Last updated: 2022-06-21

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Knit directory: paed-cf-cite-seq/

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Rmd 14ec446 Jovana Maksimovic 2022-06-21 wflow_publish(c("analysis/08_COMBO.cluster_macrophages.Rmd",
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html f3b7b92 Jovana Maksimovic 2022-06-16 Submission version

1 Load libraries

2 Load Data

Load the clustered and labelled scRNA-seq and CITE-seq data.

seu <- readRDS(file = here("data/SCEs/05_COMBO.clustered_annotated_macrophages_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$ENTREZID),]
seu <- seu[rownames(seu) %in% entrez$SYMBOL,]

3 Subcluster macrophages

Normalise and integrate data.

out <- here("data/SCEs/06_COMBO.macrophages_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]]

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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))

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DimHeatmap(seuInt, dims = 1:30, cells = 500, balanced = TRUE)

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4.2 Determine the ‘Dimensionality’ of the Dataset

ElbowPlot(seuInt, ndims = 30)

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5 Cluster the Cells

Examine cluster number and size with respect to resolution.

out <- here("data/SCEs/06_COMBO.macrophages_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
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Choose a resolution. Visualise UMAP.

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

Version Author Date
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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    5294  7241  2583  4024  2208  4370  2432  4077  2027  7064  2484  3694
NotSig  8279  6781 10589  8449 10752  7078  9895  9953 10611  6484 11200 10721
Up      2005  1556  2406  3105  2618  4130  3251  1548  2940  2030  1894  1163
         c12   c13   c14   c15   c16   c17   c18   c19   c20   c21   c22   c23
Down    2904  6800  1689  1417  1304  2416   889  1268   584  1921  2813  1627
NotSig 11321  6506 12822 11736 12614 11150  9993 12749 11762  8507 10583 12534
Up      1353  2272  1067  2425  1660  2012  4696  1561  3232  5150  2182  1417

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       4    54     1    13     2    20     9    42     5   294     5     2
NotSig 15565 15485 15569 15522 15563 15532 15535 15515 15550 15175 15545 15509
Up         9    39     8    43    13    26    34    21    23   109    28    67
         c12   c13   c14   c15   c16   c17   c18   c19   c20   c21   c22   c23
Down       6   413     0     2     0    11     1     1     0    60   221    62
NotSig 15550 15012 15569 15547 15503 15534 15355 15547 15530 14903 15134 15495
Up        22   153     9    29    75    33   222    30    48   615   223    21

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)
}

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Version Author Date
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6.2.2 Export Marker Genes per cluster

options(scipen=-1, digits = 6)
contnames <- colnames(mycont)
dirName <- here("output/marker-analysis/05-COMBO-macrophages")
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 
33623 features across 33161 samples within 5 assays 
Active assay: RNA (15578 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   12082635   645.3   21529594  1149.9   21529594  1149.9
Vcells 2650214870 20219.6 5613736589 42829.5 5613728169 42829.4

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),
    show_row_names = TRUE,
    column_names_gp = grid::gpar(fontsize = 10),
    column_title_gp = grid::gpar(fontsize = 12),
    row_names_gp = grid::gpar(fontsize = 8),
    row_title_gp = grid::gpar(fontsize = 12),
    column_title_side = "top",
    palette_value = circlize::colorRamp2(seq(-1, topMax, length.out = 256),
                                         viridis::magma(256)),
    heatmap_legend_param = list(direction = "vertical"))

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

7.4 Visualise ADTs of interest

adt <- read_csv(file = here("data/Proteins_macs_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",
    rect_gp = grid::gpar(col = "white", lwd = 1),
    show_row_names = TRUE,
    column_names_gp = grid::gpar(fontsize = 10),
    column_title_gp = grid::gpar(fontsize = 12),
    row_names_gp = grid::gpar(fontsize = 8),
    row_title_gp = grid::gpar(fontsize = 12),
    column_title_side = "top",
    palette_value = circlize::colorRamp2(seq(-1, topMax, length.out = 256),
                                         viridis::magma(256)),
    heatmap_legend_param = list(direction = "vertical"))

Version Author Date
f3b7b92 Jovana Maksimovic 2022-06-16

7.5 Visualise cytokines of interest

markers <- read_csv(file = here("data",
                                "macrophage_subcluster_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