HGG-oncohistones project [source]
01B - Prepare human fetal hindbrain 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: 01B# 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/01B## public/figures/01BSetting a random seed:
set.seed(100)2 Overview
In this analysis, we'll prepare the reference from the human fetal hindbrain data which has been processed with our single-cell workflow, and gather the required formats and outputs needed for comparisons to tumors:
- annotated 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.
The structure of this document largely follows the analogous document for the thalamus (01A), however there are no published labels used for this brain region.
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(feather)
library(Seurat)
library(icytobox)
library(ggrepel)
source(here("include/style.R"))
source(here("code/functions/scRNAseq.R"))
ggplot2::theme_set(theme_min())3.1 Metadata
info_samples <- read_tsv(here("data/metadata/metadata_human_fetal_10X.tsv")) %>%
filter(Region %in% c("Hindbrain", "Cerebellum"))
palette_sample <- info_samples %>% select(Sample, Sample_color) %>% tibble::deframe()
palette_alias <- info_samples %>% select(Alias, Sample_color) %>% tibble::deframe()
ages <- info_samples$Age %>% unique %>% sort()
palette_age <- colorRampPalette(brewer.pal(8, "YlGnBu"))(n = length(ages))
names(palette_age) <- ages3.2 QC
qc_stats <- map_dfr(1:nrow(info_samples), ~ read_tsv(here(file.path(info_samples[.x, ]$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)
3.3 Prep objects
For each sample:
- load and plot the Seurat object
- count the number of cells in each cluster, and give each cluster a label prefixed w/ sample name
- add the correlations predictions to the object
- save the object
prep_seurat <- function(path_to_sample) {
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 by correlations and add them to the Seurat object
labels_cor <- read_tsv(file.path(path_to_sample, "correlations/ref.joint_mouse_extended/correlation.predicted_labels.tsv")) %>%
tibble::column_to_rownames(var = "cell")
seurat <- AddMetaData(seurat,
metadata = labels_cor,
col.name = c("COR_ref.joint_mouse_extended", "COR_ref.joint_mouse_extended_score"))
# 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 correlations (excluding the information
# of the timepoint from the mouse clusters)
best_match <- seurat@meta.data %>% rowwise() %>%
mutate(Type = stringr::str_split_fixed(COR_ref.joint_mouse_extended, "_", 2) %>% getElement(2)) %>%
group_by(Cluster_number, Type) %>%
mutate(n = n()) %>%
group_by(Cluster_number) %>% top_n(1, n) %>%
slice(1) %>%
select(Cluster_number, n, Type, COR_ref.joint_mouse_extended)
# sanity check
nrow(info_samples_cluster) == nrow(best_match)
info_samples_cluster <- info_samples_cluster %>%
left_join(best_match, by = "Cluster_number") %>%
mutate(Prop_matched_mouse_atlas = round(n/N_cells, 2)) %>%
select(-n) %>%
mutate(ID_20210610 = paste0(Cluster, "_", Type)) %>%
select(-Type)
# put the unique cluster labels in the Seurat object
seurat$Cluster <- plyr::mapvalues(seurat$Cluster_number,
from = info_samples_cluster$Cluster_number,
to = info_samples_cluster$Cluster)
Idents(seurat) <- "Cluster"
seurat$ID_20210610 <- plyr::mapvalues(seurat$Cluster_number,
from = info_samples_cluster$Cluster_number,
to = info_samples_cluster$ID_20210610)
# correct the palette
names(seurat@misc$colours) <- info_samples_cluster$Cluster
# save the new seurat object
# save(seurat, file = here(file.path(path_to_sample, "seurat.Rda")))
return(info_samples_cluster)
}
# gather all cluster summary info
info_clusters <- map_dfr(1:nrow(info_samples), ~ prep_seurat(info_samples[.x, ]$Path))
write_tsv(info_clusters, glue("{out}/info_clusters.tsv"))info_clusters <- read_tsv(glue("{out}/info_clusters.tsv"))3.4 Sample exclusions
No samples will be excluded due to low QC.
