Last updated: 2022-11-05
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Knit directory: SRB_2022/1_analysis/
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# working with data
library(dplyr)
library(magrittr)
library(readr)
library(tibble)
library(reshape2)
library(tidyverse)
# Visualisation:
library(kableExtra)
library(ggplot2)
library(grid)
library(pander)
library(cowplot)
library(pheatmap)
# Custom ggplot
library(ggplotify)
library(ggpubr)
library(ggrepel)
library(viridis)
# Bioconductor packages:
library(edgeR)
library(limma)
library(Glimma)
theme_set(theme_minimal())
pub <- readRDS(here::here("0_data/RDS_objects/pub.rds"))
DGElist object containing the raw feature count, sample metadata, and gene metadata, created in the Set Up stage.
# load DGElist previously created in the set up
dge <- readRDS(here::here("0_data/RDS_objects/dge.rds"))
The varying methods used to identify differential expression all rely on similar initial parameters. These include:
The experimental design can be parameterised in a one-way layout where one coefficient is assigned to each group. The design matrix formulated below contains the predictors of each sample
# null design with unit vector for generation of voomWithQualityWeights downstream
null_design <- matrix(1, ncol = 1, nrow = ncol(dge))
# setup full design matrix with sample_group
full_design <- model.matrix(~ 0 + group,
data = dge$samples)
# remove "sample_group" from each column names
colnames(full_design) <- gsub(
"group",
"",
colnames(full_design))
The contrast matrix is required to provide a coefficient to each comparison and later used to test for significant differential expression with each comparison group
contrast <- limma::makeContrasts(
INTvsCONT = INT - CONT,
levels = full_design)
colnames(contrast) <- c("INT vs CONT")
Voom is used to estimate the mean-variance relationship of the data, which is then used to calculate and assign a precision weight for each of the observation (gene). This observational level weights are then used in a linear modelling process to adjust for heteroscedasticity. Log count (logCPM) data typically show a decreasing mean-variance trend with increasing count size (expression).
However, for some dataset with potential sample outliers,
voomWithQualityWeights
can be used to calculate
sample-specific quality weights. The application of observational and
sample-specific weights can objectively and systematically correct for
outliers and better than manually removing samples in cases where there
are no clear-cut reasons for replicate variations
# voom tranformation without sample weights
voom <- limma::voom(counts = dge, design = full_design, plot = TRUE,)
Voom transformation with observational weights
# voom transformation with sample weights using full_design matrix for group-specific weights
voom1 <- limma::voomWithQualityWeights(counts = dge, design = full_design, plot = TRUE)
Voom transformation with observational and group-specific weights
# voom transformation with sample weights using null design matrix
voom2 <- limma::voomWithQualityWeights(counts = dge,design = null_design, plot = TRUE)
Voom transformation with observational and sample-specific weights
# specifying FC of interest
options(digits = 6)
