NB-FOXR2 project [source]
06.2 - Project single-cell tumors to normal developing brain
Selin Jessa [selin.jessa@mail.mcgill.ca] and Bhavyaa Chandarana [bhavyaa.chandarana@mail.mcgill.ca]
05 November, 2024
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: 06.2# Specify where to save outputs
out <- here("output", doc_id); dir.create(out, recursive = TRUE)
figout <- here("figures", doc_id, "/")
cache <- paste0("/project/kleinman/bhavyaa.chandarana/cache/NB-FOXR2/public/", doc_id, "/")The root directory of this project is:
## /project/kleinman/bhavyaa.chandarana/from_hydra/2023-05-NB-FOXR2/publicOutputs and figures will be saved at these paths, relative to project root:
## public/output/06.2## public/figures/06.2//Setting a random seed:
set.seed(100)2 Overview
This document explores projections/mapping of NB-FOXR2 tumors to normal normal development using single-cell RNAseq data.
Here, we will load and inspect cell type projections for single-cell NB-FOXR2 data. These projections were run with developing mouse forebrain as a reference atlas.
Projection was performed with CoRAL (v3.0.0, https://github.com/fungenomics/CoRAL/releases/tag/v3.0.0).
3 Libraries & functions
library(here)
library(magrittr)
library(tidyr)
library(dplyr)
library(readr)
library(readxl)
library(stringr)
library(glue)
library(purrr)
library(ggplot2)
library(ggrepel)
library(tibble)
library(broom)
library(cowplot)
library(Seurat)
library(Signac)
library(ComplexHeatmap)
library(data.table)
library(ggExtra)
source(here("include/style.R"))
source(here("code/functions/RNAseq.R"))
source(here("code/functions/scRNAseq.R"))
source(here("code/functions/ssGSEA.R"))
ggplot2::theme_set(theme_min())
square_theme <- theme(aspect.ratio = 1)
large_text <- theme(text = element_text(size = 15))
conflicted::conflicts_prefer(dplyr::filter)# Function to load an RData file and return it
loadRData <- function(fileName){
load(fileName)
get(ls()[ls() != "fileName"])
}4 Load data
Here, we load metadata and Seurat objects for single-cell profiles of tumors. We will load the individual tumors (n=6) as separate objects in a list, as well as a joined object of all tumors, without any batch correction or integration ("naive").
4.1 Metadata
Load list of single-cell tumor samples.
meta_sc <- read_tsv(here("output/00/metadata_patients_sc.tsv"))## Rows: 16 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (23): Sample, ID_Patient, ID_Sample, ID, FOXR2_positive, Group, Source, ...
##
## 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.sc_samples_nb_foxr2 <- meta_sc %>% filter(Group == "NB-FOXR2") %>% pull(ID)4.2 Single-cell objects
Load single-cell tumor per-cell metadata and Seurat objects.
# Load per-sample seurat objects
samples_sc <- map(sc_samples_nb_foxr2, ~ loadRData(meta_sc[meta_sc$ID == .x, ]$Path))
names(samples_sc) <- sc_samples_nb_foxr2
# Load merged object metadata
load(here("output/04/merged_sc_meta.Rda"))
# correct a discrepancy in how barcodes are named for the multiome sample
samples_sc$`P-6778_S-10155`@meta.data$cell.barcode <- samples_sc$`P-6778_S-10155`@meta.data$gex_barcode
merged_sc_meta <- merged_sc_meta %>%
mutate(cell.barcode = ifelse(is.na(cell.barcode), gex_barcode, cell.barcode))
samples_sc <- map(samples_sc, function(seurat) {
# sanity check that cell order is preserved in each metadata slot
merged_barcodes <- merged_sc_meta %>% filter(cell.barcode %in% seurat@meta.data$cell.barcode) %>% pull(cell.barcode)
all(seurat@meta.data$cell.barcode == merged_barcodes)
tumor_normal <- merged_sc_meta %>% filter(cell.barcode %in% seurat@meta.data$cell.barcode) %>% pull(Malignant_normal)
seurat <- AddMetaData(seurat, tumor_normal, "Malignant_normal")
return(seurat)
})Print number of cells per sample:
# print number of cells per sample
map(samples_sc, ncol)## $NBFOXR2_6
## [1] 6340
##
## $`P-2236_S-2236`
## [1] 13911
##
## $`P-2273_S-2273`
## [1] 9653
##
## $`P-6776_S-10153`
## [1] 857
##
## $`P-6777_S-10154`
## [1] 1312
##
## $`P-6778_S-10155`
## [1] 2647# total number of cells in the analysis
map(samples_sc, ncol) %>% unname() %>% as.numeric() %>% sum()## [1] 347204.3 Joint object
Load joint object (without batch correction or integration) - used in figures.
