NB-FOXR2 project [source]
08 - Extracranial neuroblastoma
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: 08# 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/08## public/figures/08//Setting a random seed:
set.seed(100)2 Overview
This document compares NB-FOXR2 with extracranial neuroblastoma samples stratified by stage and FOXR2 expression.
3 Libraries
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(cowplot)
library(Seurat)
library(data.table)
library(MetBrewer)
library(dendextend)
library(readxl)
library(GSVA)
library(clusterProfiler)
library(org.Hs.eg.db)
library(GO.db)
library(ComplexHeatmap)
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::select)
conflicted::conflicts_prefer(dplyr::filter)
conflicted::conflicts_prefer(dplyr::first)
conflicted::conflicts_prefer(dplyr::last)
conflicted::conflicts_prefer(dplyr::combine)
conflicted::conflicts_prefer(dplyr::intersect)
conflicted::conflicts_prefer(base::setdiff)
conflicted::conflicts_prefer(clusterProfiler::slice)
conflicted::conflicts_prefer(dplyr::rename)
conflicted::conflicts_prefer(dplyr::desc)
conflicted::conflicts_prefer(dplyr::filter)4 Load EC-NB samples
4.1 Gartlgruber et al. 2021 (n=579)
Publication: Gartlgruber et al. Nature Cancer 2021
4.1.1 Load data
This data was downloaded from a Shiny app provided by the authors of the study.
Data was provided as un-normalized bulk counts and per-tumor metadata.
Load DESeq normalized counts produced in in R markdown document 03 for plotting gene expression.
# Load files and remove unused ones to save space
load(here("output/03/Gartlgruber_et_al_counts.Rda"))
rm(ecnb_counts_norm)
rm(ecnb_counts_vst)4.1.2 Add FOXR2 positivity to metadata
Here, we label each tumor as FOXR2 positive or negative in per-tumor metadata based on normalized expression of FOXR2. We consider tumors with FOXR2 DESeq normalized expression > 2 to be FOXR2 positive.
# Set threshold of FOXR2+ expression used throughout this document
threshold <- 2
ecnb_foxr2_counts <- ecnb_counts_tidy %>%
filter(gene_symbol == "FOXR2")
foxr2_pos_samples <- ecnb_foxr2_counts %>%
filter(gene_expression > threshold) %>%
.$sample
ecnb_foxr2_counts <- ecnb_foxr2_counts %>% mutate(FOXR2_positive = case_when(sample %in% foxr2_pos_samples ~ "Y",
T ~ "N"))
# Print the number of FOXR2 positive and negative samples
ecnb_foxr2_counts %>% count(FOXR2_positive, sort = F)# Print the number of FOXR2 positive and negative samples in each tumor stage
ecnb_foxr2_counts %>% count(Stage, FOXR2_positive, sort = F)ecnb_counts_tidy <- ecnb_counts_tidy %>%
mutate(FOXR2_positive = case_when(sample %in% foxr2_pos_samples ~ "FOXR2+",
T ~ "FOXR2-"))4.2 TARGET cohort (n=128)
Downloaded and processed by Steven Hébert in April 2024.
They were downloaded as count matrices from the public Genomic 686 Data Commons (GDC) cancer portal (https://portal.gdc.cancer.gov/) using the Cohort Builder, with options:
project = TARGET-346 NBLExperimental Strategy = RNA-seqAccess = open
4.2.1 Load data
Patients were subsetted to Risk = HR (high risk) and Stage = 3 or 4 (not 4S). This resulted in a total of 128 patient samples.
Load patient sample metadata.
# Load, replace the empty value '-- with NA values and remove all-NA columns,
# and rename id column to Sample
target_meta <- data.table::fread(here("data/RNAseq/external_data/TARGET_ECNB/clinical.tsv")) %>%
apply(2,
function(col) {
gsub(col, pattern = "'--", replacement = as.character(NA))
}
) %>%
as.data.frame() %>%
select_if(~ !all(is.na(.))) %>%
dplyr::rename(Sample = case_submitter_id)Load DESeq-normalized counts (for plotting gene expression).
# load normalized counts
target_norm <- here("data/RNAseq/external_data/TARGET_ECNB/counts_ensembl_common/GENCODEv36.norm.tsv.gz") %>%
read.table(header = T, row.names = 1, check.names = F, sep = "\t")
# Display first 5 row/col to check
target_norm[1:5, 1:5]4.2.2 Add FOXR2 positivity to metadata
Based on threshold = 2.