fc <- c(1.05, 1.1, 1.5)
lfc <- log(x = fc, 2)
# function for applying linear model, generate decideTest table, and extract topTable
limmaFit_ebayes <- function(x, adjMethod, p.val){
lm <- limma::lmFit(object = x, design = full_design) %>%
contrasts.fit(contrasts = contrast) %>%
limma::eBayes()
lm_dt <- decideTests(object = lm, adjust.method = adjMethod, p.value = p.val)
print(knitr::kable(summary(lm_dt)
, caption = paste0("Number of significant DE genes from '", deparse(substitute(x)), "' with '", adjMethod, "' adjusment method, and at a p-value/adj.p-value of ", p.val)) %>%
kable_styling(bootstrap_options = c("striped", "hover")))
lm_all <- lapply(1:ncol(lm), function(y){
limma::topTable(lm, coef = y, number = Inf, adjust.method = adjMethod) %>%
dplyr::select(c("gene", "gene_name", "gene_biotype", "logFC", "AveExpr", "P.Value", "adj.P.Val", "description", "entrezid"))
})
names(lm_all) <- as.data.frame(contrast) %>% colnames()
return(lm_all)
}
lm_voom1_pval0.01 <- limmaFit_ebayes(x = voom1, adjMethod = "none", p.val = 0.01)
INT vs CONT | |
---|---|
Down | 4583 |
NotSig | 7985 |
Up | 4654 |
lm_voom2_pval0.01 <- limmaFit_ebayes(x = voom2, adjMethod = "none", p.val = 0.01)
INT vs CONT | |
---|---|
Down | 305 |
NotSig | 14943 |
Up | 1974 |
lm_voom2_fdr0.7 <- limmaFit_ebayes(x = voom2, adjMethod = "fdr", p.val = 0.7)
INT vs CONT | |
---|---|
Down | 5931 |
NotSig | 5349 |
Up | 5942 |
limmaFit_treat <- function(x, fc, adjMethod, p.val){
lm_treat <- limma::lmFit(object = x, design = full_design) %>%
contrasts.fit(contrasts = contrast) %>%
limma::treat(fc = fc)
lm_treat_dt <- decideTests(object = lm_treat, adjust.method = adjMethod, p.value = p.val)
print(knitr::kable(summary(lm_treat_dt),
caption = paste0("Number of DE genes significantly above a FC of ", fc, " from '", deparse(substitute(x)), "' with '", adjMethod, "' adjusment method, and at a p-value/adj.p-value of ", p.val)) %>%
kable_styling(bootstrap_options = c("striped", "hover")))
lm_treat_all <- lapply(1:ncol(lm_treat), function(y){
limma::topTreat(lm_treat, coef = y, number = Inf, adjust.method = adjMethod) %>%
dplyr::select(c("gene", "gene_name", "gene_biotype", "logFC", "AveExpr", "P.Value", "adj.P.Val", "description", "entrezid"))
})
names(lm_treat_all) <- as.data.frame(contrast) %>% colnames()
return(lm_treat_all)
}
assign(paste0("lmTreat_fc", fc[1], "_voom2_fdr0.05"),
limmaFit_treat(x = voom2, fc = fc[1], adjMethod = "fdr", p.val = 0.05))
INT vs CONT | |
---|---|
Down | 95 |
NotSig | 15593 |
Up | 1534 |
assign(paste0("lmTreat_fc", fc[1], "_voom2_pval0.01"),
limmaFit_treat(x = voom2, fc = fc[1], adjMethod = "none", p.val = 0.01))
INT vs CONT | |
---|---|
Down | 260 |
NotSig | 15041 |
Up | 1921 |
assign(paste0("lmTreat_fc", fc[1], "_voom2_pval0.05"),
limmaFit_treat(x = voom2, fc = fc[1], adjMethod = "none", p.val = 0.05))
INT vs CONT | |
---|---|
Down | 1443 |
NotSig | 12700 |
Up | 3079 |
### Old code used to interatively generate lmTreat dataset with different fc cutoff
## with treat
lmTreat <- list()
lmTreat_dt <- list()
lmTreat_all <- list()
lmTreat_sig <- list()
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
lmTreat[[x]] <- limma::lmFit(object = voom2, design = full_design) %>%
limma::contrasts.fit(contrasts = contrast) %>%