load(here("data/singlecell/integrations/NB-FOXR2_naive_join/output/seurat_joint.Rda"))
seurat_joint$Joint_cluster <- Idents(seurat_joint)
seurat_joint@meta.data <- merged_sc_meta
# Sanity check: # cells in joint sample = total across individual samples
map(samples_sc, ncol) %>% unname() %>% as.numeric() %>% sum() == ncol(seurat_joint)## [1] TRUE5 Load reference information
Here, we load cell type categories and color palettes related to the developing murine brain atlas used as normal reference for this analysis, published in Jessa et al Nature Genetics 2022.
Since NB-FOXR2 are supratentorial tumors, we will only annotate cell types using forebrain reference (hindbrain data will be excluded).
Load the color palettes and cell type ontology for this reference.
# General broad palette
palette_broad_type <- c("Neuron" = "blue4",
"Glia" = "olivedrab3",
"Other" = "grey90",
"Unresolved" = "grey90",
"No consensus" = "grey90")
# Load Jessa forebrain palettes & cell type ontology
load(here("data/singlecell/references_normal/Jessa_NatGenet_2022/ontology_l2_palette.Rda"))
ontology_jessa <- ontology_jessa %>% select(Label, Label_broad = Label_broad_L3) %>%
tibble::add_row("Label" = "No consensus", "Label_broad" = "No consensus")
palette_jessa_l3 <- c(palette_jessa_l3, "No consensus" = "gray90", "Unresolved" = "gray90", "Mixed progenitors" = "yellow")Set a mapping from Label_broad (level3) to broader cell classes (Neuron/Glia/Other):
jessa_broad <- ontology_jessa %>%
mutate(broad_label_Jessa =
case_when(Label_broad %in% c("MGE inhibitory neurons",
"Excitatory IPC",
"Cortical excitatory neurons",
"Cortical inhibitory neurons",
"Inhibitory IPC",
"Cajal-Retzius neurons",
"Other neurons",
"Thalamic neurons",
"Striatal spiny neurons") ~ "Neuron",
Label_broad %in% c("Astrocytes",
"Ependymal",
"OPC",
"Oligodendrocytes",
"Gliogenic progenitors") ~ "Glia",
Label_broad %in% c("Ventral RGC",
"Dorsal RGC",
"Cortical hem",
"Rostral telencephalic midline",
"Meninges",
"Erythrocytes",
"Endothelial",
"Thalamic precursors",
"Pericytes",
"Immune",
"Mixed progenitors",
"Split cluster containing erythrocytes & neurons") ~ "Other",
Label_broad %in% c("No consensus",
"Unresolved") ~ "No consensus",
TRUE ~ as.character(NA)
)
)6 Load cell type annotation
Load the annotation labels output from CoRAL v3.0.0 (https://github.com/fungenomics/CoRAL/releases/tag/v3.0.0) run with developing mouse forebrain atlas as reference.
Here, we use a custom function get_consensus_labels (in code/functions/scRNAseq.R) to produce a consensus using broader class labels.
# Get consensus annotations in individual Seurat objects and add to each metadata
# Calculate majority vote consensus outside of CoRAL
samples_sc <- imap(samples_sc, function(seurat, alias) {
print(alias)
labels <- read_tsv(here(glue("data/singlecell/CoRAL/output/nbfoxr2_hg_Jessaforebrain/{alias}/JessaForebrain/majority/Prediction_Summary_base.tsv"))) %>%
get_consensus_labels(tools_to_include = c("Correlation", "SciBet", "SVMlinear", "singleCellNet"),
remove_timepoint = FALSE,
high_level = TRUE,
ontology = ontology_jessa,
suffix = "braindex")
labels$Consensus_broad_braindex <- factor(labels$Consensus_broad_braindex, levels = names(palette_jessa_l3))
labels <- labels %>% tibble::column_to_rownames(var = "Cell")
seurat <- AddMetaData(seurat, labels)
})Summarize these labels further to broad annotation (Neuron/Glia/Other) as per ontology in section 5.
samples_sc <- map(samples_sc,
function(seurat){
x <- seurat@meta.data %>%
select("Consensus_broad_braindex") %>%
rownames_to_column("Cell") %>%
left_join(., jessa_broad %>% select(Label_broad, broad_label_Jessa) %>% distinct(),
by = c("Consensus_broad_braindex" = "Label_broad")) %>%
select(-Consensus_broad_braindex) %>%
column_to_rownames("Cell")
x$broad_label_Jessa <- factor(x$broad_label_Jessa, levels = rev(names(palette_broad_type)))
seurat <- AddMetaData(seurat, x)
return(seurat)
}
)Transfer cell type annotation columns from individual sample objects to joint object.
