HGG-oncohistones project [source]
01A - Prepare human fetal thalamus data
Selin Jessa []
08 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: 01A# 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/01A## public/figures/01ASetting a random seed:
set.seed(100)2 Overview
In this analysis, we'll prepare the reference from the human fetal thalamus data which has been processed with our single-cell workflow, and gather the required formats and outputs needed for comparisons to tumors:
- labels from the original publications, as well as manual additions/corrections based on known markers
- Seurat objects, per-sample and integrated across samples
- gene signatures, per-cluster
- mean expression profiles, per-cluster
This data comes from Bhaduri et al, and Eze et al.
3 Libraries
# Load libraries here
library(here)
library(tidyr)
library(dplyr)
library(readxl)
library(readr)
library(glue)
library(ggplot2)
library(magrittr)
library(purrr)
library(cowplot)
library(pvclust)
library(dendextend)
library(feather)
library(Seurat)
library(icytobox)
library(ggrepel)
source(here("include/style.R"))
source(here("code/functions/scRNAseq.R"))
ggplot2::theme_set(theme_min())4 Overview of individual samples
Each sample has a folder in the pipeline at:
data/scRNAseq/pipeline_human_fetal/
|-- sample1/
--- |-- preprocessing # output of the SC pipeline
----|-- correlations # output of projections
4.1 Metadata
Load per sample metadata:
info_samples <- read_tsv(here("data/metadata/metadata_human_fetal_10X.tsv")) %>%
filter(Region == "Thalamus") %>%
filter(grepl("GW", Age))
palette_sample <- info_samples %>% select(Sample, Sample_color) %>% tibble::deframe()
palette_alias <- info_samples %>% select(Alias, Sample_color) %>% tibble::deframe()
ages <- info_samples$Age %>% unique %>% sort()
palette_age <- colorRampPalette(brewer.pal(8, "YlGnBu"))(n = length(ages))
names(palette_age) <- agesLoad the labels from the authors of the original studies:
metadata_pub_gw <- read_excel(here("data/scRNAseq/references/Bhaduri2021_fetal_brain_GW/GW_sample_labels_Nature_2021_Supp_Table_1.xlsx")) %>%
filter(structure == "thalamus")
# get sample names in the same format
samples_pub <- metadata_pub_gw %>% filter(structure == "thalamus") %>%
pull(cell.name) %>%
{gsub('.{17}$', '', .)} %>%
unique()
all(info_samples$Sample_public %in% samples_pub)## [1] TRUE4.2 QC
Verify QC of each sample, to determine whether to include all:
qc_stats <- map_dfr(1:nrow(info_samples), ~ read_tsv(here(file.path(info_samples[.x, ]$Path, "preprocessing/seurat_metrics.tsv"))) %>%
tibble::add_column(.before = 1, Sample = info_samples[.x, ]$Sample))
#' a function to plot a scatterplot of 2 column in the aggregated qc dataframe
#'
#' @example qc_scatter(nCount_RNA_min.postQC, nCount_RNA_mean.postQC)
qc_scatter <- function(col1, col2) {
col1_quo <- enquo(col1)
col2_quo <- enquo(col2)
qc_stats %>%
ggplot(aes(x = !! col1_quo, y = !! col2_quo)) +
geom_point(aes(color = Sample), size = 4, alpha = 0.5) +
geom_text_repel(aes(label = Sample, color = Sample), size = 4) +
scale_color_manual(values = palette_sample) +
theme_min() +
no_legend()
}
qc_scatter(N_cells_before, N_cells_after)
4.3 Prep objects
For each sample:
- load the Seurat object
- count the number of cells in each cluster, and give each cluster a label prefixed w/ sample name
- add the labels from the published metadata
prep_seurat <- function(path_to_sample, sample_public_id) {
message("@ ", basename(path_to_sample))
# load the object as output by the SC workflow
load(file.path(path_to_sample, "preprocessing/seurat.Rda"))
# get the labels from the published metadata and add them to the Seurat object
public_labels <- seurat@meta.data %>%
tibble::rownames_to_column(var = "cell.name") %>%
rowwise() %>%
mutate(barcode_public = paste0(sample_public_id, "_", gsub("-1", "", cell.name, fixed = TRUE))) %>%
left_join(metadata_pub_gw %>%
select(barcode_public = cell.name, Label_public = `cell type`), by = c("barcode_public")) %>%
select(cell.name, Label_public) %>%
tibble::column_to_rownames(var = "cell.name")
seurat <- AddMetaData(seurat,
metadata = public_labels,
col.name = c("Label_published"))