limma::treat(lfc = lfc[i])
# decide test, do before taking topTreat, as input need to be MArraryLM list
lmTreat_dt[[x]] <- decideTests(lmTreat[[x]], adjust.methods = "fdr", p.value = 0.05)
# extract a table of genes from a linear model fit, export and used for downstream analysis
lmTreat_all[[x]] <- topTreat(fit = lmTreat[[x]], coef = 1, number = Inf, adjust.method = "fdr") %>%
dplyr::select(c("gene_name", "logFC", "AveExpr", "P.Value", "adj.P.Val", "description", "entrezid"))
# extract a table of significant genes from a linear model fit, export and used for downstream analysis
lmTreat_sig[[x]] <- topTreat(fit = lmTreat[[x]], coef = 1, number = Inf, adjust.method = "fdr", p.value = 0.05) %>%
dplyr::select(c("gene_name", "logFC", "AveExpr", "P.Value", "adj.P.Val", "description", "entrezid"))
}
lmTreat_hist <- list()
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
lmTreat_hist[[x]] <- hist(x = lmTreat[[x]]$p.value, breaks = 100, plot = F)
}
plot(
x = lmTreat_hist[[1]],
main = paste0("P-Values FC = ", fc[[1]]),
xlab = "P-Value",
col = "gray60"
)
invisible(dev.print(svg, here::here(paste0("2_plots/de/pval_", fc[1], ".svg"))))
ma <- list()
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
# add an extra column and determine whether the DE genes are significant
lmTreat_all[[x]] <- lmTreat_all[[x]] %>%
as.data.frame() %>%
dplyr::mutate(Expression = case_when(
adj.P.Val <= 0.05 & logFC >= lfc ~ "Up-regulated",
adj.P.Val <= 0.05 & logFC <= -lfc ~ "Down-regulated",
TRUE ~ "Insignificant"
))
# adding labels to top genes
top <- 3
top_limma <- bind_rows(
lmTreat_all[[x]] %>%
dplyr::filter(Expression == "Up-regulated") %>%
arrange(adj.P.Val, desc(abs(logFC))) %>%
head(top),
lmTreat_all[[x]] %>%
dplyr::filter(Expression == "Down-regulated") %>%
arrange(adj.P.Val, desc(abs(logFC))) %>%
head(top)
)
invisible(top_limma %>% as.data.frame())
ma[[x]] <- lmTreat_all[[x]] %>%
ggplot(aes(x = AveExpr, y = logFC)) +
geom_point(aes(colour = Expression),
### PUBLISH
size = 0.3,
# alpha = 0.7,
show.legend = T
) +
# geom_label_repel(
# data = top_limma,
# mapping = aes(x = AveExpr, logFC, label = gene_name),
#
# ### PUBLISH
# size = 1.7,
# label.padding = 0.15,
# # label.r = 0.15,
# box.padding = 0.15
# # point.padding = 0.2
# ) +
geom_hline(yintercept = c(-fc[i], 0, fc[i]), linetype = c("dashed", "solid", "dashed")) +
### PUBLISH
ylim(-8, 8) +
theme(legend.position = "bottom",
legend.box.margin = margin(-10,0,0,0),
legend.key.size = unit(0, "lines")
)+
xlab(expression("log"[2] * "CPM")) +
ylab(expression("log"[2] * "FC")) +
scale_fill_manual(values = c("dodgerblue3", alpha(colour = "gray80", alpha = 0.9), "firebrick3")) +
scale_color_manual(labels = c(paste0("Down: ", sum(lmTreat_all[[x]]$Expression == "Down-regulated"), " "),
paste0("NS: ", sum(lmTreat_all[[x]]$Expression == "Insignificant"), " "),
paste0("Up: ", sum(lmTreat_all[[x]]$Expression == "Up-regulated"), " ")),
values = c("dodgerblue3", alpha(colour = "gray80", alpha = 0.6), "firebrick3")) +
guides(colour = guide_legend(override.aes = list(size = 1.5))) +
labs(
# title = "MA Plot: LIMMA-VOOM + TREAT",
# subtitle = "Intact vs Control",
colour = "Expression")
# save to directory
ggsave(paste0("ma_", fc[i], ".png"),
plot = ma[[x]],