# DESeq norm expression threshold used for FOXR2 +/-
threshold <- 2
target_counts_tidy <- target_norm %>%
as.data.frame() %>%
rownames_to_column("Gene") %>%
separate(col = "Gene", sep = ":", into = c("ENS", "gene_symbol")) %>%
pivot_longer(cols = -c("ENS", "gene_symbol"), names_to = "ID", values_to = "gene_expression")
foxr2_pos_samples_target <- target_counts_tidy %>%
filter(gene_symbol == "FOXR2") %>%
filter(gene_expression > threshold) %>%
.$ID
target_meta_foxr2 <- target_counts_tidy %>%
mutate(FOXR2_positive = case_when(ID %in% foxr2_pos_samples_target ~ "Y",
T ~ "N")) %>%
select(ID, FOXR2_positive) %>% distinct5 FOXR2 coexpression with MGE TFs in EC-NB
5.1 Gartlgruber et al. 2021
Stage 4, high-risk tumors only. (Samples with NA in either Risk or Stage metadata columns are removed.)
tum_interest <- ecnb_counts_tidy %>%
filter(Risk == "HR") %>%
filter(Stage == "4") %>%
filter(gene_symbol %in% c("FOXR2", "LHX6", "DLX5", "DLX6"))
# DLX5
foxr2_dlx5 <- tum_interest %>%
filter(gene_symbol %in% c("FOXR2", "DLX5")) %>%
pivot_wider(names_from = "gene_symbol",
values_from = "gene_expression",
id_cols = c("sample", "FOXR2_positive"))
(cor_foxr2_dlx5 <- cor(foxr2_dlx5$FOXR2, foxr2_dlx5$DLX5, method = "pearson"))## [1] 0.1477744(sig_dlx5 <- wilcox.test(foxr2_dlx5 %>% filter(FOXR2_positive == "FOXR2+") %>% .$DLX5,
foxr2_dlx5 %>% filter(FOXR2_positive == "FOXR2-") %>% .$DLX5))##
## Wilcoxon rank sum test with continuity correction
##
## data: foxr2_dlx5 %>% filter(FOXR2_positive == "FOXR2+") %>% .$DLX5 and foxr2_dlx5 %>% filter(FOXR2_positive == "FOXR2-") %>% .$DLX5
## W = 3549, p-value = 0.8924
## alternative hypothesis: true location shift is not equal to 0dlx5_scatter <- foxr2_dlx5 %>%
ggplot(aes(x = FOXR2, y = DLX5, alpha = 0.2)) +
geom_point(alpha = 0.5, size = 3) +
ggtitle("DLX5 - EC-NB stage 4") +
annotate("text", label = glue("r == {round(cor_foxr2_dlx5,4)}"),
x = 470, y = 4000, parse = T)
dlx5_vln <- foxr2_dlx5 %>%
ggplot(aes(x = FOXR2_positive, y = DLX5, fill = FOXR2_positive)) +
geom_violin(scale = "width") +
ggtitle("DLX5 - EC-NB stage 4") +
no_legend() + scale_y_log10() +
stat_summary(fun.y=median, geom="crossbar", size=1,
color="black", aes(width = 0.3)) +
annotate("text", label = glue("p == {round(sig_dlx5$p.value,4)}"),
x = 2, y = 4000, parse = T)
# DLX6
foxr2_dlx6 <- tum_interest %>%
filter(gene_symbol %in% c("FOXR2", "DLX6")) %>%
pivot_wider(names_from = "gene_symbol",
values_from = "gene_expression",
id_cols = c("sample", "FOXR2_positive"))