# Bind together individual sample metadata
# Join it to the right of the seurat_joint metadata
# (preserving the cell order of the joint metadata)
joint_anno <- imap(samples_sc, ~ .x@meta.data %>%
select(cell.barcode,
Consensus_broad_braindex,
broad_label_Jessa) %>%
mutate(Sample = .y)) %>%
Reduce(rbind, .) %>%
left_join(seurat_joint@meta.data, ., by = c("cell.barcode", "Sample")) %>%
select(Consensus_broad_braindex,
broad_label_Jessa)
rownames(joint_anno) <- rownames(seurat_joint@meta.data)
joint_anno$Consensus_broad_braindex <- factor(joint_anno$Consensus_broad_braindex, levels = names(palette_jessa_l3))
joint_anno$broad_label_Jessa <- factor(joint_anno$broad_label_Jessa, levels = names(palette_broad_type))
seurat_joint <- AddMetaData(seurat_joint, joint_anno)7 Plot cell type annotation
Here, we visualize consensus cell type annotations produced through CoRAL (majority vote).
Define helper functions:
Define a function for bar plot of distribution of cell type annotation in malignant cells.
Plot the following cell types (in order), plus any other cell types which have > 2% of sample:
- "MGE inhibitory neurons"
- "Cortical excitatory neurons"
- "Other neurons"
- "Inhibitory IPC"
- "OPC"
- "Astrocytes"
- "Oligodendrocytes"
# Function to plot malignant cells only in a bar plot
plot_by_label_bar_mal_only <- function(seurat, id, min_cells, column) {
# Place these cell types in every plot, in the same order, to facilitate comparison between samples
all_plots <- c("MGE inhibitory neurons",
"Cortical excitatory neurons",
"Other neurons",
"Inhibitory IPC",
"OPC",
"Astrocytes",
"Oligodendrocytes")
order <- c(all_plots, setdiff(names(palette),
all_plots))
order <- setdiff(order, "No consensus")
# drop cell types with few cells, and no consensus cells
types_keep <- table(subset(seurat, subset = Malignant_normal == "Malignant")[[column]])
types_keep <- names(types_keep)[types_keep > min_cells]
types_keep <- setdiff(types_keep, "No consensus")
# Ensure all cell types in list "all_plots" are present even if <= min_cells
# Add any extra cell types which are > min_cells
order <- union(all_plots, setdiff(types_keep, all_plots))
p1 <- seurat@meta.data %>%
select(c(column, "Malignant_normal")) %>%
rename(Label = column) %>%
filter(Label %in% order) %>%
filter(Malignant_normal == "Malignant") %>%
mutate(Label = factor(Label, levels = rev(order))) %>%
ggplot(aes(x = Label)) +
geom_bar(aes(fill = Label), width = 0.5) +
scale_fill_manual(values = palette_jessa_l3) +
theme_min2() +
coord_flip() +
no_legend() +
ggtitle(id) +
scale_x_discrete(order, drop = FALSE) +
xlab(column) + ylab("# cells") +
theme(axis.title.y=element_blank()) +
rotate_x()
p1 + large_text
}7.1 Jessa forebrain
Tools run through CoRAL:
- Correlation
- SciBet
- SVMlinear
- singleCellNet
Plot all tumor samples in a joint space.
Remove cells labeled as "No consensus", and set color of normal cells to grey.
umap(subset(seurat_joint, subset = Consensus_broad_braindex != "No consensus"),
color_by = "Consensus_broad_braindex",
colors = palette_jessa_l3,
alpha = 0.5, label = FALSE, point_size = 1, legend = TRUE,
# rasterize points
rasterize = TRUE,
na_color = "grey80",
title = "Joint human NB-FOXR2",
cells = Seurat::WhichCells(seurat_joint, expression = Malignant_normal == "Malignant")) +
theme(legend.position = "bottom") +
mod_legend_col(title = NULL) +
square_theme +
large_text
Plot the individual samples separately.