path = here::here("2_plots/de/"),
### PUBLISH
width = 58,
height = 70,
units = "mm"
)
}
# display
ma[[1]]
vol <- list()
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
# adding labels to top genes
top <- 3
top_limma <- bind_rows(
lmTreat_all[[x]] %>%
dplyr::filter(Expression == "Up-regulated") %>%
arrange(adj.P.Val, desc(abs(logFC))) %>%
head(top),
lmTreat_all[[x]] %>%
dplyr::filter(Expression == "Down-regulated") %>%
arrange(adj.P.Val, desc(abs(logFC))) %>%
head(top)
)
invisible(top_limma %>% as.data.frame())
# generate vol plot with the allDEgene data.frame
vol[[x]] <- lmTreat_all[[x]] %>%
ggplot(aes(
x = logFC,
y = -log(adj.P.Val, 10)
)) +
geom_point(aes(colour = Expression),
### PUBLISH
size = 0.3,
# alpha = 0.8,
show.legend = T
) +
# geom_label_repel(
# data = top_limma,
# mapping = aes(logFC, -log(adj.P.Val, 10), label = gene_name),
#
# ### PUBLISH
# size = 1.7,
# label.padding = 0.15,
# # label.r = 0.15,
# box.padding = 0.15
# # point.padding = 0.2
# ) +
### PUBLISH
xlim(-8.15, 8.15)+
theme(legend.position = "bottom",
legend.box.margin = margin(-10,0,0,0),
legend.key.size = unit(0, "lines")
)+
xlab(expression("log"[2] * "FC")) +
ylab(expression("-log"[10] * "FDR")) +
scale_fill_manual(values = c("dodgerblue3", alpha(colour = "gray80", alpha = 0.9), "firebrick3")) +
scale_color_manual(labels = c(paste0("Down: ", sum(lmTreat_all[[x]]$Expression == "Down-regulated"), " "),
paste0("NS: ", sum(lmTreat_all[[x]]$Expression == "Insignificant"), " "),
paste0("Up: ", sum(lmTreat_all[[x]]$Expression == "Up-regulated"), " ")),
values = c("dodgerblue3", alpha(colour = "gray80", alpha = 0.6), "firebrick3")) +
guides(colour = guide_legend(override.aes = list(size = 1.5))) +
labs(
### PUBLISH
# title = "Volcano Plot: LIMMA-VOOM + TREAT",
# subtitle = "Intact vs Control",
colour = "Expression"
)
# save to directory
ggsave(paste0("vol_", fc[i], ".png"),
plot = vol[[x]],
path = here::here("2_plots/de/"),
### PUBLISH
width = 58,
height = 70,
units = "mm"
)
}
# display
vol[[1]]
# create df with normalised read counts with an additional entrezid column for binding
logCPM <- cpm(dge, prior.count = 3, log = TRUE) %>% subset(select = 1:7)
rownames(logCPM) <- dge$genes$gene_name
# colnames(logCPM) <- c("Control 1", "Control 2", "Control 4", "Intact 1", "Intact 2", "Intact 3", "Intact 4")
# colour palette for heatmap
my_palette <- colorRampPalette(c("dodgerblue3", "white", "firebrick3"))(n = 201)
# df for heatmap annotation of sample group
anno <- as.factor(dge$samples$group) %>% as.data.frame() %>% dplyr::slice(1:7)
colnames(anno) <- "Sample Groups"
anno$`Sample Groups` <- gsub("CONT", "Control", anno$`Sample Groups`)
anno$`Sample Groups` <- gsub("INT", "Intact", anno$`Sample Groups`)
rownames(anno) <- colnames(logCPM)
# setting colour of sample group annotation
anno_colours <- c("#f8766d", "#00bdc4")
names(anno_colours) <- c("Control", "Intact")
logCPM_up=list()
logCPM_down=list()
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
# filtering top upregulated genes then filter the logCPM values of those genes.
upReg <- lmTreat_sig[[x]] %>%
dplyr::filter(logFC > 0) %>%
arrange(sort(adj.P.Val, decreasing = F))
upReg <- upReg[1:20,]
logCPM_up[[x]] <- logCPM[upReg$gene_name,] %>% as.data.frame()