(cor_foxr2_dlx6 <- cor(foxr2_dlx6$FOXR2, foxr2_dlx6$DLX6, method = "pearson"))## [1] 0.2062904(sig_dlx6 <- wilcox.test(foxr2_dlx6 %>% filter(FOXR2_positive == "FOXR2+") %>% .$DLX6,
foxr2_dlx6 %>% filter(FOXR2_positive == "FOXR2-") %>% .$DLX6))##
## Wilcoxon rank sum test with continuity correction
##
## data: foxr2_dlx6 %>% filter(FOXR2_positive == "FOXR2+") %>% .$DLX6 and foxr2_dlx6 %>% filter(FOXR2_positive == "FOXR2-") %>% .$DLX6
## W = 3461.5, p-value = 0.6946
## alternative hypothesis: true location shift is not equal to 0dlx6_scatter <- foxr2_dlx6 %>%
ggplot(aes(x = FOXR2, y = DLX6, alpha = 0.2)) +
geom_point(alpha = 0.5, size = 3) + ggtitle("DLX6 - EC-NB stage 4") +
annotate("text", label = glue("r == {round(cor_foxr2_dlx6,4)}"),
x = 470, y = 3000, parse = T)
dlx6_vln <- foxr2_dlx6 %>%
ggplot(aes(x = FOXR2_positive, y = DLX6, fill = FOXR2_positive)) +
geom_violin(scale = "width") +
ggtitle("DLX6 - EC-NB stage 4") +
no_legend() + scale_y_log10() +
stat_summary(fun.y=median, geom="crossbar", size=1,
color="black", aes(width = 0.3)) +
annotate("text", label = glue("p == {round(sig_dlx6$p.value,4)}"),
x = 2, y = 4000, parse = T)
# LHX6
foxr2_lhx6 <- tum_interest %>%
filter(gene_symbol %in% c("FOXR2", "LHX6")) %>%
pivot_wider(names_from = "gene_symbol",
values_from = "gene_expression",
id_cols = c("sample", "FOXR2_positive"))
(cor_foxr2_lhx6 <- cor(foxr2_lhx6$FOXR2, foxr2_lhx6$LHX6, method = "pearson"))## [1] -0.04012718(sig_lhx6 <- wilcox.test(foxr2_lhx6 %>% filter(FOXR2_positive == "FOXR2+") %>% .$LHX6,
foxr2_lhx6 %>% filter(FOXR2_positive == "FOXR2-") %>% .$LHX6))##
## Wilcoxon rank sum test with continuity correction
##
## data: foxr2_lhx6 %>% filter(FOXR2_positive == "FOXR2+") %>% .$LHX6 and foxr2_lhx6 %>% filter(FOXR2_positive == "FOXR2-") %>% .$LHX6
## W = 3546, p-value = 0.8854
## alternative hypothesis: true location shift is not equal to 0lhx6_scatter <- foxr2_lhx6 %>%
ggplot(aes(x = FOXR2, y = LHX6, alpha = 0.2)) +
geom_point(alpha = 0.5, size = 3) +
ggtitle("LHX6 - EC-NB stage 4") +
annotate("text", label = glue("r == {round(cor_foxr2_lhx6,4)}"),
x = 470, y = 600, parse = T)
lhx6_vln <- foxr2_lhx6 %>%
ggplot(aes(x = FOXR2_positive, y = LHX6, fill = FOXR2_positive)) +
geom_violin(scale = "width") +
ggtitle("LHX6 - EC-NB stage 4") +
no_legend() +
stat_summary(fun.y=median, geom="crossbar", size=1,
color="black", aes(width = 0.3)) +
annotate("text", label = glue("p == {round(sig_lhx6$p.value,4)}"),
x = 2, y = 600, parse = T)
cowplot::plot_grid(dlx5_scatter, dlx5_vln,
dlx6_scatter, dlx6_vln,
lhx6_scatter, lhx6_vln,
nrow = 1, align = "hv")
5.2 TARGET cohort
FOXR2 + threshold: DESEq normalized value "2".