3.5 Per-cluster QC
info_clusters %>% nrow()## [1] 161info_clusters2 <- info_clusters %>%
mutate(Note_sample = "OK",
Note_cluster = case_when(
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] 141write_tsv(info_clusters2, glue("{out}/info_clusters2.tsv"))
# total # of cells
sum(info_clusters2$N_cells)## [1] 794283.6 QC tables
TABLE_hindbrain_qc <- info_samples %>%
left_join(qc_stats, by = "Sample") %>%
mutate(Publication = case_when(
grepl("CS", Sample) ~ "Eze et al, Nature Neuroscience, 2021",
grepl("GW", Sample) ~ "Bhaduri et al, Nature, 2021"
)) %>%
mutate(Note_sample = "OK") %>%
select(
# Sample info
Sample, Age, Alias, Region, Publication,
# QC
# N_reads = `Number of Reads`,
# N_cells_estimated = `Estimated Number of Cells`,
N_cells_after_filtering = N_cells_after,
# Reads_mapped_to_genome = `Reads Mapped to Genome`,
# Reads_mapped_to_transcriptome = `Reads Mapped Confidently to Transcriptome`,
Mean_mitochondrial_content_after_filtering = percent.mito_mean.postQC,
Mean_UMIs_after_filtering = nCount_RNA_mean.postQC,
Mean_N_genes_after_filtering = nFeature_RNA_mean.postQC,
Max_mito_threshold = percent.mito_max.threshold,
Min_N_genes_threshold = nFeature_RNA_min.threshold,
Max_N_genes_threshold = nFeature_RNA_max.threshold,
Max_UMIs_threshold = nCount_RNA_max.threshold,
Note_sample)
rr_write_tsv(TABLE_hindbrain_qc,
glue("{out}/TABLE_hindbrain_QC.tsv"),
"Summary of sample info and QC for hindbrain samples")## ...writing description of TABLE_hindbrain_QC.tsv to public/output/01B/TABLE_hindbrain_QC.desc4 Gather gene signatures
First, load all cluster markers and filter out markers for samples/clusters that we'll exclude:
clusters_keep <- info_clusters2 %>% filter(Note_cluster == "OK") %>% pull(Cluster)
cluster_markers <- map_dfr(1:nrow(info_samples), ~ read_tsv(here(file.path(info_samples[.x, ]$Path, "preprocessing/cluster_markers.tsv"))) %>%
tibble::add_column(Sample = info_samples[.x, ]$Sample)) %>%
# get positive markers
filter(avg_logFC > 0) %>%
mutate(cluster = as.numeric(cluster)) %>%
# add the cluster/sample info
left_join(info_clusters2 %>% select(Sample, Cluster_number, Cluster), by = c("Sample" = "Sample", "cluster" = "Cluster_number")) %>%
rename(Cluster_number = cluster) %>%
# filter out excluded samples and clusters
filter(Cluster %in% clusters_keep) %>%
# get the diff between pct.1 and pct.2
mutate(pct_diff = pct.1 - pct.2) %>%
# reorder
select(gene, pct_diff, everything())
# sanity checks
unique(cluster_markers$Cluster) %>% length()## [1] 141all(cluster_markers$Cluster %in% clusters_keep)## [1] TRUEhead(cluster_markers)# save
write_tsv(cluster_markers, glue("{out}/cluster_markers.tsv"))Second, filter these down to 100-gene signatures:
#' Create 100-gene signature given the markers for one cluster