# filtering top upregulated genes then filter the logCPM values of those genes.
downReg <- lmTreat_sig[[x]] %>%
dplyr::filter(logFC < 0) %>%
arrange(sort(adj.P.Val, decreasing = F))
if (nrow(downReg) >= 20) {max <- 20} else {max <- nrow(downReg)}
downReg <- downReg[1:max,]
logCPM_down[[x]] <- logCPM[downReg$gene_name,] %>% as.data.frame()
}
heat_up=list()
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
heat_up[[x]] <-
pheatmap(
mat = logCPM_up[[x]],
### Publish
show_colnames = F,
main = paste0("Top ", nrow(logCPM_up[[x]]), " significant upregulated genes\n"),
legend = F,
annotation_legend = T,
fontsize = 8,
fontsize_col = 9,
fontsize_number = 7,
fontsize_row = 8,
treeheight_row = 25,
treeheight_col = 10,
clustering_distance_rows = "euclidean",
legend_breaks = c(seq(-3, 11, by = .5), 1.3),
legend_labels = c(seq(-3, 11, by = .5), "Z-Score"),
angle_col = 90,
cutree_cols = 2,
cutree_rows = 1,
border_color = NA,
color = viridis_pal(option = "viridis")(300),
scale = "row",
annotation_col = anno,
annotation_colors = list("Sample Groups" = anno_colours),
annotation_names_col = F,
annotation = T,
silent = T,
labels_row = as.expression(lapply(rownames(logCPM_up[[x]]), function(a) bquote(italic(.(a)))))
) %>% as.ggplot()
# save to directory
ggsave(paste0("heat_up_", fc[i], ".svg"),
plot = heat_up[[x]],
path = here::here("2_plots/de/"),
### PUBLISH
width = 112,
height = 110,
units = "mm"
)
}
heat_up[[1]]
heat_down=list()
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
heat_down[[x]] <-
pheatmap(
mat = logCPM_down[[x]],
### Publish
show_colnames = F,
main = paste0("Top ", nrow(logCPM_down[[x]]), " significant downregulated genes\n"),
legend = F,
annotation_legend = T,
fontsize = 8,
fontsize_col = 9,
fontsize_number = 7,
fontsize_row = 8,
treeheight_row = 25,
treeheight_col = 10,
clustering_distance_rows = "euclidean",
legend_breaks = c(seq(-3, 11, by = .5), 1.3),
legend_labels = c(seq(-3, 11, by = .5), "Z-Score"),
angle_col = 90,
cutree_cols = 2,
cutree_rows = 1,
border_color = NA,
color = viridis_pal(option = "viridis")(300),
scale = "row",
annotation_col = anno,
annotation_colors = list("Sample Groups" = anno_colours),
annotation_names_col = F,
annotation = T,
silent = T,
labels_row = as.expression(lapply(rownames(logCPM_down[[x]]), function(a) bquote(italic(.(a)))))
) %>% as.ggplot()
# save to directory
ggsave(paste0("heat_down_", fc[i], ".svg"),
plot = heat_down[[x]],
path = here::here("2_plots/de/"),
### PUBLISH
width = 112,
height = 110,
units = "mm"
)
}
heat_down[[1]]
assign(paste0("lmTreat_fc", fc[2], "_voom2_fdr0.05"),
limmaFit_treat(x = voom2, fc = fc[2], adjMethod = "fdr", p.val = 0.05))
INT vs CONT | |
---|---|