tum_interest <- target_counts_tidy %>%
filter(gene_symbol %in% c("FOXR2", "LHX6", "DLX5", "DLX6")) %>%
left_join(., target_meta_foxr2, by = "ID") %>%
dplyr::rename("sample" = "ID") %>%
mutate(FOXR2_positive = case_when(FOXR2_positive == "Y" ~ "FOXR2+",
FOXR2_positive == "N" ~ "FOXR2-",
TRUE ~ NA))
# DLX5
foxr2_dlx5 <- tum_interest %>%
filter(gene_symbol %in% c("FOXR2", "DLX5")) %>%
pivot_wider(names_from = "gene_symbol",
values_from = "gene_expression",
id_cols = c("sample", "FOXR2_positive"))
(cor_foxr2_dlx5 <- cor(foxr2_dlx5$FOXR2, foxr2_dlx5$DLX5, method = "pearson"))## [1] 0.07350015(sig_dlx5 <- wilcox.test(foxr2_dlx5 %>% filter(FOXR2_positive == "FOXR2+") %>% .$DLX5,
foxr2_dlx5 %>% filter(FOXR2_positive == "FOXR2-") %>% .$DLX5))##
## Wilcoxon rank sum test with continuity correction
##
## data: foxr2_dlx5 %>% filter(FOXR2_positive == "FOXR2+") %>% .$DLX5 and foxr2_dlx5 %>% filter(FOXR2_positive == "FOXR2-") %>% .$DLX5
## W = 1816, p-value = 0.2406
## alternative hypothesis: true location shift is not equal to 0dlx5_scatter <- foxr2_dlx5 %>%
ggplot(aes(x = FOXR2, y = DLX5, alpha = 0.2)) +
geom_point(alpha = 0.5, size = 3) +
ggtitle("DLX5 - EC-NB stage 4") +
annotate("text", label = glue("r == {round(cor_foxr2_dlx5,4)}"),
x = 900, y = 5500, parse = T)
dlx5_vln <- foxr2_dlx5 %>%
ggplot(aes(x = FOXR2_positive, y = DLX5, fill = FOXR2_positive)) +
geom_violin(scale = "width") +
ggtitle("DLX5 - EC-NB stage 4") +
no_legend() + scale_y_log10() +
stat_summary(fun.y=median, geom="crossbar", size=1,
color="black", aes(width = 0.3)) +
annotate("text", label = glue("p == {round(sig_dlx5$p.value,4)}"),
x = 2, y = 5500, parse = T)
# DLX6
foxr2_dlx6 <- tum_interest %>%
filter(gene_symbol %in% c("FOXR2", "DLX6")) %>%
pivot_wider(names_from = "gene_symbol",
values_from = "gene_expression",
id_cols = c("sample", "FOXR2_positive"))
(cor_foxr2_dlx6 <- cor(foxr2_dlx6$FOXR2, foxr2_dlx6$DLX6, method = "pearson"))## [1] 0.1009971(sig_dlx6 <- wilcox.test(foxr2_dlx6 %>% filter(FOXR2_positive == "FOXR2+") %>% .$DLX6,
foxr2_dlx6 %>% filter(FOXR2_positive == "FOXR2-") %>% .$DLX6))##
## Wilcoxon rank sum test with continuity correction
##
## data: foxr2_dlx6 %>% filter(FOXR2_positive == "FOXR2+") %>% .$DLX6 and foxr2_dlx6 %>% filter(FOXR2_positive == "FOXR2-") %>% .$DLX6
## W = 1823.5, p-value = 0.2248
## alternative hypothesis: true location shift is not equal to 0dlx6_scatter <- foxr2_dlx6 %>%
ggplot(aes(x = FOXR2, y = DLX6, alpha = 0.2)) +
geom_point(alpha = 0.5, size = 3) + ggtitle("DLX6 - EC-NB stage 4") +
annotate("text", label = glue("r == {round(cor_foxr2_dlx6,4)}"),
x = 900, y = 3500, parse = T)
dlx6_vln <- foxr2_dlx6 %>%
ggplot(aes(x = FOXR2_positive, y = DLX6, fill = FOXR2_positive)) +
geom_violin(scale = "width") +
ggtitle("DLX6 - EC-NB stage 4") +
no_legend() + scale_y_log10() +
stat_summary(fun.y=median, geom="crossbar", size=1,
color="black", aes(width = 0.3)) +
annotate("text", label = glue("p == {round(sig_dlx6$p.value,4)}"),
x = 2, y = 3500, parse = T)
# LHX6
foxr2_lhx6 <- tum_interest %>%
filter(gene_symbol %in% c("FOXR2", "LHX6")) %>%
pivot_wider(names_from = "gene_symbol",
values_from = "gene_expression",
id_cols = c("sample", "FOXR2_positive"))
