Dendrogram for similarity across cell types
12 September, 2025
# **MODIFY THIS CHUNK**
project_id <- "HDMA-public" # determines the name of the cache folder
doc_id <- "02-global_analysis/02" # determines name of the subfolder of `figures` where pngs/pdfs are saved
out <- here::here("output/", doc_id); dir.create(out, recursive = TRUE)
figout <- here::here("figures/", doc_id, "/")
cache <- paste0("~/scratch/cache/", project_id, "/", doc_id, "/")1 Overview
Construct a dendrogram over clusters.
2 Set up
# libraries
library(here)
library(dplyr)
library(tidyr)
library(ggplot2)
library(purrr)
library(glue)
library(readr)
library(tibble)
library(stringr)
library(Seurat)
library(cowplot)
library(data.table)
library(pvclust)
library(dendextend)
library(ArchR)
library(here)
# shared project scripts/functions
source(here::here("code/utils/plotting_config.R"))
source(here::here("code/utils/matrix_helpers.R"))
source(here::here("code/utils/seurat_helpers.R"))
source(here::here("code/utils/sj_scRNAseq_helpers.R"))
source(here::here("code/utils/hdma_palettes.R"))
ggplot2::theme_set(theme_BOR())set.seed(100)3 Load metadata
Get organ metadata:
organ_meta <- read_tsv(here::here("code/02-global_analysis/01-organs_keep.tsv"))## Rows: 12 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): organcode, organ, iteration
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.organ_meta## # A tibble: 12 × 3
## organcode organ iteration
## <chr> <chr> <chr>
## 1 AG Adrenal v4
## 2 BR Brain v4
## 3 EY Eye v6
## 4 HT Heart v4
## 5 LI Liver v4
## 6 LU Lung v8
## 7 MU Muscle v4
## 8 SK Skin v4
## 9 SP Spleen v4
## 10 ST StomachEsophagus v2
## 11 TM Thymus v4
## 12 TR Thyroid v2Set up cluster metadata, which we’ll use to label the dendrogram:
cluster_meta <- map_dfr(list.files(here::here("output/01-preprocessing/02/shared/meta"),
pattern = "*_meta_cluster.txt", full.names = TRUE),
~ fread(.x, data.table = FALSE)) %>%
mutate(Cluster = glue("{organ_code}_{L1_clusterID}")) %>%
mutate(Cluster_labelled = glue("{organ_code}_{L1_clusterID}_{L3_clusterName}")) %>%
left_join(enframe(cmap_organ, "organ", "Color")) %>%
left_join(enframe(cmap_compartment, "L0_clusterName", "Color_compartment")) %>%
tibble::column_to_rownames(var = "Cluster")## Joining with `by = join_by(organ)`
## Joining with `by = join_by(L0_clusterName)`Load cell metadata:
load(here::here("output/02-global_analysis/01/cell_meta_all.Rda"))Load sample metadata:
sample_meta <- read_csv(here::here("output/01-preprocessing/02/shared/SampleMetadata.csv"))## Rows: 130 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (14): SampleName, R1_BC_Row, StorageCode, TubeLabel, Organ_Desc, Organ, ...
## dbl (5): Batch, BioID, PCW, PCD, Total cells into splitpool (thousands)
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.4 Cluster average expression
To be able to assess some global relationships between all clusters, let’s take average expression across cells in each cluster:
# for each organ, load up the RNA object and calculate cluster mean expression across all genes
for (i in 1:nrow(organ_meta)) {
print(organ_meta$organ[i])
seurat_path <- sprintf(here::here("output/01-preprocessing/02/%s/rna_preprocess_output/RNA_obj_clustered_final.rds"), organ_meta$organ[i])
if (file.exists(seurat_path) & !file.exists(glue("{out}/{organ_meta$organcode[i]}_avg_exp.decontx.Rds"))) {
obj <- readRDS(seurat_path)
# prepend clusters with organ code
obj$Clusters <- paste0(organ_meta$organcode[i], "_", obj$Clusters)
Idents(obj) <- "Clusters"
# get average expression for both decontx and RNA assays
obj_avg <- Seurat::AverageExpression(obj, return.seurat = FALSE, assays = "decontX")$decontX
saveRDS(obj_avg, glue("{out}/{organ_meta$organcode[i]}_avg_exp.decontx.Rds"))
# RNA data isn't log-normalized
DefaultAssay(obj) <- "RNA"
obj <- NormalizeData(obj)
obj_avg <- Seurat::AverageExpression(obj, return.seurat = FALSE, assays = "RNA")$RNA
saveRDS(obj_avg, glue("{out}/{organ_meta$organcode[i]}_avg_exp.rna.Rds"))
} else {
next()
}
}5 Dendrogram based on RNA
Here, we can approximately follow a method used by Tasic et al 2018
and Yao et al 2021 in Allen Institute publications, where dendrograms
are computed by using the mean cluster expression on top pairwise
differentially expressed genes. For construction of the dendrogram, we
will consult the build_dend() function used in the
scrattch.hicat package: https://github.com/AllenInstitute/scrattch.hicat/blob/7ccfbc24e3a51326740d08b5b306b18afeb890d9/R/dendro.R#L49
Instead of using pairwise differentially expressed genes, we will use the top cluster markers in the one-vs-all differential expression analyses computed for each organ.
n <- 20
top_markers <- map2(organ_meta$organ, organ_meta$iteration, function(organ, iter) {
# if data for that organ exists,
if (!is.na(iter)) {
# load the markers corresponding to the final iteration
markers <- fread(
file.path(here::here("output/01-preprocessing/02/"), organ, glue("rna_preprocess_output/cluster_markers/cluster_markers_SCTdecontXnorm_{iter}.tsv")),
data.table = FALSE)
# for each cluster, filter to only positive markers, and select the top 20 based on LFC
markers %>%
group_by(cluster) %>%
filter(avg_log2FC > 0) %>%
top_n(n, avg_log2FC) %>%
pull(gene) %>%
unique()
}
})
top_markers <- top_markers %>% unlist() %>% unique()
length(top_markers)## [1] 1652saveRDS(top_markers, file = glue("{out}/top_markers.Rds"))avg_exp_decontx_list <- map(list.files(out, pattern = "*_avg_exp.decontx.Rds", full.names = TRUE), ~ readRDS(.x))
map(avg_exp_decontx_list, dim)## [[1]]
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## [1] 26163 12# subset each mat to the collection of marker genes, to save time in the merge
avg_exp_decontx_subset_list <- map(avg_exp_decontx_list, ~ .x[rownames(.x) %in% top_markers, ])
map(avg_exp_decontx_subset_list, dim)## [[1]]
## [1] 1636 10
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## [1] 1646 12#' A version of base::merge with some handy defaults (bind columns of different
#' matrices, according to their rownames, allowing for differences in the rows
#' present in the two dfs)
#'