Down | 55 |
NotSig | 15775 |
Up | 1392 |
assign(paste0("lmTreat_fc", fc[2], "_voom2_pval0.01"),
limmaFit_treat(x = voom2, fc = fc[2], adjMethod = "none", p.val = 0.01))
INT vs CONT | |
---|---|
Down | 187 |
NotSig | 15205 |
Up | 1830 |
assign(paste0("lmTreat_fc", fc[2], "_voom2_pval0.05"),
limmaFit_treat(x = voom2, fc = fc[2], adjMethod = "none", p.val = 0.05))
INT vs CONT | |
---|---|
Down | 1230 |
NotSig | 13070 |
Up | 2922 |
plot(x = lmTreat_hist[[2]],
main = paste0("P-Values FC = ", fc[[2]]),
xlab = "P-Value",
col = "gray60")
invisible(dev.print(svg, here::here(paste0("2_plots/de/pval_", fc[2], ".svg"))))
ma[[2]]
vol[[2]]
heat_up[[2]]
heat_down[[2]]
assign(paste0("lmTreat_fc", fc[3], "_voom2_fdr0.05"),
limmaFit_treat(x = voom2, fc = fc[3], adjMethod = "fdr", p.val = 0.05))
INT vs CONT | |
---|---|
Down | 2 |
NotSig | 16834 |
Up | 386 |
assign(paste0("lmTreat_fc", fc[3], "_voom2_pval0.01"),
limmaFit_treat(x = voom2, fc = fc[3], adjMethod = "none", p.val = 0.01))
INT vs CONT | |
---|---|
Down | 14 |
NotSig | 16238 |
Up | 970 |
assign(paste0("lmTreat_fc", fc[3], "_voom2_pval0.05"),
limmaFit_treat(x = voom2, fc = fc[3], adjMethod = "none", p.val = 0.05))
INT vs CONT | |
---|---|
Down | 155 |
NotSig | 15317 |
Up | 1750 |
plot(x = lmTreat_hist[[3]],
main = paste0("P-Values FC = ", fc[[3]]),
xlab = "P-Value",
col = "gray60")
invisible(dev.print(svg, here::here(paste0("2_plots/de/pval_", fc[3], ".svg"))))
ma[[3]]
vol[[3]]
heat_up[[3]]
heat_down[[3]]
# export toptable for Dexter rewrite
## First paper (suitable # of DE genes for INT vs CONT)
writexl::write_xlsx(x = lmTreat_fc1.5_voom2_fdr0.05, here::here("3_output/lmTreat_fc1.5_voom2_all_fdr.xlsx"))
## Second paper (suitable # of DE genes for INT vs SVS_VAS, SVX vs SVX_VAS, and VAS vs SVX_VAS)
writexl::write_xlsx(x = lm_voom2_pval0.01, here::here("3_output/lm_voom2_all.xlsx"))
# export excel spreadsheet
writexl::write_xlsx(x = lmTreat_all, here::here("3_output/lmTreat_all.xlsx"))
writexl::write_xlsx(x = lmTreat_sig, here::here("3_output/lmTreat_sig.xlsx"))
# save RDS object for enrichment analysis
saveRDS(object = fc, file = here::here("0_data/RDS_objects/fc.rds"))
saveRDS(object = lfc, file = here::here("0_data/RDS_objects/lfc.rds"))
saveRDS(object = full_design, file = here::here("0_data/RDS_objects/full_design.rds"))
saveRDS(object = contrast, file = here::here("0_data/RDS_objects/contrast.rds"))
saveRDS(object = lmTreat, file = here::here("0_data/RDS_objects/lmTreat.rds"))
saveRDS(object = lmTreat_all, file = here::here("0_data/RDS_objects/lmTreat_all.rds"))
saveRDS(object = lmTreat_sig, file = here::here("0_data/RDS_objects/lmTreat_sig.rds"))
sessionInfo()
