HGG-oncohistones project [source]
05 - Summary figures for extended mouse atlas
Selin Jessa []
12 September, 2022
1 Configuration
Configuration of project directory & analysis outputs:
Show full config
source(here("rr_helpers.R"))
# Set up outputs
message("Document index: ", doc_id)## Document index: 05# Specify where to save outputs
out <- here("output", doc_id); dir.create(out, recursive = TRUE)
figout <- here("figures", doc_id); dir.create(figout, recursive = TRUE)
cache <- paste0(readLines(here("include/project_root.txt")), basename(here()), "/", doc_id, "/")Outputs and figures will be saved at these paths, relative to project root:
## public/output/05## public/figures/05Setting a random seed:
set.seed(100)2 Overview
This document summarizes the extended mouse scRNAseq developmental atlas and generates the dendrograms over single-cell clusters for each brain region, as shown in Extended Data Figure 1.
3 Libraries
# Load libraries here
library(here)
library(tidyr)
library(dplyr)
library(readr)
library(readxl)
library(glue)
library(purrr)
library(pvclust)
library(ggplot2)
library(ggrepel)
library(cowplot)
library(dendextend)
library(ggrastr)
library(Seurat)
source(here("include/style.R"))
source(here("code/functions/scRNAseq.R"))
ggplot2::theme_set(theme_min())
# set seed to reproduce earlier analysis
set.seed(23)4 Load data
atlas_path <- read_lines(here("data/scRNAseq/references/mouse_atlas_extended/atlas_path_hydra.tsv"))
mean_expression_profile <- readRDS(file.path(atlas_path, "data/joint_mouse_extended/mean_expression_per_cluster.Rds"))
cluster_signatures <- readRDS(file.path(atlas_path, "data/joint_mouse_extended/joint_mouse_extended.signatures_ID_20210710.Rds"))
mouse_signatures <- cluster_signatures$mm_sym5 Compute dendrograms
Compute dendrograms based on correlation between mean transcriptome of each cluster. We use pvclust for constructing dendrograms.
# get the unique signature genes from mouse only
atlas_unique_genes_mm <- mouse_signatures %>% unlist %>% unique
f_signatures <- mouse_signatures[grepl("F-", names(mouse_signatures))] %>% unlist %>% unique
p_signatures <- mouse_signatures[grepl("P-.+_", names(mouse_signatures))] %>% unlist %>% unique
meanexp_mm_uniq <- mean_expression_profile[atlas_unique_genes_mm]
rownames(meanexp_mm_uniq) <- mean_expression_profile$ID_20210710
# subset the gene signatures to the clusters used in the dendrogram
meanexp_f_uniq <- meanexp_mm_uniq %>% filter(grepl("F-", rownames(meanexp_mm_uniq))) %>% .[f_signatures]
meanexp_p_uniq <- meanexp_mm_uniq %>% filter(grepl("P-.+_", rownames(meanexp_mm_uniq))) %>% .[p_signatures]
# transpose
meanexp_f_uniq <- as.data.frame(t(meanexp_f_uniq))
meanexp_p_uniq <- as.data.frame(t(meanexp_p_uniq))
# take correlations
cor_f <- cor(meanexp_f_uniq, meanexp_f_uniq, method = "spearman", use = "complete.obs")
cor_p <- cor(meanexp_p_uniq, meanexp_p_uniq, method = "spearman", use = "complete.obs")
# remove NAs
meanexp_f_uniq_no_NA <- meanexp_f_uniq
meanexp_f_uniq_no_NA[is.na(meanexp_f_uniq_no_NA)] <- 0
meanexp_p_uniq_no_NA <- meanexp_p_uniq
meanexp_p_uniq_no_NA[is.na(meanexp_p_uniq_no_NA)] <- 0
result_f <- pvclust(meanexp_f_uniq_no_NA, method.dist = spearman,
method.hclust = "complete", nboot = 100)## Bootstrap (r = 0.5)... Done.
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## Bootstrap (r = 1.4)... Done.result_p <- pvclust(meanexp_p_uniq_no_NA, method.dist = spearman,
method.hclust = "complete", nboot = 100)## Bootstrap (r = 0.5)... Done.