# get some summary stats per cluster
seurat[["Cluster_number"]] <- Idents(object = seurat)
info_samples_cluster <- seurat@meta.data %>%
group_by(Cluster_number) %>%
mutate(N_cells = n()) %>%
group_by(Cluster_number, N_cells) %>%
summarize_at(vars(nCount_RNA, nFeature_RNA, percent.mito, percent.ribo, S.Score, G2M.Score), mean) %>%
tibble::add_column(.before = 1, Sample = basename(path_to_sample)) %>%
# add sample info table, and prefix the cluster number by the alias, so that it's unique
# in the form, [Alias]_[Cluster]
left_join(select(info_samples, Sample, Age, Alias), by = "Sample") %>%
mutate(Cluster = paste0(Alias, "_", Cluster_number)) %>%
select(Sample, Age, Alias, Cluster_number, Cluster, everything())
# get the most common projection from the public data
cluster_labels_published <- seurat@meta.data %>% rowwise() %>%
group_by(Cluster_number) %>%
mutate(N_cells_in_cluster = n()) %>%
filter(!is.na(Label_published)) %>%
group_by(Cluster_number, N_cells_in_cluster, Label_published) %>%
summarize(N_celltype_in_cluster = n()) %>%
mutate(Prop_celltype_in_cluster = round(N_celltype_in_cluster/N_cells_in_cluster, 2)) %>%
group_by(Cluster_number) %>% top_n(1, Prop_celltype_in_cluster) %>%
slice(1) %>%
filter(Prop_celltype_in_cluster >= 0.25) %>%
select(Cluster_number, N_celltype_in_cluster, Prop_celltype_in_cluster, Label_published)
info_samples_cluster <- info_samples_cluster %>%
left_join(cluster_labels_published, by = "Cluster_number") %>%
mutate(ID_20220321 = paste0(Cluster, "_", Label_published)) %>%
dplyr::rename(Prop_cluster_with_published_label = Prop_celltype_in_cluster) %>%
select(-N_celltype_in_cluster)
return(info_samples_cluster)
}
# gather all cluster summary info
info_clusters <- map_dfr(1:nrow(info_samples), ~ prep_seurat(info_samples[.x, ]$Path, info_samples[.x, ]$Sample_public))
write_tsv(info_clusters, glue("{out}/info_clusters.tsv"))info_clusters <- read_tsv(glue("{out}/info_clusters.tsv"))4.4 Sample exclusions
We'll exclude the following samples:
- GW19_thalamus, due to low # of cells
- GW14_thalamus, due to low structure
- GW18_2_ventral_thalamus, due to high mito content
- GW20_34_dorsalthalamus, due to low coverage
4.5 Per-cluster QC
samples_drop <- c("GW19_thalamus", "GW14_thalamus", "GW18_2_ventral_thalamus", "GW20_34_dorsalthalamus")
info_clusters %>% nrow()## [1] 233info_clusters2 <- info_clusters %>%
mutate(Note_sample = case_when(
Sample == "GW19_thalamus" ~ "Sample excluded, low number of cells",
Sample == "GW14_thalamus" ~ "Sample excluded, low structure",
Sample == "GW18_2_ventral_thalamus" ~ "Sample excluded, high mitochondrial content",
Sample == "GW20_34_dorsalthalamus" ~ "Sample excluded, low coverage",
TRUE ~ "OK"),
Note_cluster = case_when(
Note_sample != "OK" ~ "Sample excluded",
N_cells < 30 ~ "Less than 30 cells",
nCount_RNA < 1000 ~ "Low number of UMIs",
percent.ribo > 50 ~ "High ribosomal content",
TRUE ~ "OK"
))
info_clusters2 %>% filter(Note_sample == "OK" & Note_cluster == "OK") %>% nrow()## [1] 167write_tsv(info_clusters2, glue("{out}/info_clusters2.tsv"))
# total # of cells
sum(info_clusters2$N_cells)## [1] 902264.6 QC tables
Produce a QC table for the supplementary materials:
TABLE_thalamus_qc <- info_samples %>%
left_join(qc_stats, by = "Sample") %>%
mutate(Note_sample = case_when(
Sample == "GW19_thalamus" ~ "Sample excluded, low number of cells",
Sample == "GW14_thalamus" ~ "Sample excluded, low structure",
Sample == "GW18_2_ventral_thalamus" ~ "Sample excluded, high mitochondrial content",
Sample == "GW20_34_dorsalthalamus" ~ "Sample excluded, low coverage",
TRUE ~ "OK")) %>%
mutate(Publication = case_when(
grepl("CS", Sample) ~ "Eze et al, Nature Neuroscience, 2021",
grepl("GW", Sample) ~ "Bhaduri et al, Nature, 2021"
)) %>%
select(
# Sample info
Sample, Age, Alias, Region, Publication,
# QC
# N_reads = `Number of Reads`,
# N_cells_estimated = `Estimated Number of Cells`,
N_cells_after_filtering = N_cells_after,
# Reads_mapped_to_genome = `Reads Mapped to Genome`,
# Reads_mapped_to_transcriptome = `Reads Mapped Confidently to Transcriptome`,
Mean_mitochondrial_content_after_filtering = percent.mito_mean.postQC,