# Consensus cell type labels
imap(samples_sc,
function(seurat, id) {
umap(subset(seurat,
subset = Consensus_broad_braindex != "No consensus"),
color_by = "Consensus_broad_braindex",
colors = palette_jessa_l3,
alpha = 0.5, label = FALSE, point_size = 1, legend = TRUE,
# rasterize points
rasterize = TRUE,
na_color = "grey80",
title = id,
cells = Seurat::WhichCells(seurat, expression = Malignant_normal == "Malignant")) +
theme(legend.position = "bottom") +
mod_legend_col(title = NULL) +
square_theme +
large_text
}) %>%
{plot_grid(plotlist = ., nrow = 1, align = "h", axis = "tb")}
In malignant cells only, plot the distribution of the following cell types (in order), plus any other cell types which have > 2% of sample or more cells:
- "MGE inhibitory neurons"
- "Cortical excitatory neurons"
- "Other neurons"
- "Inhibitory IPC"
- "OPC"
- "Astrocytes"
- "Oligodendrocytes"
column <- "Consensus_broad_braindex"
imap(samples_sc, ~ plot_by_label_bar_mal_only(seurat = .x,
id = .y,
min_cells = 0.02*ncol(.x),
column = column)) %>%
{plot_grid(plotlist = ., nrow = 1, align = "h", axis = "tb")}
In malignant cells only, plot the distribution of all cell types.
column <- "Consensus_broad_braindex"
imap(samples_sc, ~ plot_by_label_bar_mal_only(seurat = .x,
id = .y,
min_cells = 0,
column = column)) %>%
{plot_grid(plotlist = ., nrow = 1, align = "h", axis = "tb")}
Produce faceted UMAP plots to color each cell type separately:
map(unique(seurat_joint$Consensus_broad_braindex),
function(i) {
cells <- Seurat::WhichCells(seurat_joint, expression = Consensus_broad_braindex == i)
umap(seurat_joint,
color_by = "Consensus_broad_braindex",
colors = "#000000",
cells = cells,
rasterize = TRUE,
point_size = 0.8,
# put cells not selected in grey
na_color = "grey80",
label = FALSE,
legend = FALSE,
title = glue("{i} - {length(cells)} cells"),
hide_axes = T) +
square_theme +
large_text
}) %>%
cowplot::plot_grid(plotlist = ., nrow = 4, ncol = 4)
8 Calculate cell type proportions
It seems that most samples are predominantly composed of cells projected to MGE inhibitory neurons.
To confirm, we will calculate and print the percentage of cells per sample with this label, excluding the cells with "No consensus" label in the total cells per sample. For each sample we will also save a table counting number of cells per cell type.
We will use predictions from the majority vote consensus.
# Set desired order of cell types
jessa_celltype_order <- c("MGE inhibitory neurons",
"Oligodendrocytes",
"OPC",
"Inhibitory IPC",
"Cortical excitatory neurons",
"Other neurons",
"Astrocytes",
"Cortical inhibitory neurons",
"Striatal spiny neurons",
"Thalamic neurons",
"Cajal-Retzius neurons",
"Excitatory IPC",
"Ventral RGC",
"Dorsal RGC",
"Cortical hem",
"Rostral telencephalic midline",
"Thalamic precursors",
"Gliogenic progenitors",
"Ependymal",
"Endothelial",
"Pericytes",
"Immune",
"Meninges",
"Mixed progenitors",
"No consensus",
"Unresolved")
# Add new metadata column to each sample with the anonymized sample name
for(sample_name in names(samples_sc)){
samples_sc[[sample_name]]@meta.data <-
samples_sc[[sample_name]]@meta.data %>%
mutate(sample_id_paper = sample_name)
}
# Loop through samples, calculate proportion MGE, and write full
for (sample in names(samples_sc)){
seurat <- samples_sc[[sample]]
print(seurat@meta.data[["sample_id_paper"]] %>% unique)
proportion <- seurat@meta.data %>%
select(Malignant_normal, Consensus_broad_braindex) %>%
filter(Malignant_normal == "Malignant") %>%
table() %>%
as.data.frame()
proportion_excl_no_consensus <- proportion %>%
filter(Consensus_broad_braindex != "No consensus")
