beachmat_2.10.0           polyclip_1.10-0          
[191] rvest_1.0.2               ComplexHeatmap_2.10.0    
[193] mgcv_1.8-38               globals_0.14.0           
[195] hgu133plus2.db_3.13.0     KEGGgraph_1.54.0         
[197] codetools_0.2-18          lubridate_1.8.0          
[199] metapod_1.2.0             gtools_3.9.2             
[201] getPass_0.2-2             dbplyr_2.1.1             
[203] RSpectra_0.16-0           R.methodsS3_1.8.1        
[205] gtable_0.3.0              DBI_1.1.2                
[207] git2r_0.29.0              highr_0.9                
[209] tensor_1.5                httr_1.4.2               
[211] KernSmooth_2.23-20        vroom_1.5.7              
[213] stringi_1.7.6             reshape2_1.4.4           
[215] farver_2.1.0              annotate_1.72.0          
[217] viridis_0.6.2             Rgraphviz_2.38.0         
[219] DT_0.20                   xml2_1.3.3               
[221] BiocNeighbors_1.12.0      scattermore_0.7          
[223] bit_4.0.4                 spatstat.data_2.1-2      
[225] hgu133a.db_3.13.0         pkgconfig_2.0.3          
[227] HTMLUtils_0.1.7           knitr_1.37