R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8
[3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
[5] LC_TIME=English_Australia.utf8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Glimma_2.6.0 edgeR_3.38.4 limma_3.52.4 viridis_0.6.2
[5] viridisLite_0.4.1 ggrepel_0.9.1 ggpubr_0.4.0 ggplotify_0.1.0
[9] pheatmap_1.0.12 cowplot_1.1.1 pander_0.6.5 kableExtra_1.3.4
[13] forcats_0.5.2 stringr_1.4.1 purrr_0.3.5 tidyr_1.2.1
[17] ggplot2_3.3.6 tidyverse_1.3.2 reshape2_1.4.4 tibble_3.1.8
[21] readr_2.1.3 magrittr_2.0.3 dplyr_1.0.10
loaded via a namespace (and not attached):
[1] readxl_1.4.1 backports_1.4.1
[3] workflowr_1.7.0 systemfonts_1.0.4
[5] plyr_1.8.7 splines_4.2.1
[7] BiocParallel_1.30.3 GenomeInfoDb_1.32.4
[9] digest_0.6.29 yulab.utils_0.0.5
[11] htmltools_0.5.3 fansi_1.0.3
[13] memoise_2.0.1 googlesheets4_1.0.1
[15] tzdb_0.3.0 Biostrings_2.64.1
[17] annotate_1.74.0 modelr_0.1.9
[19] matrixStats_0.62.0 svglite_2.1.0
[21] colorspace_2.0-3 blob_1.2.3
[23] rvest_1.0.3 textshaping_0.3.6
[25] haven_2.5.1 xfun_0.33
[27] crayon_1.5.2 RCurl_1.98-1.9
[29] jsonlite_1.8.2 genefilter_1.78.0
[31] survival_3.3-1 glue_1.6.2
[33] gtable_0.3.1 gargle_1.2.1
[35] zlibbioc_1.42.0 XVector_0.36.0
[37] webshot_0.5.4 DelayedArray_0.22.0
[39] car_3.1-0 BiocGenerics_0.42.0
[41] abind_1.4-5 scales_1.2.1
[43] DBI_1.1.3 rstatix_0.7.0
[45] Rcpp_1.0.9 xtable_1.8-4
[47] gridGraphics_0.5-1 bit_4.0.4
[49] stats4_4.2.1 htmlwidgets_1.5.4
[51] httr_1.4.4 RColorBrewer_1.1-3
[53] ellipsis_0.3.2 farver_2.1.1
[55] pkgconfig_2.0.3 XML_3.99-0.11
[57] sass_0.4.2 dbplyr_2.2.1
[59] here_1.0.1 locfit_1.5-9.6
[61] utf8_1.2.2 labeling_0.4.2
[63] tidyselect_1.2.0 rlang_1.0.6
[65] later_1.3.0 AnnotationDbi_1.58.0
[67] munsell_0.5.0 cellranger_1.1.0
[69] tools_4.2.1 cachem_1.0.6
[71] cli_3.4.1 generics_0.1.3
[73] RSQLite_2.2.18 broom_1.0.1
[75] evaluate_0.17 fastmap_1.1.0
[77] ragg_1.2.3 yaml_2.3.5
[79] knitr_1.40 bit64_4.0.5
[81] fs_1.5.2 KEGGREST_1.36.3
[83] xml2_1.3.3 compiler_4.2.1
[85] rstudioapi_0.14 png_0.1-7
[87] ggsignif_0.6.3 reprex_2.0.2
[89] geneplotter_1.74.0 bslib_0.4.0
[91] stringi_1.7.8 highr_0.9
[93] lattice_0.20-45 Matrix_1.5-1
[95] vctrs_0.4.2 pillar_1.8.1
[97] lifecycle_1.0.3 jquerylib_0.1.4
[99] bitops_1.0-7 httpuv_1.6.6
[101] GenomicRanges_1.48.0 R6_2.5.1
[103] promises_1.2.0.1 gridExtra_2.3
[105] writexl_1.4.0 IRanges_2.30.1
[107] codetools_0.2-18 assertthat_0.2.1
[109] SummarizedExperiment_1.26.1 DESeq2_1.36.0
[111] rprojroot_2.0.3 withr_2.5.0
[113] S4Vectors_0.34.0 GenomeInfoDbData_1.2.8
[115] parallel_4.2.1 hms_1.1.2
[117] rmarkdown_2.17 MatrixGenerics_1.8.1
[119] carData_3.0-5 googledrive_2.0.0
[121] git2r_0.30.1 Biobase_2.56.0
[123] lubridate_1.8.0