#' @param m1 and m2, data frames to merge by rownames
merge2 <- function(m1, m2) {
# all controls whether or not to keep genes (rows) that apppear in some matrices
# but not others. We keep them and fill missing values with NA. This is to allow
# keeping genes that might be highly organ specific.
merge(m1, m2, by = "row.names", all = TRUE) %>%
# merge returns a df where the first column contains the rownames, here
# we just move it out to allow for subsequent merges
tibble::column_to_rownames("Row.names")
}
# combine all avg exp matrices
avg_exp_decontx_mat_subset <- Reduce(merge2, avg_exp_decontx_subset_list)
dim(avg_exp_decontx_mat_subset)## [1] 1652 203any(is.na(avg_exp_decontx_mat_subset))## [1] TRUEavg_exp_decontx_mat_subset[is.na(avg_exp_decontx_mat_subset)] <- 0
any(is.na(avg_exp_decontx_mat_subset))## [1] FALSE# avg_exp_rna_mat <- Reduce(merge2, avg_exp_rna_list)
# dim(avg_exp_rna_mat)
# any(is.na(avg_exp_rna_mat))
saveRDS(avg_exp_decontx_mat_subset, file = glue("{out}/avg_expr_marker_subset.decontx.Rds"))Dendrogram helpers:
# adapting from scrattch.hicat
# https://github.com/AllenInstitute/scrattch.hicat/blob/7ccfbc24e3a51326740d08b5b306b18afeb890d9/R/dendro.R#L49
pvclust_show_signif_gradient <- function(dend, pvclust_obj, signif_type = c("bp", "au"),
signif_col_fun = NULL, ...) {
signif_type <- match.arg(signif_type)
pvalue_per_node <- pvclust_obj$edges[[signif_type]]
ord <- rank(get_branches_heights(dend, sort = FALSE))
pvalue_per_node <- pvalue_per_node[ord]
signif_col <- signif_col_fun(100)
pvalue_by_all_nodes <- rep(NA, nnodes(dend))
ss_leaf <- which_leaf(dend)
pvalue_by_all_nodes[!ss_leaf] <- pvalue_per_node
pvalue_by_all_nodes <- na_locf(pvalue_by_all_nodes)
the_cols <- signif_col[round(pvalue_by_all_nodes * 100)]
signif_lwd = seq(0.5,2,length.out=100)
the_lwds = signif_lwd[round(pvalue_by_all_nodes * 100)]
dend = dend %>%
assign_values_to_branches_edgePar(the_cols, "col") %>%
assign_values_to_branches_edgePar(the_lwds, "lwd") %>%
assign_values_to_branches_edgePar(pvalue_by_all_nodes, "conf")
}
#' A distance function for Spearman rank correlation
#' from https://davetang.org/muse/2010/11/26/hierarchical-clustering-with-p-values-using-spearman/
spearman <- function(x, ...) {
x <- as.matrix(x)
res <- as.dist(1 - cor(x, method = "spearman", use = "everything"))
res <- as.dist(res)
attr(res, "method") <- "spearman"
return(res)
}
build_dend <- function(mat, n_boot = 100, distance = spearman, hclust = "complete") {
pvclust_res <- pvclust::pvclust(mat,
nboot = n_boot,
method.dist = distance,
method.hclust = hclust)
dend <- as.dendrogram(pvclust_res$hclust)
# dend <- dend %>% pvclust_show_signif_gradient(
# pvclust_res,
# signif_type = "bp",
# signif_col_fun = colorRampPalette(c("gray80", "gray50", "black")))
return(list(dend = dend, pvclust_res = pvclust_res))
}Construct dendrogram, first for the decontX data:
dim(avg_exp_decontx_mat_subset)
# get correlation matrix
cor_decontx <- cor(avg_exp_decontx_mat_subset, method = "spearman")
# build dendrogram
dend_res_decontx <- build_dend(avg_exp_decontx_mat_subset, distance = spearman, hclust = "average", n_boot = 1000)
save(avg_exp_decontx_mat_subset, cor_decontx, dend_res_decontx,
file = glue("{out}/dend_res_decontx.Rda"))load(glue("{out}/dend_res_decontx.Rda"))Label and color the leaves:
dend_labeled_decontx <- dend_res_decontx$dend
labels(dend_labeled_decontx) <- cluster_meta[labels(dend_labeled_decontx), "Cluster_labelled"]
labels_colors(dend_labeled_decontx) <- cluster_meta[colnames(cor_decontx), ]$Color[order.dendrogram(dend_labeled_decontx)]
dend_labeled_decontx2 <- dend_res_decontx$dend
labels(dend_labeled_decontx2) <- cluster_meta[labels(dend_labeled_decontx2), "Cluster_labelled"]
labels_colors(dend_labeled_decontx2) <- cluster_meta[colnames(cor_decontx), ]$Color_compartment[order.dendrogram(dend_labeled_decontx2)]Pvclust results and dendrogram with tree colored by significance of the branches:
par(mar=c(4,4,4,20))
plot(dend_res_decontx$pvclust_res, horiz = TRUE)
par(mar=c(20,4,4,4))
plot(dend_labeled_decontx, horiz = FALSE)
legend("top", legend = names(cmap_organ), fill = cmap_organ, horiz = TRUE)
par(mar=c(20,4,4,4))
plot(dend_labeled_decontx2, horiz = FALSE)
