Last updated: 2022-06-21

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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.

seu <- readRDS(file = here("data/SCEs/05_COMBO.clustered_annotated_tcells_diet.SEU.rds"))

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,]

3 Subcluster other cells

Normalise and integrate data.

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

if(!file.exists(out)){ 
  seuInt <- intDat(seu, type = "RNA", 
                   reference = unique(seu$capture[seu$experiment == 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.tcells_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.annotation.l1") +
  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     660   948  1162   542  1843   754   967   441   846   378   594   371
NotSig 14584 14647 13888 14927 13137 14523 14691 15167 14434 15187 14216 14809
Up       757   406   951   532  1021   724   343   393   721   436  1191   821
         c12   c13   c14   c15   c16   c17   c18
Down     206   219    61   935   190    50   103
NotSig 15504 14673 15650 14097 15303 14096 15270
Up       291  1109   290   969   508  1855   628

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       6     1     7     0    21     9     2     1     4     4    10     8
NotSig 15990 15994 15978 15983 15978 15980 15995 15990 15966 15987 15972 15978
Up         5     6    16    18     2    12     4    10    31    10    19    15
         c12   c13   c14   c15   c16   c17   c18
Down       0     3     0    14     7     2     6
NotSig 15988 15986 15975 15981 15967 15898 15971
Up        13    12    26     6    27   101    24

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-tcells")
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 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 
31490 features across 6462 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  12107202  646.6   21529605 1149.9  21529605 1149.9
Vcells 403801079 3080.8 1197607910 9137.1 997927824 7613.6

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",
    rect_gp = grid::gpar(col = "white", lwd = 1),
    palette_value = circlize::colorRamp2(seq(-1, topMax, length.out = 256),
                                         viridis::magma(256)),
    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_T-NK_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", 
                                "T-NK_subclusters_cytokines.csv"),
                    col_names = FALSE)
p <- DotPlot(seuInt, 
             features = markers$X1,
             cols = c("grey", "red"),
             dot.scale = 5,
             assay = "RNA",
             group.by = "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

Analysis of CF_BAL_Pilot & C133_Neeland Data Combined

T/NK Cells Sub-clustering

Jovana Maksimovic

June 21, 2022

1 Load libraries

2 Load Data

3 Subcluster other cells

4 Clustering

4.1 Perform Linear Dimensional Reduction

4.2 Determine the ‘Dimensionality’ of the Dataset

5 Cluster the Cells

5.1 Examine clusters

6 Identify Cluster Marker Genes

6.1 Test for Marker Genes using `limma`

6.2 Test relative to a threshold (TREAT)

6.2.1 Mean-difference Plots per Cluster

6.2.2 Export Marker Genes per cluster

6.2.3 Cluster marker gene dot plot

6.2.4 No. cells per cluster

7 Load protein data

7.1 Add to `Seurat` object

7.2 Load protein annotations

7.3 Visualise all ADTs

7.4 Visualise ADTs of interest

7.5 Visualise cytokines of interest

8 Session info

9 References

Analysis of CF_BAL_Pilot & C133_Neeland Data Combined

T/NK Cells Sub-clustering

Jovana Maksimovic

June 21, 2022

1 Load libraries

2 Load Data

3 Subcluster other cells

4 Clustering

4.1 Perform Linear Dimensional Reduction

4.2 Determine the ‘Dimensionality’ of the Dataset

5 Cluster the Cells

5.1 Examine clusters

6 Identify Cluster Marker Genes

6.1 Test for Marker Genes using limma

6.2 Test relative to a threshold (TREAT)

6.2.1 Mean-difference Plots per Cluster

6.2.2 Export Marker Genes per cluster

6.2.3 Cluster marker gene dot plot

6.2.4 No. cells per cluster

7 Load protein data

7.1 Add to Seurat object

7.2 Load protein annotations

7.3 Visualise all ADTs

7.4 Visualise ADTs of interest

7.5 Visualise cytokines of interest

8 Session info

9 References

6.1 Test for Marker Genes using `limma`

7.1 Add to `Seurat` object