Analysis of scRNA-seq & CITE-seq Data Combined

Sub-cluster Macrophages (Round 2)

workflowr

• Summary
• Checks
• Past versions

Last updated: 2022-12-19

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: e799f52

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 e799f52. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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

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

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/08_COMBO.cluster_macrophages_round2.Rmd) and HTML (docs/08_COMBO.cluster_macrophages_round2.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	e799f52	Jovana Maksimovic	2022-12-19	wflow_publish(c("analysis/emptyDrops.Rmd", "analysis/postprocess*.Rmd",
html	63f8ee8	Jovana Maksimovic	2022-12-15	Build site.
Rmd	916bafa	Jovana Maksimovic	2022-12-15	wflow_publish(c("analysis/.emptyDrops.Rmd", "analysis/postprocess_*.Rmd",
html	4368d1d	Jovana Maksimovic	2022-12-09	Build site.
Rmd	3e823ad	Jovana Maksimovic	2022-12-09	wflow_publish("analysis/08_COMBO.cluster_macrophages_round2.Rmd")

---

1 Load libraries

2 Load Data

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

seuInt <- readRDS(file = here("data/SCEs/06_COMBO.macrophages_clustered.SEU.rds"))
seuInt

An object of class Seurat 
33301 features across 33161 samples within 3 assays 
Active assay: integrated (3000 features, 3000 variable features)
 2 other assays present: RNA, SCT
 2 dimensional reductions calculated: pca, umap

2.1 Load manual annotations

labels <- read_csv(here("data/macrophage_subcluster_annotation_29.05.22.csv"))

seuInt@meta.data %>%
  left_join(labels %>%
              mutate(Annotation = ifelse(is.na(Annotation),
                                         "SUSPECT",
                                         Annotation),
                     Broad = ifelse(is.na(Broad),
                                         "SUSPECT",
                                         Broad)) %>%
              mutate(Cluster = as.factor(Cluster),
                     Annotation = as.factor(Annotation)),
            by = c("integrated_snn_res.1" = "Cluster")) -> ann 

ann %>% dplyr::pull(Annotation) -> seuInt$Annotation
ann %>% dplyr::pull(Broad) -> seuInt$Broad

seuInt$Annotation <- fct_drop(seuInt$Annotation)
seuInt$Broad <- fct_drop(seuInt$Broad)
seuInt

An object of class Seurat 
33301 features across 33161 samples within 3 assays 
Active assay: integrated (3000 features, 3000 variable features)
 2 other assays present: RNA, SCT
 2 dimensional reductions calculated: pca, umap

2.2 Check for doublet enrichment

The macro-T cluster expresses both macrophage and T-cell marker genes so we need to check if it is artefactual e.g. contains doublets. We have already removed a total of 3826 heterogenic, cross-sample doublets based on vireo and hashedDrops calls. However, those methods cannot detect heterotypic and homotypic within-sample doublets. We have also run scds and scDblFinder to detect putative within-sample doublets.

Load doublet detection results and match up with annotated cells.

e1Doublets <- readRDS(here("data/SCEs/experiment1_doublets.rds"))
e1Doublets$cell <- paste0("A-", e1Doublets$cell)
e2Doublets <- readRDS(here("data/SCEs/experiment2_doublets.rds"))
e2Doublets$cell <- paste0("B-", e2Doublets$cell)

doublets <- rbind(e1Doublets, e2Doublets)
m <- match(colnames(seuInt), doublets$cell)
doublets <- doublets[m,]
all(doublets$cell == colnames(seuInt))

[1] TRUE

The macro-T cluster is comprised of ~60% putative doublets.

table(doublets$scDblFinder.class == "doublet" & doublets$hybrid_call, 
      seuInt$Annotation) %>%
  data.frame %>% 
  group_by(Var2) %>%
  mutate(prop = Freq/sum(Freq)) %>%
  ungroup() %>%
  ggplot(aes(x = Var2, y = prop, fill = Var1)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  geom_hline(yintercept = 0.1, linetype = "dashed") +
  labs(fill = "Doublet",
       x = "Fine annotation",
       y = "Proportion") -> p1

table(doublets$scDblFinder.class == "doublet" & doublets$hybrid_call, 
      seuInt$Annotation) %>%
  data.frame %>% 
  ggplot(aes(x = Var2, y = Freq, fill = Var1)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  labs(fill = "Doublet",
       x = "Fine annotation",
       y = "Frequency") -> p2

(p2 | p1) + plot_layout(guides = "collect") &
  theme(legend.position = "bottom")

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

Calculate if doublets are statistically over-represented in any clusters using Fisher’s Exact Test.