legend("topright", legend = names(cmap_compartment), fill = cmap_compartment)
Plot the global correlation matrix:
hm_fun <- purrr::partial(pheatmap::pheatmap,
mat = cor_decontx[
rev(order.dendrogram(dend_labeled_decontx)),
rev(order.dendrogram(dend_labeled_decontx))],
main = "Correlations; order from bootstrapped tree; mouse only",
cluster_rows = FALSE,
cluster_cols = FALSE,
annotation_col = cluster_meta %>% dplyr::select(organ),
annotation_colors = list("organ" = cmap_organ),
# use the project's RNA-specific palette
color = colorRampPalette(cmap_rna)(n=100),
scale = "none",
breaks = seq(0, 1, 0.01),
border_color = NA,
cellwidth = 13,
cellheight = 13)
# make png/pdf
hm_fun(filename = glue("{figout}/cor_heatmap.decontx.pdf"))
hm_fun(filename = glue("{figout}/cor_heatmap.decontx.png"))
knitr::include_graphics(glue("{figout}/cor_heatmap.decontx.png"))
Let’s layer on additional information:
cluster_meta2 <- cluster_meta %>%
tibble::rownames_to_column("Cluster") %>%
# get the order from the dendrogram
left_join(data.frame(Cluster = colnames(cor_decontx)[order.dendrogram(dend_labeled_decontx)]) %>%
tibble::rowid_to_column("Order"),
by = "Cluster") %>%
# order by dendro order
arrange(Order) %>%
mutate(Cluster_labelled = factor(Cluster_labelled, levels = unique(.$Cluster_labelled)))
write_tsv(cluster_meta2, glue("{out}/cluster_metadata_dend_order.tsv"))Set up the plot:
# set theme
theme_d <- function() {
theme(
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.x = element_text(angle = -90, size = 20, hjust = 0),
panel.border = element_blank(),
legend.position = "right", # changed this from bottom to right for proper grid alignment
legend.text = element_text(angle = 0, hjust = 0, size = 20))
}
p1 <- cell_meta_rna %>%
left_join(cluster_meta2 %>% dplyr::select(Cluster, Cluster_labelled), by = "Cluster") %>%
ggplot(aes(x = Cluster_labelled)) +
geom_bar(aes(fill = PCW), position = "fill") +
scale_fill_manual(values = cmap_pcw) +
theme_d() +
coord_flip() +
ggtitle("PCW")
p2 <- cluster_meta2 %>%
ggplot(aes(x = Cluster_labelled, y = median_numi)) +
geom_col(aes(fill = median_numi)) +
# geom_raster(aes(fill = median_numi)) +
scale_fill_gradientn(colors = ylrd) +
theme_d() +
coord_flip() +
ggtitle("median UMIs/cell") +
ylab(NULL)
p3 <- cluster_meta2 %>%
ggplot(aes(x = Cluster_labelled, y = median_ngene)) +
geom_col(aes(fill = median_ngene)) +
# geom_raster(aes(fill = median_ngene)) +
scale_fill_gradientn(colors = ylrd) +
theme_d() +
coord_flip() +
ggtitle("median # genes/cell") +
ylab(NULL)
p4 <- cluster_meta2 %>%
ggplot(aes(x = Cluster_labelled, y = median_nfrags)) +
geom_col(aes(fill = median_nfrags)) +
# geom_raster(aes(fill = median_nfrags)) +
scale_fill_gradientn(colors = ylrd) +
theme_d() +
coord_flip() +
ggtitle("median \n# fragments/cell") +
ylab(NULL)
p5 <- cluster_meta2 %>%
ggplot(aes(x = Cluster_labelled, y = median_tss)) +
geom_col(aes(fill = median_tss)) +
# geom_raster(aes(fill = median_tss)) +
scale_fill_gradientn(colors = ylrd) +
theme_d() +
coord_flip() +
ggtitle("median \nTSS enrichment") +
ylab(NULL)
p6 <- cluster_meta2 %>%
ggplot(aes(x = Cluster_labelled, y = 1)) +
geom_col(aes(fill = L0_clusterName)) +
scale_fill_manual(values = cmap_compartment) +
theme_d() +
coord_flip() +
ggtitle("Compartment") +
ylab(NULL)
p7 <- cluster_meta2 %>%
ggplot(aes(x = Cluster_labelled, y = 1)) +
geom_col(aes(fill = organ)) +
scale_fill_manual(values = cmap_organ) +
theme_d() + theme(legend.position = "none") +
coord_flip() +
ggtitle("Organ") +
ylab(NULL)
meta_withbioid <- cell_meta_rna %>%
left_join(cluster_meta2 %>% dplyr::select(Cluster, Cluster_labelled), by = "Cluster") %>%
left_join(sample_meta %>% dplyr::select(Sample=SampleName, BioID))## Joining with `by = join_by(Sample)`meta_withbioid$BioID <- as.factor(meta_withbioid$BioID)
pal <- colorRampPalette(c("lightgray", "orange", "brown"))(23)
p8 <- meta_withbioid %>%
ggplot(aes(x = Cluster_labelled)) +
geom_bar(aes(fill = BioID), position = "fill") +
scale_fill_manual(values=pal) +
theme_d() +
coord_flip() +
ggtitle("BioID")
# we have orders of magnitudes different # of cells in some clusters,
# so let's put these on a log10 scale
p0 <- cluster_meta2 %>%
ggplot(aes(x = Cluster_labelled, y = log10(ncell))) +
geom_col(aes(fill = organ)) +