filter_markers <- function(markers, sp = "hg", n_top = 100) {
markers %>%
# filter out mitochondrial and ribosomal genes
dplyr::filter(., !grepl("RPS|RPL|MRPS|MRPL|^MT-", gene)) %>%
group_by(Cluster) %>%
# get positive markers
dplyr::filter(avg_logFC > 0) %>%
# sort by p-val
arrange(p_val_adj) %>%
# make sure they're unique
distinct(gene, .keep_all = TRUE) %>%
dplyr::slice(1:n_top) %>%
ungroup()
}
# read gene annotation
anno <- read_tsv(here("data/misc/gene_annotation_with_homologous_mice_genes.txt"))##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## hsapiens_ensembl_gene_id = col_character(),
## hsapiens_external_gene_id = col_character(),
## mmusculus_homolog_ensembl_gene = col_character(),
## mmusculus_external_gene_id = col_character(),
## gene_biotype = col_character(),
## description = col_character()
## )hindbrain_signatures_df <- cluster_markers %>%
left_join(select(anno,
hsapiens_external_gene_id,
hsapiens_ensembl_gene_id,
mmusculus_external_gene_id,
mmusculus_ensembl_gene_id = mmusculus_homolog_ensembl_gene),
by = c("gene" = "hsapiens_external_gene_id")) %>%
filter_markers()4.1 Signature QC
Compute some stats to assess how specific these are:
# Sanity check
sort(table(hindbrain_signatures_df$Cluster))##
## CS13-H_0 CS15-H_2 CS13-H_1 CS14-H1_0 CS13-H_7 CS15-H_0
## 6 8 10 12 20 20
## CS14-H1_1 CS19-H_5 CS15-H_3 CS14-H1_8 CS14-H1_9 CS13-H_4
## 23 23 27 33 34 35
## CS19-H_9 CS14-H1_3 CS14-H1_6 CS13-H_5 CS13-H_11 CS14-H1_11
## 39 45 47 49 50 51
## CS22-Cb_13 CS14-H2_2 CS22-Cb_3 CS13-H_3 CS13-H_13 CS22-H_0
## 52 54 56 63 73 73
## CS15-H_7 GW20-Cb_0 CS20-Cb1_5 CS20-Cb2_0 CS14-H1_5 CS14-H1_4
## 76 76 84 84 85 86
## CS19-H_3 CS14-H1_15 CS22-H_7 CS13-H_2 CS20-Cb1_0 CS14-H1_12
## 88 89 89 93 95 96
## CS14-H2_9 CS14-H2_0 CS19-H_13 GW20-Cb_10 CS22-Cb_1 CS13-H_10
## 96 98 98 98 99 100
## CS13-H_12 CS13-H_6 CS13-H_8 CS13-H_9 CS14-H1_10 CS14-H1_13
## 100 100 100 100 100 100
## CS14-H1_14 CS14-H1_16 CS14-H1_17 CS14-H1_18 CS14-H1_2 CS14-H1_7
## 100 100 100 100 100 100
## CS14-H2_1 CS14-H2_10 CS14-H2_3 CS14-H2_4 CS14-H2_5 CS14-H2_6
## 100 100 100 100 100 100
## CS14-H2_7 CS14-H2_8 CS15-H_1 CS15-H_4 CS15-H_5 CS15-H_6
## 100 100 100 100 100 100
## CS15-H_8 CS19-H_0 CS19-H_1 CS19-H_10 CS19-H_11 CS19-H_12
## 100 100 100 100 100 100
## CS19-H_14 CS19-H_15 CS19-H_16 CS19-H_17 CS19-H_18 CS19-H_19
## 100 100 100 100 100 100
## CS19-H_2 CS19-H_4 CS19-H_6 CS19-H_7 CS19-H_8 CS20-Cb1_1
## 100 100 100 100 100 100
## CS20-Cb1_10 CS20-Cb1_11 CS20-Cb1_2 CS20-Cb1_3 CS20-Cb1_4 CS20-Cb1_6
## 100 100 100 100 100 100
## CS20-Cb1_7 CS20-Cb1_8 CS20-Cb1_9 CS20-Cb2_1 CS20-Cb2_10 CS20-Cb2_2
## 100 100 100 100 100 100
## CS20-Cb2_3 CS20-Cb2_4 CS20-Cb2_6 CS20-Cb2_7 CS20-Cb2_8 CS20-Cb2_9
## 100 100 100 100 100 100
## CS22-Cb_0 CS22-Cb_10 CS22-Cb_11 CS22-Cb_12 