percent_mge_excl_no_consensus <- (proportion_excl_no_consensus[which(proportion_excl_no_consensus$Consensus_broad_braindex == "MGE inhibitory neurons"),]$Freq)/(sum(proportion_excl_no_consensus$Freq))
# Proportion values per sample
print(c("% malignant cells labeled MGE, excluding cells with no consensus" = percent_mge_excl_no_consensus))
# Save table of # cells per cell type a tsv file
rownames(proportion) <-
proportion %>%
rr_write_tsv(path = glue("{out}/NB-FOXR2-{sample}-cell-type-distribution.tsv"),
desc = glue("Number of malignant cells per projected cell type in NB-FOXR2 sample {sample}"))
}## [1] "NBFOXR2_6"
## % malignant cells labeled MGE, excluding cells with no consensus
## 0.5949729## ...writing description of NB-FOXR2-NBFOXR2_6-cell-type-distribution.tsv to public/output/06.2/NB-FOXR2-NBFOXR2_6-cell-type-distribution.desc## [1] "P-2236_S-2236"
## % malignant cells labeled MGE, excluding cells with no consensus
## 0.5700052## ...writing description of NB-FOXR2-P-2236_S-2236-cell-type-distribution.tsv to public/output/06.2/NB-FOXR2-P-2236_S-2236-cell-type-distribution.desc## [1] "P-2273_S-2273"
## % malignant cells labeled MGE, excluding cells with no consensus
## 0.8561583## ...writing description of NB-FOXR2-P-2273_S-2273-cell-type-distribution.tsv to public/output/06.2/NB-FOXR2-P-2273_S-2273-cell-type-distribution.desc## [1] "P-6776_S-10153"
## % malignant cells labeled MGE, excluding cells with no consensus
## 0.7011309## ...writing description of NB-FOXR2-P-6776_S-10153-cell-type-distribution.tsv to public/output/06.2/NB-FOXR2-P-6776_S-10153-cell-type-distribution.desc## [1] "P-6777_S-10154"
## % malignant cells labeled MGE, excluding cells with no consensus
## 0.4056886## ...writing description of NB-FOXR2-P-6777_S-10154-cell-type-distribution.tsv to public/output/06.2/NB-FOXR2-P-6777_S-10154-cell-type-distribution.desc## [1] "P-6778_S-10155"
## % malignant cells labeled MGE, excluding cells with no consensus
## 0.05577957## ...writing description of NB-FOXR2-P-6778_S-10155-cell-type-distribution.tsv to public/output/06.2/NB-FOXR2-P-6778_S-10155-cell-type-distribution.desc9 Plot gene expression
Here, we investigate the expression of specific gene markers in single cell tumor profiles.
9.1 MDM4 expression
Plot MDM4 expression in tumor cells of each single-cell sample.
# Get color palette for single-cell samples
palette_sample <- read_tsv(here("output/00/sc_info.samples.tsv")) %>%
select(Sample, Color) %>% tibble::deframe()## Rows: 6 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (5): Path, Sample, Technology, Group, Color
##
## 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.VlnPlot(object = subset(seurat_joint, Malignant_normal == "Malignant"),
features = c("MDM4"), cols = palette_sample,
group.by = "Sample",pt.size = 0)
9.2 Neuronal markers
Plot interneuron and excitatory neuron marker expression in malignant cell types per sample.
# Set neuronal gene markers
min_markers <- c("SNAP25", "STMN2", # Pan-neuronal
"GAD1", "GAD2", # pan-GABAergic
"DLX5", "DLX6", # TF fingerprint
"LHX6", "SST", "NXPH1", # interneuron
"SLC17A7") # pan-glutamatergic
# Plot violin grid with 1 row per sample with final anonymized names
(p <- violin_grid(seurat = subset(seurat_joint, Malignant_normal == "Malignant"),
genes = min_markers,
order = "group",
group_col = "Sample",
points = FALSE,
title = "Malignant cells per sample",
scale = "width",
colours = palette_sample,
gene_angle = 90) + large_text)
10 Save metadata
seurat_tumor_metadata <- map(samples_sc,
function(seurat) {
meta <- seurat@meta.data %>%
as.data.frame() %>%
rownames_to_column("Cell")
rownames(meta) <- NULL
return(meta)
})
save(seurat_tumor_metadata, file = glue("{out}/seurat_tumor_metadata.Rda"))
seurat_tumor_metadata_joint <- seurat_joint@meta.data %>%