scale_x_discrete(position = "top") +
scale_fill_manual(values = cmap_organ) +
theme(panel.border = element_blank(), legend.position = "right") +
coord_flip() +
ggtitle("# cells")
plot_grid(p1, p8, p2, p3, p4, p5, p6, p7, p0, nrow = 1,
rel_widths = c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2),
align = "h", axis = "tb")
6 Visualize the top marker heatmap
# scale
avg_exp_decontx_mat_subset_scaled <- avg_exp_decontx_mat_subset %>%
# genes are in rows; for each gene, scale expression to [0, 1]
apply(1, scales::rescale) %>%
t()Loop through clusters in dendrogram order to get their top marker genes, to try and get a diagonal heatmap:
# get an order for marker genes based on the dendrogram order. First we will simply
# load the markers, as before, but without getting unique genes, and keeping
# cluster from which each marker originated
n <- 20
top_markers_df <- map2_dfr(organ_meta$organ, organ_meta$iteration, function(organ, iter) {
# if data for that organ exists,
if (!is.na(iter)) {
# load the markers corresponding to the final iteration
markers <- fread(
file.path(here::here("output/01-preprocessing/02/"), organ, glue("rna_preprocess_output/cluster_markers/cluster_markers_SCTdecontXnorm_{iter}.tsv")),
data.table = FALSE)
# for each cluster, filter to only positive markers, and select the top 20 based on LFC
markers %>%
group_by(cluster) %>%
filter(avg_log2FC > 0) %>%
top_n(n, avg_log2FC) %>%
tibble::add_column(organ = organ, .before = 1)
}
})
head(top_markers_df)## # A tibble: 6 × 8
## # Groups: cluster [1]
## organ p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 Adrenal 0 2.86 0.998 0.55 0 0 NPC1
## 2 Adrenal 0 2.46 0.904 0.152 0 0 C1orf54
## 3 Adrenal 0 2.31 0.99 0.508 0 0 GALC
## 4 Adrenal 0 2.26 0.989 0.271 0 0 USP36
## 5 Adrenal 0 2.05 1 0.689 0 0 GRK5
## 6 Adrenal 5.54e-305 1.91 1 0.664 1.40e-300 0 CDK8# reformat df
top_markers_df <- top_markers_df %>%
left_join(cluster_meta2, by = c("organ" = "organ", "cluster" = "L1_clusterID")) %>%
dplyr::select(-cluster)## Adding missing grouping variables: `cluster`# arrange by Order, then pull out markers
markers_ordered_unique <- top_markers_df %>% arrange(Order) %>% group_by(Cluster) %>% pull(gene) %>% unique()
length(markers_ordered_unique)## [1] 1652all(is.numeric(avg_exp_decontx_mat_subset_scaled[markers_ordered_unique, cluster_meta2$Cluster]))## [1] TRUEcluster_meta2 <- as.data.frame(cluster_meta2)
rownames(cluster_meta2) <- cluster_meta2$Cluster
# make a function to plot the heatmap in dendrogram order, and with markers ordered diagonally
marker_hm_fun <- purrr::partial(
pheatmap::pheatmap,
mat = avg_exp_decontx_mat_subset_scaled[markers_ordered_unique, cluster_meta2$Cluster],
border_color = NA,
cluster_cols = FALSE,
cluster_rows = FALSE,
annotation_col = cluster_meta2 %>% dplyr::select(organ),
annotation_colors = list("organ" = cmap_organ),
# use the project's RNA-specific palette
color = colorRampPalette(cmap_rna)(n=100),
show_rownames = TRUE,
cellwidth = 13,
cellheight = 5,
main = "Average expression of top 20 marker genes per cluster, scaled to [0,1] across clusters")
# make png/pdf
marker_hm_fun(filename=paste0(figout, "/marker_heatmap.decontx.pdf"))
# marker_hm_fun(filename = glue("{figout}/marker_heatmap.decontx.png"))
#
# knitr::include_graphics(glue("{figout}/marker_heatmap.decontx.png"))pheatmap::pheatmap(
mat = avg_exp_decontx_mat_subset_scaled[markers_ordered_unique, cluster_meta2$Cluster],
border_color = NA,
cluster_cols = FALSE,
cluster_rows = FALSE,
# annotation_col = cluster_meta2 %>% dplyr::select(organ),
# annotation_colors = list("organ" = cmap_organ),
# use the project's RNA-specific palette
color = colorRampPalette(cmap_rna)(n=100),
show_rownames = TRUE,
cellwidth = 8,
cellheight = 4,
main = "Average expression of top 20 marker genes per cluster, scaled to [0,1] across clusters")
### manual selection of marker genes inspect the marker genes in a few
cell groups of interest
top_markers_df[grep("hepatocyte", top_markers_df$L3_clusterName),"gene"] %>%
table %>% as.data.frame %>% dplyr::arrange(desc(Freq))## . Freq