CS22-Cb_2 CS22-Cb_4
## 100 100 100 100 100 100
## CS22-Cb_5 CS22-Cb_6 CS22-Cb_7 CS22-Cb_8 CS22-Cb_9 CS22-H_1
## 100 100 100 100 100 100
## CS22-H_10 CS22-H_11 CS22-H_12 CS22-H_13 CS22-H_2 CS22-H_3
## 100 100 100 100 100 100
## CS22-H_4 CS22-H_5 CS22-H_6 CS22-H_8 CS22-H_9 GW20-Cb_1
## 100 100 100 100 100 100
## GW20-Cb_11 GW20-Cb_12 GW20-Cb_13 GW20-Cb_14 GW20-Cb_15 GW20-Cb_16
## 100 100 100 100 100 100
## GW20-Cb_17 GW20-Cb_18 GW20-Cb_19 GW20-Cb_20 GW20-Cb_3 GW20-Cb_4
## 100 100 100 100 100 100
## GW20-Cb_7 GW20-Cb_8 GW20-Cb_9
## 100 100 100hindbrain_signatures_df_qc <- hindbrain_signatures_df %>%
group_by(Sample, Cluster) %>%
summarise(median_logFC = median(avg_logFC),
max_logFC = max(avg_logFC),
n_gene_FC_above_1.5 = sum(avg_logFC > log2(1.5)),
median_pct.1 = median(pct.1),
median_pct.2 = median(pct.2)) %>%
arrange(median_logFC)## `summarise()` has grouped output by 'Sample'. You can override using the `.groups` argument.# save intermediate files
write_tsv(hindbrain_signatures_df, glue("{out}/hindbrain_signatures_df.tsv"))
write_tsv(hindbrain_signatures_df_qc, glue("{out}/hindbrain_signatures_df_qc.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())## .
## CS14-H1_15 CS15-H_7 GW20-Cb_20
## 4 4 4# 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())## .
## CS13-H_12 CS13-H_8 CS13-H_9 CS14-H1_14 CS14-H2_10 CS14-H2_5 CS14-H2_7
## 1 1 1 1 1 1 1
## GW20-Cb_19
## 1# remove the clusters with all three of these:
melano_clusters <- names(melano_count)[melano_count == 3]Filter out signatures which have outlier QC stats, indicative of non-specific signatures:
hindbrain_signatures_df_qc_filt <- hindbrain_signatures_df_qc %>%
filter(median_pct.2 < 0.4) %>%
filter(n_gene_FC_above_1.5 > 10)
signatures_keep <- hindbrain_signatures_df_qc_filt$Cluster
length(signatures_keep)## [1] 106info_clusters3 <- info_clusters2 %>%
mutate(Note_cluster = case_when(
Cluster %in% erythro_clusters ~ "Erythrocyte cluster, excluded",
Cluster %in% melano_clusters ~ "Melanocyte cluster, excluded",
TRUE ~ Note_cluster
)) %>%
mutate(Note_signature = case_when(
Note_cluster != "OK" ~ "Excluded, see Note_sample/Note_cluster",
!(Cluster %in% signatures_keep) ~ "Non-specific signature",
TRUE ~ "OK"
))
write_tsv(info_clusters3, glue("{out}/info_clusters3.tsv"))
clusters_drop <- c(erythro_clusters, melano_clusters)Convert to a list:
hindbrain_signatures_df_filt <- hindbrain_signatures_df %>%
filter(Cluster %in% signatures_keep & !(Cluster %in% clusters_drop))
hf_hindbrain_signatures <- list('hg_sym' = split(hindbrain_signatures_df, f = hindbrain_signatures_df$Cluster) %>% map(~ pull(.x, gene) %>% .[!is.na(.)]),
'hg_ens' = split(hindbrain_signatures_df, f = hindbrain_signatures_df$Cluster) %>% map(~ pull(.x, hsapiens_ensembl_gene_id) %>% .[!is.na(.)]))