Mean_UMIs_after_filtering = nCount_RNA_mean.postQC,
Mean_N_genes_after_filtering = nFeature_RNA_mean.postQC,
Max_mito_threshold = percent.mito_max.threshold,
Min_N_genes_threshold = nFeature_RNA_min.threshold,
Max_N_genes_threshold = nFeature_RNA_max.threshold,
Max_UMIs_threshold = nCount_RNA_max.threshold,
Note_sample)
rr_write_tsv(TABLE_thalamus_qc,
glue("{out}/TABLE_thalamus_QC.tsv"),
"Summary of sample info and QC for thalamus samples")## ...writing description of TABLE_thalamus_QC.tsv to public/output/01A/TABLE_thalamus_QC.desc5 Gather gene signatures
First, load all cluster markers and filter out markers for samples/clusters that we'll exclude:
clusters_keep <- info_clusters2 %>% filter(Note_cluster == "OK") %>% pull(Cluster)
cluster_markers <- map_dfr(1:nrow(info_samples), ~ read_tsv(here(file.path(info_samples[.x, ]$Path, "preprocessing/cluster_markers.tsv"))) %>%
tibble::add_column(Sample = info_samples[.x, ]$Sample)) %>%
# get positive markers
filter(avg_logFC > 0) %>%
mutate(cluster = as.numeric(cluster)) %>%
# add the cluster/sample info
left_join(info_clusters2 %>% select(Sample, Cluster_number, Cluster) %>% mutate(Cluster_number = as.numeric(as.character(Cluster_number))),
by = c("Sample" = "Sample", "cluster" = "Cluster_number")) %>%
rename(Cluster_number = cluster) %>%
# filter out excluded samples and clusters
filter(Cluster %in% clusters_keep) %>%
# get the diff between pct.1 and pct.2
mutate(pct_diff = pct.1 - pct.2) %>%
# reorder
select(gene, pct_diff, everything())
# sanity checks
unique(cluster_markers$Cluster) %>% length()## [1] 167any(cluster_markers$Sample %in% samples_drop)## [1] FALSEall(cluster_markers$Cluster %in% clusters_keep)## [1] TRUEhead(cluster_markers)# save
write_tsv(cluster_markers, glue("{out}/cluster_markers.tsv"))We'll also want to remove erythrocyte & melanocyte clusters, which are quite biologically distinct:
erythro_markers <- c("HBB", "HBA1", "HBA2", "HBZ", "HBG1")
(erythro_count <- cluster_markers %>% filter(gene %in% erythro_markers) %>% pull(Cluster) %>% table())## .
## GW19-T2_20 GW20-DT2_13 GW22-T1_14 GW22-T2_16 GW25-T_18 GW25-T_8
## 5 1 4 4 5 1# remove the clusters with 4-5 of these markers:
erythro_clusters <- names(erythro_count)[erythro_count >= 4]
melano_markers <- c("DCT", "MITF", "PMEL")
(melano_count <- cluster_markers %>% filter(gene %in% melano_markers) %>% pull(Cluster) %>% table())## .
## GW22-T1_15 GW22-T2_17 GW25-T_15
## 3 3 1# remove the clusters with all three of these:
melano_clusters <- names(melano_count)[melano_count == 3]6 Complete and refine cluster labels
We will manually update the cluster labels (provided by the author) in order to ensure all populations of interest are labelled. We will use the following markers:
- Astrocytes: FABP7, S100B, CLU, AQP4
- Neurons: STMN2, TUBB, SYT1
- Microglia: C1QC, LY86
- Ependymal: FOXJ1, DNAH10, KIF19, DNAH12, TEK1
- OPC: PDGFRA, OLIG1, OLIG2
- Glial progenitors: SOX2, OLIG1/2
After using these markers to update the labels outside of R, we load in the updated labels:
labels_manual <- read_tsv(here("data/scRNAseq/references/Bhaduri2021_fetal_brain_GW/2022-03-21-human_fetal_thalamus_labels_manual_SJ.tsv")) %>%
select(Cluster, Label_manual)
info_clusters3 <- info_clusters2 %>%
left_join(labels_manual) %>%
mutate(Note_cluster = case_when(
grepl("Outlier", Label_published) ~ "Exclude cluster, outlier",
Label_manual == "Erythrocytes" ~ "Exclude cluster, erythrocytes",
Label_manual == "Melanocytes" ~ "Exclude cluster, melanocytes",
TRUE ~ Note_cluster
)) %>%
mutate(ID_20220321 = case_when(
Note_sample != "OK" | Note_cluster != "OK" ~ paste0(Cluster, "_EXCLUDE"),
is.na(Label_manual) ~ ID_20220321,
!is.na(Label_manual) ~ paste0(Cluster, "_", Label_manual)
)) %>%
select(-Label_manual)
write_tsv(info_clusters3, glue("{out}/info_clusters3.tsv"))
clusters_keep <- info_clusters3 %>% filter(!grepl("EXCLUDE", ID_20220321)) %>% pull(Cluster)
length(clusters_keep) ## [1] 1607 Make reference for cell type projection
In this section, we prepare the reference matrix for cell type projection, containing the mean expression of each gene in each cluster.