## 1 A2M 2
## 2 AHSG 2
## 3 AKR1C1 2
## 4 C3 2
## 5 C5 2
## 6 CYP3A5 2
## 7 FGA 2
## 8 FGB 2
## 9 FGG 2
## 10 ITIH1 2
## 11 MIR122HG 2
## 12 PTP4A1 2
## 13 SLC2A2 2
## 14 SLC39A14 2
## 15 SLCO1B3-SLCO1B7 2
## 16 AFP 1
## 17 AGMO 1
## 18 CASC19 1
## 19 CPT1A 1
## 20 CTH 1
## 21 EFNA1 1
## 22 ENSG00000228697 1
## 23 ENSG00000230490 1
## 24 ENSG00000230647 1
## 25 ENSG00000231424 1
## 26 ENSG00000284418 1
## 27 ENSG00000289368 1
## 28 FN1 1
## 29 GAREM1 1
## 30 GAS2 1
## 31 GLUD1 1
## 32 GPC3 1
## 33 HAL 1
## 34 HMGCS1 1
## 35 HNF1A-AS1 1
## 36 HSP90AA1 1
## 37 HSPD1 1
## 38 HSPH1 1
## 39 ITIH2 1
## 40 LDLR 1
## 41 LINC00470 1
## 42 LINC00907 1
## 43 LINC01146 1
## 44 LINC02027 1
## 45 LINC02532 1
## 46 MEG9 1
## 47 NLGN4Y 1
## 48 NPSR1-AS1 1
## 49 PCK1 1
## 50 PIK3C2G 1
## 51 POR 1
## 52 PPARGC1A 1
## 53 RAPH1 1
## 54 SIK2 1
## 55 SLC22A25 1
## 56 SLC22A9 1
## 57 SLC2A9 1
## 58 SLC7A2 1
## 59 SMOC1 1
## 60 SNAP25-AS1 1
## 61 SNHG14 1
## 62 SOX5 1
## 63 TLE1 1
## 64 TTC39C 1
## 65 ZNF331 1top_markers_df[grep("endothelial", top_markers_df$L3_clusterName),"gene"] %>%
table %>% as.data.frame %>% dplyr::arrange(desc(Freq))## . Freq
## 1 FLT1 7
## 2 PTPRB 7
## 3 MECOM 6
## 4 ADGRL4 5
## 5 CALCRL 5
## 6 KDR 5
## 7 LDB2 5
## 8 PECAM1 5
## 9 ANO2 4
## 10 ARHGAP29 4
## 11 DACH1 4
## 12 EMCN 4
## 13 ETS1 4
## 14 GALNT18 4
## 15 RAPGEF5 4
## 16 SHANK3 4
## 17 ATP11A 3
## 18 CADPS2 3
## 19 CPM 3
## 20 DIPK2B 3
## 21 FGD4 3
## 22 FLI1 3
## 23 NALF1 3
## 24 A2M 2
## 25 ADAMTS9 2
## 26 ARHGAP18 2
## 27 ARL15 2
## 28 CHRM3 2
## 29 COL4A1 2
## 30 DAPK1 2
## 31 DOCK4 2
## 32 ENSG00000289873 2
## 33 EPAS1 2
## 34 FENDRR 2
## 35 HSPG2 2
## 36 KHDRBS2 2
## 37 MAGI1 2
## 38 NEDD9 2
## 39 PREX2 2
## 40 PTPRM 2
## 41 RGS6 2
## 42 SH3RF3 2
## 43 SOX5 2
## 44 STOX2 2
## 45 TEK 2
## 46 TTC6 2
## 47 VWF 2
## 48 ABCB1 1
## 49 ADAMTSL1 1
## 50 ADGRB3 1
## 51 AKAP12 1
## 52 BMPER 1
## 53 BTNL9 1
## 54 CABLES1 1
## 55 CCBE1 1
## 56 COBLL1 1
## 57 COL15A1 1
## 58 COL27A1 1
## 59 CSGALNACT1 1
## 60 CTNNA3 1
## 61 DLC1 1
## 62 DOCK9 1
## 63 EBF1 1
## 64 ELMO1 1
## 65 ENSG00000226149 1
## 66 ENSG00000226197 1
## 67 ENSG00000234810 1
## 68 ENSG00000251600 1
## 69 ENSG00000287042 1
## 70 ERG 1
## 71 EYS 1
## 72 FAM184A 1
## 73 FAT3 1
## 74 FBXL7 1
## 75 FGFR2 1
## 76 FLRT2 1
## 77 FN1 1
## 78 FOXP2 1
## 79 FREM2 1
## 80 FRMD4B 1
## 81 GPC5 1
## 82 IGFBP5 1
## 83 IGFBP7 1
## 84 ITGA1 1
## 85 ITGA8 1
## 86 KALRN 1
## 87 KCNIP4 1
## 88 LEF1 1
## 89 LINC01331 1
## 90 LINC02147 1
## 91 LRMDA 1
## 92 MCTP1 1
## 93 MEIS1 1
## 94 MIR4435-2HG 1
## 95 MYLK 1
## 96 MYO1B 1
## 97 NRG3 1
## 98 OIT3 1
## 99 PDE3B 1
## 100 PIEZO2 1
## 101 PIK3C2G 1
## 102 PLEKHG1 1
## 103 PLEKHH2 1
## 104 PRKCH 1
## 105 RBPMS 1
## 106 RNF220 1
## 107 SEL1L3 1
## 108 SEMA5A 1
## 109 SEPTIN9 1
## 110 SLC7A1 1
## 111 SNED1 1
## 112 ST6GAL1 1
## 113 ST6GALNAC3 1
## 114 ST8SIA6 1
## 115 STAB1 1
## 116 STAB2 1
## 117 TFRC 1
## 118 TIAM1 1
## 119 TTN 1
## 120 UNC5C 1
## 121 UTRN 1top_markers_df[grep("CM", top_markers_df$L3_clusterName),"gene"] %>%
table %>% as.data.frame %>% dplyr::arrange(desc(Freq))## . Freq
## 1 DAB1 2
## 2 TRDN-AS1 2
## 3 ACTC1 1
## 4 ACTN2 1
## 5 ADAMTS6 1
## 6 AGBL4 1
## 7 ANGPT1 1
## 8 ANKRD1 1
## 9 ANLN 1
## 10 APOLD1 1
## 11 ATAD2 1
## 12 ATP5F1B 1
## 13 B4GALNT3 1
## 14 BARD1 1
## 15 BRINP3 1
## 16 BRIP1 1
## 17 CACNB2 1
## 18 CAMK1D 1
## 19 CCDC54-AS1 1
## 20 CDIN1 1
## 21 CENPK 1
## 22 CENPP 1
## 23 CEP128 1
## 24 CKM 1
## 25 COL11A1 1
## 26 CRYAB 1
## 27 CSMD1 1
## 28 CYP2J2 1
## 29 DES 1
## 30 DIAPH3 1
## 31 DPF3 1
## 32 DSP 1
## 33 DTL 1
## 34 EEF1A1 1
## 35 EEF2 1
## 36 ENSG00000253369 1
## 37 ENSG00000257252 1
## 38 ENSG00000285961 1
## 39 ENSG00000286619 1
## 40 ENSG00000287999 1
## 41 ENSG00000288659 1
## 42 ENSG00000289309 1
## 43 EPHA4 1
## 44 EZH2 1
## 45 FANCA 1
## 46 FANCI 1
## 47 FHL2 1
## 48 FLNC 1
## 49 FRMD5 1
## 50 GAPDH 1
## 51 GCNT2 1
## 52 HELLS 1
## 53 HSP90AA1 1
## 54 HSP90AB1 1
## 55 HSPA8 1
## 56 HSPB7 1
## 57 KCNH7 1
## 58 KCNJ3 1
## 59 KCNQ5 1
## 60 KIDINS220 1
## 61 KIF26B 1
## 62 KNTC1 1
## 63 L3MBTL4 1
## 64 LDB3 1
## 65 LINC00923 1
## 66 LINC01505 1
## 67 LINC02208 1
## 68 LINC02899 1
## 69 LRRC2 1
## 70 LRRTM3 1
## 71 MARCHF11 1
## 72 MGAT4C 1
## 73 MIR924HG 1
## 74 MMS22L 1
## 75 MYBPC3 1
## 76 MYH6 1
## 77 MYL2 1
## 78 MYL3 1
## 79 MYL7 1
## 80 MYO18B 1
## 81 NAV1 1
## 82 NES 1
## 83 NEXN 1
## 84 NLGN1 