tab <- table(doublets$scDblFinder.class == "doublet" & doublets$hybrid_call, 
      seuInt$Annotation)
dblStats <- table(doublets$scDblFinder.class == "doublet" & doublets$hybrid_call)

apply(tab, 2, function(x){
  dblFreq <- matrix(c(x[2], dblStats[2] - x[2], x[1], dblStats[1] - x[1]), 
                      nrow = 2,
                  dimnames = list(c("In cluster", "Not in cluster"),
                                  c("Doublet", "Singlet")))
  fisher.test(dblFreq, alternative = "greater")$p.value
}) -> pvals

pvals %>% 
  data.frame %>%
  rownames_to_column(var = "cell") %>%
  dplyr::rename("p.value" = ".") %>%
  mutate(FDR = p.adjust(p.value, method = "BH")) %>%
  ggplot(aes(y = -log10(FDR), x = cell,
             fill = FDR < 0.05)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  geom_col() 

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

Doublets are defined as cells that are called doublets by both scds and scDblFinder. Filter out all doublets and all the cells in the macro-T cluster, which are all likely to be doublets based on association.

keep <- !(doublets$scDblFinder.class == "doublet" & doublets$hybrid_call)
DefaultAssay(seuInt) <- "RNA"
seu <- DietSeurat(subset(seuInt, cells = which(keep)),
                  assays = "RNA")
seu

An object of class Seurat 
15578 features across 30847 samples within 1 assay 
Active assay: RNA (15578 features, 0 variable features)

rm(seuInt)
gc()

            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  11665366  623.0   21569357  1152.0   18138030   968.7
Vcells 388405267 2963.3 2457345909 18748.1 2532459186 19321.2

3 Subcluster macrophages

Normalise and integrate data.

out <- here("data/SCEs/07_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]]

Past versions of unnamed-chunk-12-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

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))

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

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

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

4.2 Determine the ‘Dimensionality’ of the Dataset

ElbowPlot(seuInt, ndims = 30)

Past versions of unnamed-chunk-15-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

5 Cluster the Cells

Examine cluster number and size with respect to resolution.

out <- here("data/SCEs/07_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)

Past versions of unnamed-chunk-16-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

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()

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

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))

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

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    4674  6777  3587  2477  1989  2258  4080  4202  1798  6527  2478  2231
NotSig  8834  7185  8641 10873 11034  9848  7059  9681 10730  6938 11624 11405
Up      2070  1616  3350  2228  2555  3472  4439  1695  3050  2113  1476  1942
         c12   c13   c14   c15   c16   c17   c18   c19   c20   c21   c22
Down    3484  6956  1317  1476  1297   758  1076  1921  1812  1890  2749
NotSig 10919  6439 12473 12964 11866 10163 12781 11362  8585 12120 10709
Up      1175  2183  1788  1138  2415  4657  1721  2295  5181  1568  2120

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    54    13     1     2    10    27    51     5   262     7     4
NotSig 15562 15483 15517 15566 15563 15526 15515 15501 15552 15211 15551 15543
Up        10    41    48    11    13    42    36    26    21   105    20    31
         c12   c13   c14   c15   c16   c17   c18   c19   c20   c21   c22
Down       3   472     0     0     3     1     2    19    55    54   224
NotSig 15505 14924 15512 15569 15541 15375 15543 15518 14911 15520 15132
Up        70   182    66     9    34   202    33    41   612     4   222

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)
}

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

Past versions of unnamed-chunk-21-2.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

Past versions of unnamed-chunk-21-3.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

6.2.2 Export Marker Genes per cluster

options(scipen=-1, digits = 6)
contnames <- colnames(mycont)
dirName <- here("output/marker-analysis/06-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")

Past versions of unnamed-chunk-23-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

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()

Past versions of unnamed-chunk-24-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

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 
33440 features across 30847 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   12136289   648.2   21569357  1152.0   21569357  1152.0
Vcells 2454265713 18724.6 5180913551 39527.3 5180905326 39527.2

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")

Past versions of unnamed-chunk-28-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

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")) 

Past versions of unnamed-chunk-29-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

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")) 

Past versions of unnamed-chunk-30-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

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

Past versions of unnamed-chunk-31-1.png

Version	Author	Date
4368d1d	Jovana Maksimovic	2022-12-09

8 Session info

The analysis and this document were prepared using the following software (click triangle to expand)

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.1.0 (2021-05-18)
 os       CentOS Linux 7 (Core)
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_AU.UTF-8
 ctype    en_AU.UTF-8
 tz       Australia/Melbourne
 date     2022-12-19
 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.

Session information

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