as.data.frame() %>%
rownames_to_column("Cell")
rownames(seurat_tumor_metadata_joint) <- NULL
save(seurat_tumor_metadata_joint, file = glue("{out}/seurat_tumor_metadata_joint.Rda"))These can be added back to the objects as follows:
load(glue("{out}/seurat_tumor_metadata.Rda"))
samples_sc <- map2(samples_sc, seurat_tumor_metadata,
function(seurat, meta) {
seurat@meta.data <- meta %>%
column_to_rownames("Cell")
return(seurat)
})
load(glue("{out}/seurat_tumor_metadata_joint.Rda"))
seurat_joint@meta.data <- seurat_tumor_metadata_joint %>%
column_to_rownames("Cell")11 Reproducibility
This document was last rendered on:
## 2024-11-05 10:54:19The git repository and last commit:
## Local: main /project/kleinman/bhavyaa.chandarana/from_hydra/2023-05-NB-FOXR2/public
## Remote: main @ origin (https://github.com/fungenomics/NB-FOXR2.git)
## Head: [72d1c5c] 2024-11-04: Add DOI badgeThe random seed was set with set.seed(100)
The R session info:
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## setting value
## version R version 4.1.2 (2021-11-01)
## os Rocky Linux 8.10 (Green Obsidian)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Toronto
## date 2024-11-05
## pandoc 1.19.2.1 @ /cvmfs/soft.computecanada.ca/gentoo/2020/usr/bin/ (via rmarkdown)
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## P cowplot * 1.1.1 2020-12-30 [?] CRAN (R 4.1.2)
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## P data.table * 1.14.2 2021-09-27 [?] CRAN (R 4.1.2)
## P deldir 1.0-6 2021-10-23 [?] CRAN (R 4.1.2)
## P devtools 2.4.5 2022-10-11 [?] CRAN (R 4.1.2)
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## P docopt 0.7.1 2020-06-24 [?] CRAN (R 4.1.2)
## P doParallel 1.0.16 2020-10-16 [?] CRAN (R 4.1.2)
## P dplyr * 1.1.1 2023-03-22 [?] CRAN (R 4.1.2)
## P ellipsis 0.3.2 2021-04-29 [?] CRAN (R 4.1.2)
## P evaluate 0.23 2023-11-01 [?] CRAN (R 4.1.2)
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## P fastmap 1.1.0 2021-01-25 [?] CRAN (R 4.1.2)
## P fastmatch 1.1-3 2021-07-23 [?] CRAN (R 4.1.2)
## P fitdistrplus 1.1-6 2021-09-28 [?] CRAN (R 4.1.2)
## P foreach 1.5.1 2020-10-15 [?] CRAN (R 4.1.2)
## P fs 1.5.2 2021-12-08 [?] CRAN (R 4.1.2)
## P future 1.25.0 2022-04-24 [?] CRAN (R 4.1.2)
## P future.apply 1.8.1 2021-08-10 [?] CRAN (R 4.1.2)
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## P GenomeInfoDb 1.30.1 2022-01-30 [?] Bioconductor
## P GenomeInfoDbData 1.2.4 2023-11-28 [?] Bioconductor
## P GenomicRanges 1.46.1 2021-11-18 [?] Bioconductor
## P GetoptLong 1.0.5 2020-12-15 [?] CRAN (R 4.1.2)
## P ggbeeswarm 0.7.1 2022-12-16 [?] CRAN (R 4.1.2)
## P ggExtra * 0.10.0 2022-03-23 [?] CRAN (R 4.1.2)
## P ggforce 0.3.3 2021-03-05 [?] CRAN (R 4.1.2)
## P ggplot2 * 3.4.2 2023-04-03 [?] CRAN (R 4.1.2)
## P ggrastr 1.0.1 2021-12-08 [?] CRAN (R 4.1.2)
## P ggrepel * 0.9.1 2021-01-15 [?] CRAN (R 4.1.2)
## P ggridges 0.5.3 2021-01-08 [?] CRAN (R 4.1.2)
## P ggseqlogo 0.1 2017-07-25 [?] CRAN (R 4.1.2)
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## P highr 0.9 2021-04-16 [?] CRAN (R 4.1.2)
## P hms 1.1.1 2021-09-26 [?] CRAN (R 4.1.2)
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## P htmlwidgets 1.5.4 2021-09-08 [?] CRAN (R 4.1.2)
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## P jsonlite 1.8.8 2023-12-04 [?] CRAN (R 4.1.2)
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## P readr * 2.1.1 2021-11-30 [?] CRAN (R 4.1.2)
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## [2] /home/kleinman/bhavyaa.chandarana/.cache/R/renv/sandbox/R-4.1/x86_64-pc-linux-gnu/145cef2c
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A project of the Kleinman Lab at McGill University, using the rr reproducible research template.