1
## 85 NPPA 1
## 86 NTM 1
## 87 NUDT4 1
## 88 PACRG 1
## 89 PAM 1
## 90 PCDH7 1
## 91 PHACTR1 1
## 92 PLCL2 1
## 93 RBM20 1
## 94 RELN 1
## 95 RFC3 1
## 96 ROR1 1
## 97 RRM2 1
## 98 RYR3 1
## 99 SFRP1 1
## 100 SIPA1L1 1
## 101 SLC16A1-AS1 1
## 102 SLC1A3 1
## 103 SLC25A4 1
## 104 SLC27A6 1
## 105 SLIT3 1
## 106 STK39 1
## 107 TBX5 1
## 108 TENT5A 1
## 109 THSD4 1
## 110 TMEM71 1
## 111 TNNC1 1
## 112 TRPM3 1
## 113 TTTY10 1
## 114 TTTY14 1
## 115 UTY 1
## 116 XIRP1 1
## 117 XIRP2 1
## 118 ZNF385B 1top_markers_df[grep("neu", top_markers_df$L3_clusterName),"gene"] %>%
table %>% as.data.frame %>% dplyr::arrange(desc(Freq))## . Freq
## 1 RALYL 4
## 2 CUX2 3
## 3 DPYD 3
## 4 IL1RAPL2 3
## 5 KCNIP4 3
## 6 KCNQ5 3
## 7 LRRC4C 3
## 8 SORCS1 3
## 9 TRPM3 3
## 10 ZNF536 3
## 11 ADAMTSL3 2
## 12 CDH18 2
## 13 CDH4 2
## 14 CHRM3 2
## 15 CLMP 2
## 16 CNR1 2
## 17 CNTN4 2
## 18 CNTNAP2 2
## 19 DLGAP2 2
## 20 DNAH10 2
## 21 DOK6 2
## 22 DPY19L1 2
## 23 ERBB4 2
## 24 FGF13 2
## 25 GALNTL6 2
## 26 GAS7 2
## 27 GRAMD1B 2
## 28 GRIK2 2
## 29 GRIK3 2
## 30 GRM7 2
## 31 HS3ST4 2
## 32 KLHL29 2
## 33 LHFPL3 2
## 34 LUZP2 2
## 35 MDGA2 2
## 36 MEIS2 2
## 37 NCAM2 2
## 38 NPAS3 2
## 39 NRG1 2
## 40 NRP1 2
## 41 NRXN1 2
## 42 NRXN3 2
## 43 PRSS12 2
## 44 PTPRT 2
## 45 RGS6 2
## 46 SEMA3C 2
## 47 SORBS2 2
## 48 SOX2-OT 2
## 49 TAFA2 2
## 50 TLE4 2
## 51 UNC5D 2
## 52 ZNF804B 2
## 53 ACSL1 1
## 54 ACTN2 1
## 55 ADAMTS3 1
## 56 ADARB2 1
## 57 ADCY1 1
## 58 ADGRB3 1
## 59 ADGRL2 1
## 60 ALK 1
## 61 ANK2 1
## 62 ANK3 1
## 63 ASTN2 1
## 64 ATP8A2 1
## 65 ATRNL1 1
## 66 BCHE 1
## 67 BRINP2 1
## 68 BRINP3 1
## 69 C8orf34 1
## 70 CACNA1A 1
## 71 CCBE1 1
## 72 CDH12 1
## 73 CDH13 1
## 74 CDH19 1
## 75 CDH8 1
## 76 CHRNA7 1
## 77 CLSTN2 1
## 78 CNTN3 1
## 79 CNTN5 1
## 80 CNTNAP3 1
## 81 CNTNAP5 1
## 82 COL19A1 1
## 83 COL25A1 1
## 84 CTNNA2 1
## 85 DCC 1
## 86 DGKB 1
## 87 DISP3 1
## 88 DLX6-AS1 1
## 89 DNER 1
## 90 DPP6 1
## 91 DSCAM 1
## 92 DTNA 1
## 93 ELAPOR1 1
## 94 ELFN1 1
## 95 ELMO1 1
## 96 EML5 1
## 97 EML6 1
## 98 ENSG00000253693 1
## 99 ENSG00000271860 1
## 100 ENSG00000285744 1
## 101 ENSG00000287092 1
## 102 EPB41L4A 1
## 103 FAM135B 1
## 104 FGF12 1
## 105 FRMPD4 1
## 106 GABRG3 1
## 107 GAD1 1
## 108 GADL1 1
## 109 GLRA2 1
## 110 GREM2 1
## 111 GRIA2 1
## 112 GRIA4 1
## 113 GRID2 1
## 114 GRM3 1
## 115 GRP 1
## 116 GUCY1A2 1
## 117 HS3ST5 1
## 118 HS6ST2 1
## 119 HS6ST3 1
## 120 IGSF21 1
## 121 IL1RAPL1 1
## 122 IL7 1
## 123 ITPR1 1
## 124 KCNB2 1
## 125 KCNC2 1
## 126 KCNH5 1
## 127 KCNJ3 1
## 128 KCNK2 1
## 129 KCNMA1 1
## 130 KCNN3 1
## 131 KCNQ3 1
## 132 KIAA1217 1
## 133 KIF26B 1
## 134 KIRREL3 1
## 135 KITLG 1
## 136 KLHL1 1
## 137 KLHL32 1
## 138 LAMA2 1
## 139 LHFPL6 1
## 140 LINC01091 1
## 141 LINC01102 1
## 142 LINC01322 1
## 143 LINC01435 1
## 144 LINC01606 1
## 145 LINC02254 1
## 146 LINC03000 1
## 147 LINGO2 1
## 148 LMO3 1
## 149 LRFN2 1
## 150 LRP1B 1
## 151 LRP8 1
## 152 LRRTM4 1
## 153 MAP2K1 1
## 154 MIR137HG 1
## 155 MIR4500HG 1
## 156 MTUS2 1
## 157 MUSK 1
## 158 MYO5B 1
## 159 MYRIP 1
## 160 MYT1L 1
## 161 NEGR1 1
## 162 NKAIN2 1
## 163 NKAIN3 1
## 164 NLGN4X 1
## 165 NOL4 1
## 166 NPNT 1
## 167 NR2F2-AS1 1
## 168 NR3C2 1
## 169 NRP2 1
## 170 NWD2 1
## 171 NXPH1 1
## 172 NXPH2 1
## 173 OCA2 1
## 174 OPCML 1
## 175 PALMD 1
## 176 PAM 1
## 177 PANTR1 1
## 178 PCBP3 1
## 179 PCDH11Y 1
## 180 PCSK2 1
## 181 PCSK5 1
## 182 PDZD2 1
## 183 PDZRN3 1
## 184 PDZRN4 1
## 185 PEX5L 1
## 186 PHLDB2 1
## 187 PHOX2B-AS1 1
## 188 PIP5K1B 1
## 189 PLCE1 1
## 190 PLS3 1
## 191 PLXNA2 1
## 192 POU6F2 1
## 193 PPP2R2C 1
## 194 PREX1 1
## 195 PRKCA 1
## 196 PTCHD4 1
## 197 PTPRN2 1
## 198 PTPRZ1 1
## 199 R3HDM1 1
## 200 RASGEF1C 1
## 201 RBFOX3 1
## 202 RGS7 1
## 203 RIMS2 1
## 204 RIPOR2 1
## 205 RYR3 1
## 206 SCN5A 1
## 207 SDK1 1
## 208 SEMA3E 1
## 209 SGCZ 1
## 210 SLA 1
## 211 SLAIN1 1
## 212 SLC22A23 1
## 213 SLC35F1 1
## 214 SLC35F3 1
## 215 SLC44A5 1
## 216 SLC8A3 1
## 217 SNTG2 1
## 218 SORCS3 1
## 219 ST3GAL6 1
## 220 ST8SIA5 1
## 221 STK32B 1
## 222 SYBU 1
## 223 SYT1 1
## 224 TENM3 1
## 225 THRB 1
## 226 TMEFF2 1
## 227 TMEM108 1
## 228 TRPC4 1
## 229 TRPC5 1
## 230 TRPS1 1
## 231 TSHZ3 1
## 232 UNC5C 1
## 233 XKR4 1
## 234 ZBTB16 1
## 235 ZFPM2 1
## 236 ZNF385B 1
## 237 ZNF804A 1top_markers_df[grep("TEC", top_markers_df$L3_clusterName),"gene"] %>%
table %>% as.data.frame %>% dplyr::arrange(desc(Freq))## . Freq