(cor_foxr2_lhx6 <- cor(foxr2_lhx6$FOXR2, foxr2_lhx6$LHX6, method = "pearson"))## [1] -0.1011051(sig_lhx6 <- wilcox.test(foxr2_lhx6 %>% filter(FOXR2_positive == "FOXR2+") %>% .$LHX6,
foxr2_lhx6 %>% filter(FOXR2_positive == "FOXR2-") %>% .$LHX6))##
## Wilcoxon rank sum test with continuity correction
##
## data: foxr2_lhx6 %>% filter(FOXR2_positive == "FOXR2+") %>% .$LHX6 and foxr2_lhx6 %>% filter(FOXR2_positive == "FOXR2-") %>% .$LHX6
## W = 1493, p-value = 0.5729
## alternative hypothesis: true location shift is not equal to 0lhx6_scatter <- foxr2_lhx6 %>%
ggplot(aes(x = FOXR2, y = LHX6, alpha = 0.2)) +
geom_point(alpha = 0.5, size = 3) +
ggtitle("LHX6 - EC-NB stage 4") +
annotate("text", label = glue("r == {round(cor_foxr2_lhx6,4)}"),
x = 900, y = 900, parse = T)
lhx6_vln <- foxr2_lhx6 %>%
ggplot(aes(x = FOXR2_positive, y = LHX6, fill = FOXR2_positive)) +
geom_violin(scale = "width") +
ggtitle("LHX6 - EC-NB stage 4") +
no_legend() +
stat_summary(fun.y=median, geom="crossbar", size=1,
color="black", aes(width = 0.3)) +
annotate("text", label = glue("p == {round(sig_lhx6$p.value,4)}"),
x = 2, y = 900, parse = T)
cowplot::plot_grid(dlx5_scatter, dlx5_vln,
dlx6_scatter, dlx6_vln,
lhx6_scatter, lhx6_vln,
nrow = 1, align = "hv")
6 Reproducibility
This document was last rendered on:
## 2024-11-05 10:34:07The 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:
## ─ Session info ───────────────────────────────────────────────────────────────
## 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)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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## P ggfun 0.1.4 2024-01-19 [?] CRAN (R 4.1.2)
## P ggplot2 * 3.4.2 2023-04-03 [?] CRAN (R 4.1.2)
## P ggplotify 0.1.2 2023-08-09 [?] CRAN (R 4.1.2)
## P ggraph 2.2.1 2024-03-07 [?] 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 ggtree 3.13.0 2024-05-19 [?] Github (YuLab-SMU/ggtree@05ef652)
## P git2r 0.29.0 2021-11-22 [?] CRAN (R 4.1.2)
## P GlobalOptions 0.1.2 2020-06-10 [?] CRAN (R 4.1.2)
## P globals 0.14.0 2020-11-22 [?] CRAN (R 4.1.2)
## P glue * 1.6.2 2022-02-24 [?] CRAN (R 4.1.2)
## P GO.db * 3.14.0 2024-09-20 [?] Bioconductor
## P goftest 1.2-3 2021-10-07 [?] CRAN (R 4.1.2)
## P GOSemSim 2.20.0 2021-10-26 [?] Bioconductor
## P graph 1.72.0 2021-10-26 [?] Bioconductor
## P graphlayouts 1.1.1 2024-03-09 [?] CRAN (R 4.1.2)
## P gridExtra 2.3 2017-09-09 [?] CRAN (R 4.1.2)
## P gridGraphics 0.5-1 2020-12-13 [?] CRAN (R 4.1.2)
## P GSEABase 1.56.0 2021-10-26 [?] Bioconductor
## P GSVA * 1.42.0 2021-10-26 [?] Bioconductor
## P gtable 0.3.0 2019-03-25 [?] CRAN (R 4.1.2)
## P HDF5Array 1.22.1 2021-11-14 [?] Bioconductor
## P here * 1.0.1 2020-12-13 [?] CRAN (R 4.1.2)
## 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)
## P htmltools 0.5.2 2021-08-25 [?] CRAN (R 4.1.2)
## P htmlwidgets 1.5.4 2021-09-08 [?] CRAN (R 4.1.2)
## P httpuv 1.6.5 2022-01-05 [?] CRAN (R 4.1.2)
## P httr 1.4.2 2020-07-20 [?] CRAN (R 4.1.2)
## P ica 1.0-2 2018-05-24 [?] CRAN (R 4.1.2)
## P igraph 2.0.3 2024-03-13 [?] CRAN (R 4.1.2)
## P IRanges * 2.28.0 2021-10-26 [?] Bioconductor
## P irlba 2.3.5 2021-12-06 [?] CRAN (R 4.1.2)
## P iterators 1.0.13 2020-10-15 [?] CRAN (R 4.1.2)
## P jquerylib 0.1.4 2021-04-26 [?] CRAN (R 4.1.2)
## P jsonlite 1.8.8 