save(hf_hindbrain_signatures, file = glue("{out}/hindbrain_signatures.Rda"))5 Join data
Next, let's write out the config for integrating these samples:
info_samples2 <- info_samples %>%
select(Path, Sample = Alias, Age, Color = Sample_color) %>%
mutate(Path = paste0(Path, "/seurat.Rda")) %>%
write_tsv(glue("{out}/info.samples.tsv"))
info_groups <- data.frame("Covariate" = "Age",
"Levels" = unique(info_samples$Age),
"Color" = c("#7bccc4", "#ccebc5", "#fdd49e", "#fc8d59", "#ef6548", "#d7301f", "#b30000", "#7f0000")) %T>%
write_tsv(glue("{out}/info.groups.tsv"))5.1 Annotation
Make the annotation for the cell type projection scripts:
# set cluster color as best-matching cluster in mouse atlas
palette_mouse_df <- palette_joint_mouse_extended %>% tibble::enframe()
cluster_palette_mouse_match <- info_clusters3 %>%
left_join(palette_mouse_df, by = c("COR_ref.joint_mouse_extended" = "name")) %>%
select(Cluster, value) %>%
tibble::deframe()
# write annotation
cluster_palette_mouse_match %>%
tibble::enframe("Cell_type", "Color") %>%
write_tsv(glue("{out}/hf_hindbrain_annotation.tsv"))6 Reproducibility
This document was last rendered on:
## 2022-09-08 14:54:02The 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:
## 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 2022-09-08
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## ! package * version date lib
## P abind 1.4-5 2016-07-21 [?]
## P assertthat 0.2.1 2019-03-21 [?]
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## P ps 1.6.0 2021-02-28 [?]
## P purrr * 0.3.4 2020-04-17 [?]
## P R6 2.5.0 2020-10-28 [?]
## P RANN 2.6.1 2019-01-08 [?]
## P RColorBrewer * 1.1-2 2014-12-07 [?]
## P Rcpp 1.0.6 2021-01-15 [?]
## P RcppAnnoy 0.0.18 2020-12-15 [?]
## P readr * 1.4.0 2020-10-05 [?]
## P readxl * 1.3.1 2019-03-13 [?]
## P remotes 2.1.1 2020-02-15 [?]
## renv 0.14.0 2021-07-21 [1]
## P reshape2 1.4.4 2020-04-09 [?]
## P reticulate 1.20 2021-05-03 [?]
## P rlang 0.4.11 2021-04-30 [?]
## P rmarkdown 2.8 2021-05-07 [?]
## P ROCR 1.0-11 2020-05-02 [?]
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## P rprojroot 2.0.2 2020-11-15 [?]
## P rstudioapi 0.13 2020-11-12 [?]
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## P Rtsne 0.15 2018-11-10 [?]
## P sass 0.4.0 2021-05-12 [?]
## P scales 1.1.1 2020-05-11 [?]
## P sctransform 0.3.2 2020-12-16 [?]
## P sessioninfo 1.1.1 2018-11-05 [?]
## P Seurat * 3.2.1 2020-09-07 [?]
## P shiny 1.6.0 2021-01-25 [?]
## P spatstat 1.64-1 2020-05-12 [?]
## P spatstat.data 2.1-0 2021-03-21 [?]
## P spatstat.utils 2.1-0 2021-03-15 [?]
## P stringi 1.6.1 2021-05-10 [?]
## P stringr 1.4.0 2019-02-10 [?]
## P survival 3.2-11 2021-04-26 [?]
## P tensor 1.5 2012-05-05 [?]
## P testrmd 0.0.1.9000 2021-12-06 [?]
## P testthat 2.3.2 2020-03-02 [?]
## P tibble 3.1.1 2021-04-18 [?]
## P tidyr * 1.1.3 2021-03-03 [?]
## P tidyselect 1.1.1 2021-04-30 [?]
## P usethis 1.6.1 2020-04-29 [?]
## P utf8 1.2.1 2021-03-12 [?]
## P uwot 0.1.10 2020-12-15 [?]
## P vctrs 0.3.8 2021-04-29 [?]
## 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/RtmpjmcRci/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.