## Bootstrap (r = 0.6)... Done.
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## Bootstrap (r = 1.4)... Done.dend_f <- as.dendrogram(result_f)
dend_order_f <- colnames(cor_f)[order.dendrogram(dend_f)]
dend_p <- as.dendrogram(result_p)
dend_order_p <- colnames(cor_p)[order.dendrogram(dend_p)]
saveRDS(dend_order_f,
file = glue("{out}/dendrogram_order_joint_extended_forebrain.Rds"))
saveRDS(dend_order_p,
file = glue("{out}/dendrogram_order_joint_extended_pons.Rds"))
f_palette <- palette_joint_mouse_extended_full[dend_order_f]
p_palette <- palette_joint_mouse_extended_full[dend_order_p]
dend_f <- dend_f %>% set("labels_col", f_palette) %>% set("labels_cex", 0.3)
dend_p <- dend_p %>% set("labels_col", p_palette) %>% set("labels_cex", 0.3)
par(mar = c(20,2,2,2))
plot(dend_f)
plot(dend_p)
dev.off()## null device
## 16 Generate tables
Per-sample data:
mouse_new_sample_qc <- read_xlsx(here("data/scRNAseq/references/mouse_atlas_extended/new_timepoints_stats.xlsx"))
mouse_sample_summary <- read_xlsx(here("data/metadata/2021-10-20_Omega_table-singlecell.xlsx")) %>%
filter(Diagnosis_1 == "Normal Mouse Brain") %>%
filter(!(Sample %in% c("PT_CT_0F", "fresh_mouse_brain_E18"))) %>%
left_join(mouse_new_sample_qc, by = c("Aliases" = "Alias")) %>%
rename(Alias = Aliases)## New names:
## * `library yield (nM)` -> `library yield (nM)...33`
## * `copies/ul` -> `copies/ul...34`
## * `copies/ul` -> `copies/ul...35`
## * `library yield (nM)` -> `library yield (nM)...36`# get QC stats after filtering
mouse_sample_qc_after_filt <- map_dfr(mouse_sample_summary$Alias,
~ data.table::fread(glue("{atlas_path}/data/{.x}/{.x}.metadata.tsv"), data.table = FALSE) %>%
group_by(alias) %>%
summarise_at(.vars = c("nCount_RNA", "nFeature_RNA"), .funs = mean)
)
mouse_sample_summary_with_qc <- mouse_sample_summary %>%
left_join(mouse_sample_qc_after_filt, by = c("Alias" = "alias")) %>%
select(Path, Original_sample = Sample, Alias, Protocol, Publication,
Kit = `kit version`, Species, Age, Location,
Starting_material = `Starting material`,
N_reads = `Number of reads`,
Reads_mapped_to_genome = `Reads mapped to genome`,
Reads_mapped_to_transcriptome = `Reads mapped to transcriptome`,
N_cells_estimated = `Estimated number of cells`,
N_cells_after_filtering = `number of cells post-filtering`,
Mean_UMIs_after_filtering = nCount_RNA,
Mean_N_genes_after_filtering = nFeature_RNA,
Min_cells_threshold = min_cells,
Min_N_genes_threshold = min_nGene,
Max_N_genes_threshold = max_nGene,
Min_UMIs_threshold = min_nUMI,
Max_UMIs_threshold = max_nUMI,
Number_of_PCs = `number of PCs`,
Clustering_resolution = `clustering resolution`) %>%
mutate(Publication = ifelse(is.na(Publication), "This study", Publication)) %>%
arrange(Publication, Age, Location)
mouse_sample_summary_with_qc %>%
rr_write_tsv(glue("{out}/TABLE_mouse_sample_info.tsv"),
"Sample info and QC stats for mouse brain samples")## ...writing description of TABLE_mouse_sample_info.tsv to public/output/05/TABLE_mouse_sample_info.descPer-cluster data:
mouse_info_clusters <- data.table::fread(file.path(atlas_path, "data/metadata_extended/metadata_20210710_with_qc.tsv"), data.table = FALSE) %>%
left_join(mouse_sample_summary %>% select(Original_sample = Sample, Alias), by = "Alias") %>%
select(Sample, Original_sample, Alias, everything())
# sanity check that all the signatures are in the table
all(names(cluster_signatures$mm_sym) %in% mouse_info_clusters$Label)## [1] TRUE# now, add the signatures for each cluster to the table
cluster_signatures_df <- data.frame("Signature_mm_symbols" = map_chr(cluster_signatures$mm_sym, ~ glue_collapse(.x, sep = ",")),
"Signature_mm_ensembl" = map_chr(cluster_signatures$mm_ens, ~ glue_collapse(.x, sep = ",")),
"Signature_hg_symbols" = map_chr(cluster_signatures$hg_sym, ~ glue_collapse(.x, sep = ",")),
"Signature_hg_ensembl" = map_chr(cluster_signatures$hg_ens, ~ glue_collapse(.x, sep = ","))) %>%
tibble::rownames_to_column(var = "Label")
mouse_info_clusters_with_sigs <- mouse_info_clusters %>%
left_join(cluster_signatures_df, by = "Label")