2023-12-04 [?] CRAN (R 4.1.2)
## P KEGGREST 1.34.0 2021-10-26 [?] Bioconductor
## P KernSmooth 2.23-20 2021-05-03 [?] CRAN (R 4.1.2)
## P knitr 1.37 2021-12-16 [?] CRAN (R 4.1.2)
## P labeling 0.4.2 2020-10-20 [?] CRAN (R 4.1.2)
## P later 1.3.0 2021-08-18 [?] CRAN (R 4.1.2)
## P lattice 0.20-45 2021-09-22 [?] CRAN (R 4.1.2)
## P lazyeval 0.2.2 2019-03-15 [?] CRAN (R 4.1.2)
## P leiden 0.3.9 2021-07-27 [?] CRAN (R 4.1.2)
## P lifecycle 1.0.3 2022-10-07 [?] CRAN (R 4.1.2)
## P listenv 0.8.0 2019-12-05 [?] CRAN (R 4.1.2)
## P lmtest 0.9-39 2021-11-07 [?] CRAN (R 4.1.2)
## P magrittr * 2.0.3 2022-03-30 [?] CRAN (R 4.1.2)
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## P MatrixGenerics 1.6.0 2021-10-26 [?] Bioconductor
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## P png 0.1-7 2013-12-03 [?] CRAN (R 4.1.2)
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## P prettyunits 1.1.1 2020-01-24 [?] 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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## P Rhdf5lib 1.16.0 2021-10-26 [?] Bioconductor
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## P rlang 1.1.3 2024-01-10 [?] CRAN (R 4.1.2)
## P rmarkdown 2.11 2021-09-14 [?] CRAN (R 4.1.2)
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## P rpart 4.1-15 2019-04-12 [?] CRAN (R 4.1.2)
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## P RSQLite 2.2.9 2021-12-06 [?] CRAN (R 4.1.2)
## P rsvd 1.0.5 2021-04-16 [?] RSPM (R 4.1.2)
## P Rtsne 0.15 2018-11-10 [?] CRAN (R 4.1.2)
## P S4Vectors * 0.32.4 2022-03-24 [?] Bioconductor
## P sass 0.4.0 2021-05-12 [?] CRAN (R 4.1.2)
## P ScaledMatrix 1.2.0 2021-10-26 [?] Bioconductor
## P scales 1.2.1 2022-08-20 [?] CRAN (R 4.1.2)
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## P scatterpie 0.2.2 2024-04-03 [?] CRAN (R 4.1.2)
## P sctransform 0.3.3 2022-01-13 [?] CRAN (R 4.1.2)
## P sessioninfo 1.2.2 2021-12-06 [?] CRAN (R 4.1.2)
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## P SeuratObject * 4.0.4 2021-11-23 [?] CRAN (R 4.1.2)
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## P shape 1.4.6 2021-05-19 [?] CRAN (R 4.1.2)
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## P SingleCellExperiment 1.16.0 2021-10-26 [?] Bioconductor
## P sparseMatrixStats 1.6.0 2021-10-26 [?] Bioconductor
## P spatstat 1.64-1 2020-05-12 [?] CRAN (R 4.1.2)
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## P SummarizedExperiment 1.24.0 2021-10-26 [?] Bioconductor
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## P tidygraph 1.3.1 2024-01-30 [?] CRAN (R 4.1.2)
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## P tidyselect 1.2.0 2022-10-10 [?] CRAN (R 4.1.2)
## P tidytree 0.4.6 2023-12-12 [?] CRAN (R 4.1.2)
## P treeio 1.29.0 2024-09-20 [?] Github (GuangchuangYu/treeio@9504617)
## P tweenr 1.0.2 2021-03-23 [?] CRAN (R 4.1.2)
## P tzdb 0.3.0 2022-03-28 [?] CRAN (R 4.1.2)
## P urlchecker 1.0.1 2021-11-30 [?] CRAN (R 4.1.2)
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## P viridisLite * 0.3.0 2018-02-01 [?] CRAN (R 4.1.2)
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## P zlibbioc 1.40.0 2021-10-26 [?] Bioconductor
## P zoo 1.8-9 2021-03-09 [?] 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.