## 1 NEB 3
## 2 ADGRL3 2
## 3 FGF13 2
## 4 RYR3 2
## 5 TRDN 2
## 6 ACTA1 1
## 7 ACTN2 1
## 8 ADARB2 1
## 9 AGBL1 1
## 10 AHNAK 1
## 11 ANK2 1
## 12 ANK3 1
## 13 ATP10B 1
## 14 BCHE 1
## 15 BNC2 1
## 16 C3 1
## 17 CA3 1
## 18 CADM2 1
## 19 CALCR 1
## 20 CCDC80 1
## 21 CDIN1 1
## 22 CDON 1
## 23 CHRM3 1
## 24 CLCN5 1
## 25 CMYA5 1
## 26 CNTN4 1
## 27 COL15A1 1
## 28 COL19A1 1
## 29 COL25A1 1
## 30 CRIM1 1
## 31 CTNNA3 1
## 32 CYP1B1-AS1 1
## 33 DCC 1
## 34 DLG2 1
## 35 DMD 1
## 36 DPP6 1
## 37 ENSG00000226043 1
## 38 ENSG00000268981 1
## 39 ENSG00000289084 1
## 40 ENSG00000289950 1
## 41 EYA2 1
## 42 FAM163A 1
## 43 FILIP1L 1
## 44 FOLH1 1
## 45 FRMD4A 1
## 46 GADL1 1
## 47 GPC5 1
## 48 GREM2 1
## 49 H19 1
## 50 HS6ST2 1
## 51 IGF2 1
## 52 KCNIP4 1
## 53 KCNQ1OT1 1
## 54 KCTD8 1
## 55 LAMA2 1
## 56 LDB3 1
## 57 LINC01091 1
## 58 LINC02955 1
## 59 LRP2 1
## 60 MAP2K1 1
## 61 MARCHF1 1
## 62 MCF2L 1
## 63 MEF2C 1
## 64 MEG3 1
## 65 MEG8 1
## 66 MEG9 1
## 67 MEGF10 1
## 68 MGAT4C 1
## 69 MIR133A1HG 1
## 70 MIR493HG 1
## 71 MLIP 1
## 72 MUSK 1
## 73 MYBPC1 1
## 74 MYH2 1
## 75 MYH3 1
## 76 MYH7 1
## 77 MYH7B 1
## 78 MYH8 1
## 79 MYL1 1
## 80 MYL2 1
## 81 MYOM2 1
## 82 NCAM1 1
## 83 NEK10 1
## 84 NFIB 1
## 85 NKAIN2 1
## 86 NPNT 1
## 87 NRXN3 1
## 88 PAX7 1
## 89 PCDH7 1
## 90 PHLDB2 1
## 91 PKHD1L1 1
## 92 PLCE1 1
## 93 PRG4 1
## 94 PRSS16 1
## 95 PTPRQ 1
## 96 PTPRT 1
## 97 RBFOX1 1
## 98 ROBO2 1
## 99 ROR1 1
## 100 RYR1 1
## 101 RYR2 1
## 102 SAMD4A-AS1 1
## 103 SCN5A 1
## 104 SEMA3C 1
## 105 SLC22A23 1
## 106 SLC2A14 1
## 107 SLC8A3 1
## 108 SLF2 1
## 109 SLIT3 1
## 110 SOX6 1
## 111 SPATS2L 1
## 112 STARD13 1
## 113 SULF1 1
## 114 TBATA 1
## 115 TECRL 1
## 116 THSD4 1
## 117 TLN2 1
## 118 TMEM108 1
## 119 TNNC2 1
## 120 TNNI1 1
## 121 TNNT1 1
## 122 TNNT3 1
## 123 TPM1 1
## 124 TPM2 1
## 125 TPM3 1
## 126 TTN 1
## 127 VCAN 1
## 128 WDPCP 1
## 129 WNK2 1
## 130 XIRP2 1
## 131 XKR4 1
## 132 ZEB2 1
## 133 ZNF385B 1
## 134 ZNF385D 1Manually add top marker genes for cell groups of interest to markergenes_dendro.R
Then run the following plotting blocks
source(here::here("output/01-preprocessing/02/shared/markergenes/markergenes_dendro.R"))
# subset each gene expr mat to the collection of markers, to save time in the merge
avg_exp_decontx_subset_list2 <- map(avg_exp_decontx_list, ~ .x[rownames(.x) %in% featureSets, ])
map(avg_exp_decontx_subset_list2, dim)## [[1]]
## [1] 34 10
##
## [[2]]
## [1] 36 18
##
## [[3]]
## [1] 36 22
##
## [[4]]
## [1] 36 16
##
## [[5]]
## [1] 36 14
##
## [[6]]
## [1] 36 18
##
## [[7]]
## [1] 36 22
##
## [[8]]
## [1] 36 19
##
## [[9]]
## [1] 36 12
##
## [[10]]
## [1] 36 22
##
## [[11]]
## [1] 36 18
##
## [[12]]
## [1] 35 12avg_exp_decontx_mat_subset2 <- Reduce(merge2, avg_exp_decontx_subset_list2)
dim(avg_exp_decontx_mat_subset2)## [1] 36 203any(is.na(avg_exp_decontx_mat_subset2))## [1] TRUEavg_exp_decontx_mat_subset2[is.na(avg_exp_decontx_mat_subset2)] <- 0 # set any NA to zero
any(is.na(avg_exp_decontx_mat_subset2))## [1] FALSE# scale expr matrix
avg_exp_decontx_mat_subset_scaled2 <- avg_exp_decontx_mat_subset2 %>%
# genes are in rows; for each gene, scale expression to [0, 1]
apply(1, scales::rescale) %>%
t()
markers <- featureSets[featureSets %in% rownames(avg_exp_decontx_mat_subset_scaled2)]# make a function to plot the heatmap in dendrogram order, and with markers ordered diagonally
marker_hm_fun <- purrr::partial(
pheatmap::pheatmap,
mat = avg_exp_decontx_mat_subset_scaled2[markers, cluster_meta2$Cluster],
border_color = NA,
cluster_cols = FALSE,
cluster_rows = FALSE,
annotation_col = cluster_meta2 %>% dplyr::select(organ),
annotation_colors = list("organ" = cmap_organ),
# use the project's RNA-specific palette
color = colorRampPalette(cmap_rna)(n=100),
show_rownames = TRUE,
cellwidth = 13,
cellheight = 5,
main = "Average expression of top marker genes per cluster, scaled to [0,1] across clusters")
# make png/pdf
# marker_hm_fun()
marker_hm_fun(filename=paste0(figout, "/marker_heatmap_manual.pdf"))flat_data <- melt(avg_exp_decontx_mat_subset_scaled2[markers, cluster_meta2$Cluster],