# add cell class
# mouse_info_clusters_with_sigs_class <- mouse_info_clusters_with_sigs %>%
# summarize_cell_types("Level4_short") %>%
# rename(Cell_ontological_class = Type) %>%
# relocate(Cell_ontological_class, .before = "Level1_type")
# add dend order
mouse_info_clusters_with_sigs_class_dend <- mouse_info_clusters_with_sigs %>%
left_join(data.frame("Label" = dend_order_f) %>%
tibble::rowid_to_column(var = "Dendrogram_order_forebrain")) %>%
left_join(data.frame("Label" = dend_order_p) %>%
tibble::rowid_to_column(var = "Dendrogram_order_pons")) %>%
relocate(Dendrogram_order_forebrain, Dendrogram_order_pons, .after = Exclude)## Joining, by = "Label"
## Joining, by = "Label"mouse_info_clusters_with_sigs_class_dend %>%
rr_write_tsv(glue("{out}/TABLE_mouse_cluster_info.tsv"),
"Cluster info, QC stats, signatures, and dendrogram order for each mouse brain single-cell cluster")## ...writing description of TABLE_mouse_cluster_info.tsv to public/output/05/TABLE_mouse_cluster_info.desc7 Supplementary figures
Generate annotations for the dendrograms:
anno_dend_f <- mouse_info_clusters_with_sigs_class_dend %>%
select(Label, Dendrogram_order_forebrain, Timepoint) %>%
filter(!is.na(Dendrogram_order_forebrain)) %>%
arrange(Dendrogram_order_forebrain) %>%
mutate(Label = factor(Label, levels = .$Label))
anno_dend_f %>%
ggplot(aes(x = Label, y = 1)) +
geom_tile(aes(fill = Timepoint), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = palette_timepoint) +
theme_min() +
theme(panel.border = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks = element_blank()) +
rotate_x()
anno_dend_p <- mouse_info_clusters_with_sigs_class_dend %>%
select(Label, Dendrogram_order_pons, Timepoint) %>%
filter(!is.na(Dendrogram_order_pons)) %>%
arrange(Dendrogram_order_pons) %>%
mutate(Label = factor(Label, levels = .$Label))
anno_dend_p %>%
ggplot(aes(x = Label, y = 1)) +
geom_tile(aes(fill = Timepoint), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = palette_timepoint) +
theme_min() +
theme(panel.border = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks = element_blank()) +
rotate_x()
Plot the # cells per sample:
joint_mouse_meta <- readRDS(file.path(atlas_path,
"2020-10_analyze_extended_dataset/integration_joint_mouse_extended/output/metadata.Rds"))
joint_mouse_meta %>%
separate(Sample, into = c("Location2", "Timepoint"), sep = " ") %>%
group_by(Timepoint, Location) %>%
count() %>%
ggplot(aes(x = Timepoint, y = n)) +
geom_col(aes(fill = Timepoint), width = 0.5) +
facet_wrap(~ Location) +
geom_text(aes(label = n), angle = 90, size = 4, hjust = -0.05) +
scale_fill_manual(values = palette_timepoint) +
rotate_x() +
ylim(c(0, 13000))
8 Reproducibility
This document was last rendered on:
## 2022-09-12 15:22:22The git repository and last commit:
## Local: master /lustre06/project/6004736/sjessa/from_narval/HGG-oncohistones/public
## Remote: master @ origin (git@github.com:fungenomics/HGG-oncohistones.git)
## Head: [1a06382] 2022-09-08: Update comments, documentation, etc, based on lab feedbackThe random seed was set with set.seed(100)
The R session info:
## Registered S3 method overwritten by 'cli':
## method from
## print.boxx spatstat## Error in get(genname, envir = envir) : object 'testthat_print' not found## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.1 (2019-07-05)
## os Rocky Linux 8.6 (Green Obsidian)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_CA.UTF-8
## ctype en_CA.UTF-8
## tz EST5EDT
## date 2022-09-12
##
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## P viridisLite * 0.4.0 2021-04-13 [?] CRAN (R 3.6.1)
## P withr 2.4.2 2021-04-18 [?] CRAN (R 3.6.1)
## P xfun 0.22 2021-03-11 [?] CRAN (R 3.6.1)
## P xtable 1.8-4 2019-04-21 [?] CRAN (R 3.6.1)
## P yaml 2.2.1 2020-02-01 [?] CRAN (R 3.6.1)
## P zoo 1.8-9 2021-03-09 [?] CRAN (R 3.6.1)
##
## [1] /lustre06/project/6004736/sjessa/from_narval/HGG-oncohistones/public/renv/library/R-3.6/x86_64-pc-linux-gnu
## [2] /tmp/RtmppM1df5/renv-system-library
##
## P ── Loaded and on-disk path mismatch.The resources requested when this document was last rendered:
## #SBATCH --time=01:00:00
## #SBATCH --cpus-per-task=1
## #SBATCH --mem=20G
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