7.1 Load data
load(here("data/scRNAseq/integrations/human_fetal_thalamus/output/seurat_joint.Rda"))7.2 Mean expression
Calculate mean expression per cluster:
Idents(seurat_joint) <- "Cluster"
hf_thalamus_meanexp <- AverageExpression(seurat_joint, return.seurat = TRUE, assays = "RNA", verbose = FALSE)
hf_thalamus_meanexp <- GetAssayData(hf_thalamus_meanexp)
# peek at the corner
dim(hf_thalamus_meanexp)## [1] 23955 217hf_thalamus_meanexp[1:5, 1:5]## CS19-T_1 CS19-T_0 CS19-T_4 CS19-T_6 CS19-T_2
## FO538757.2 0.642409234 0.671287431 0.659322505 0.72569247 0.67059287
## AP006222.2 0.379104823 0.457272354 0.424646100 0.53114856 0.58883959
## RP4-669L17.10 0.008780798 0.006391665 0.008229348 0.01997191 0.00430778
## RP11-206L10.9 0.116477049 0.102566086 0.039760352 0.15006909 0.03784705
## LINC00115 0.018969886 0.016771483 0.030140318 0.00000000 0.03769330hf_thalamus_meanexp <- hf_thalamus_meanexp[, clusters_keep]
# sanity checks
dim(hf_thalamus_meanexp)## [1] 23955 160length(clusters_keep)## [1] 160save(hf_thalamus_meanexp, file = glue("{out}/hf_thalamus_mean_expression.Rda"))
saveRDS(hf_thalamus_meanexp, glue("{out}/hf_thalamus_mean_expression.Rds"))7.3 Annotation
Make the annotation for the cell type projection scripts:
info_clusters3 %>%
filter(Cluster %in% clusters_keep) %>%
select(Cell_type = Cluster, ID_20220321) %>%
mutate(Color = case_when(
grepl("Proliferating OPC", ID_20220321) ~ palette_type[["Proliferating OPC"]],
grepl("OPC", ID_20220321) ~ palette_type[["OPC"]],
grepl("Oligo", ID_20220321) ~ palette_type[["Oligodendrocytes"]],
grepl("[Nn]euron", ID_20220321) ~ palette_type[["Neurons"]],
grepl("Astro", ID_20220321) ~ palette_type[["Astrocytes"]],
grepl("Ependymal", ID_20220321) ~ palette_type[["Ependymal"]],
grepl("Micro", ID_20220321) ~ palette_type[["Immune"]],
grepl("Endo", ID_20220321) ~ palette_type[["Vascular & other"]],
grepl("Dividing|RG", ID_20220321) ~ palette_type[["RGC"]],
)) %>%
select(-ID_20220321) %>%
write_tsv(glue("{out}/hf_thalamus_annotation.tsv"))8 Reproducibility
This document was last rendered on:
## 2022-09-08 14:54:28The 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: [4101e76] 2022-09-08: Update README.mdThe random seed was set with set.seed(100)
The R session info:
## Registered S3 method overwritten by 'cli':
## method from
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## 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 2022-09-08
##
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## P viridis * 0.5.1 2018-03-29 [?]
## P viridisLite * 0.4.0 2021-04-13 [?]
## P withr 2.4.2 2021-04-18 [?]
## P xfun 0.22 2021-03-11 [?]
## P xtable 1.8-4 2019-04-21 [?]
## P yaml 2.2.1 2020-02-01 [?]
## P zoo 1.8-9 2021-03-09 [?]
## source
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## [1] /lustre06/project/6004736/sjessa/from_narval/HGG-oncohistones/public/renv/library/R-3.6/x86_64-pc-linux-gnu
## [2] /tmp/RtmpRjPYXN/renv-system-library
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## P ── Loaded and on-disk path mismatch.The resources requested when this document was last rendered:
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A project of the Kleinman Lab at McGill University, using the rr reproducible research template.