varnames = c("Markers", "Cluster"),
value.name = "Expression")
ggplot(flat_data, aes(x=Cluster, y=Markers, color=Expression, size=Expression)) +
geom_point(stroke=0) + theme(axis.text.x = element_text(angle=90, hjust=1)) +
scale_size(range = c(0, 5)) + # Adjust dot size range +
scale_y_discrete(limits = rev(unique(flat_data$Markers))) + # Reverse marker order
#scale_color_gradientn(colors = cmap_rna) +
labs(x = "Cluster", y = "Markers", size = "Percentage (%)", color = "Expression") +
scale_color_gradient(low = "white", high = "red")
# pdf(paste0(figout, "/manual_marker_dotplot.pdf"), width=30, height=5)
# print(p)
# dev.off()
#
# png(paste0(figout, "/manual_marker_dotplot.png"), width=30, height=5, units ="in", res=300)
# print(p)
# dev.off()7 Session info
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /share/software/user/open/openblas/0.3.10/lib/libopenblas_haswellp-r0.3.10.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggthemes_4.2.4 RColorBrewer_1.1-3
## [3] DoubletFinder_2.0.3 BPCells_0.2.0
## [5] RcppAlgos_2.7.2 ggrastr_1.0.1
## [7] GenomicFeatures_1.46.5 AnnotationDbi_1.56.2
## [9] rhdf5_2.38.1 SummarizedExperiment_1.24.0
## [11] Biobase_2.54.0 MatrixGenerics_1.6.0
## [13] Rcpp_1.0.10 Matrix_1.5-4
## [15] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
## [17] IRanges_2.28.0 S4Vectors_0.32.4
## [19] BiocGenerics_0.40.0 matrixStats_0.63.0
## [21] plyr_1.8.8 magrittr_2.0.3
## [23] gtable_0.3.3 gtools_3.9.4
## [25] gridExtra_2.3 ArchR_1.0.2
## [27] dendextend_1.17.1 pvclust_2.2-0
## [29] data.table_1.14.8 cowplot_1.1.1
## [31] SeuratObject_4.1.3 Seurat_4.3.0
## [33] stringr_1.5.0 tibble_3.2.1
## [35] readr_2.1.4 glue_1.6.2
## [37] purrr_1.0.2 ggplot2_3.5.0
## [39] tidyr_1.3.1 dplyr_1.1.4
## [41] here_1.0.1
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.3 spatstat.explore_3.1-0 reticulate_1.28
## [4] tidyselect_1.2.0 RSQLite_2.3.1 htmlwidgets_1.6.2
## [7] gmp_0.7-1 BiocParallel_1.28.3 Rtsne_0.16
## [10] munsell_0.5.0 codetools_0.2-18 ica_1.0-3
## [13] future_1.33.2 miniUI_0.1.1.1 withr_3.0.0
## [16] spatstat.random_3.1-4 colorspace_2.1-0 progressr_0.13.0
## [19] filelock_1.0.2 highr_0.10 knitr_1.42
## [22] ROCR_1.0-11 tensor_1.5 listenv_0.9.0
## [25] labeling_0.4.2 GenomeInfoDbData_1.2.7 polyclip_1.10-4
## [28] farver_2.1.1 pheatmap_1.0.12 bit64_4.0.5
## [31] rprojroot_2.0.3 parallelly_1.35.0 vctrs_0.6.5
## [34] generics_0.1.3 xfun_0.39 BiocFileCache_2.2.1
## [37] R6_2.5.1 ggbeeswarm_0.7.2 bitops_1.0-7
## [40] rhdf5filters_1.6.0 spatstat.utils_3.0-2 cachem_1.0.8
## [43] DelayedArray_0.20.0 vroom_1.6.3 promises_1.2.0.1
## [46] BiocIO_1.4.0 scales_1.3.0 beeswarm_0.4.0
## [49] globals_0.16.2 goftest_1.2-3 rlang_1.1.3
## [52] splines_4.1.2 rtracklayer_1.54.0 lazyeval_0.2.2
## [55] spatstat.geom_3.1-0 yaml_2.3.7 reshape2_1.4.4
## [58] abind_1.4-5 httpuv_1.6.10 tools_4.1.2
## [61] ellipsis_0.3.2 jquerylib_0.1.4 ggridges_0.5.4
## [64] progress_1.2.2 zlibbioc_1.40.0 RCurl_1.98-1.12
## [67] prettyunits_1.1.1 deldir_1.0-6 pbapply_1.7-0
## [70] viridis_0.6.3 zoo_1.8-12 ggrepel_0.9.5
## [73] cluster_2.1.2 scattermore_1.0 lmtest_0.9-40
## [76] RANN_2.6.1 fitdistrplus_1.1-11 hms_1.1.3
## [79] patchwork_1.2.0 mime_0.12 evaluate_0.21
## [82] xtable_1.8-4 XML_3.99-0.14 compiler_4.1.2
## [85] biomaRt_2.50.3 KernSmooth_2.23-20 crayon_1.5.2
## [88] htmltools_0.5.5 later_1.3.1 tzdb_0.4.0
## [91] DBI_1.1.3 dbplyr_2.3.2 MASS_7.3-54
## [94] rappdirs_0.3.3 cli_3.6.2 parallel_4.1.2
## [97] igraph_1.4.2 pkgconfig_2.0.3 GenomicAlignments_1.30.0
## [100] sp_1.6-0 plotly_4.10.1 spatstat.sparse_3.0-1
## [103] xml2_1.3.4 vipor_0.4.5 bslib_0.4.2
## [106] XVector_0.34.0 digest_0.6.31 sctransform_0.3.5
## [109] RcppAnnoy_0.0.20 spatstat.data_3.0-1 Biostrings_2.62.0
## [112] rmarkdown_2.21 leiden_0.4.2 uwot_0.1.14
## [115] restfulr_0.0.15 curl_5.0.0 Rsamtools_2.10.0
## [118] shiny_1.7.4 rjson_0.2.21 lifecycle_1.0.3
## [121] nlme_3.1-153 jsonlite_1.8.4 Rhdf5lib_1.16.0
## [124] viridisLite_0.4.2 fansi_1.0.4 pillar_1.9.0
## [127] lattice_0.20-45 KEGGREST_1.34.0 fastmap_1.1.1
## [130] httr_1.4.6 survival_3.2-13 png_0.1-8
## [133] bit_4.0.5 stringi_1.7.12 sass_0.4.6
## [136] blob_1.2.4 memoise_2.0.1 irlba_2.3.5.1
## [139] future.apply_1.10.0