Acute APAP mouse model
Last updated: 2021-02-28
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Knit directory: liver-disease-atlas/
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Introduction
Here we analysis a mouse model of APAP induced acute liver damage. The transcriptomic profiles were measured at 10 different time points ranging from 1 hour to 16 days.
Libraries and sources
These libraries and sources are used for this analysis.
library(mouse4302.db)
library(tidyverse)
library(tidylog)
library(here)
library(oligo)
library(annotate)
library(limma)
library(biobroom)
library(progeny)
library(dorothea)
library(janitor)
library(msigdf) # remotes::install_github("ToledoEM/msigdf@v7.1")
library(AachenColorPalette)
library(cowplot)
library(lemon)
library(patchwork)
options("tidylog.display" = list(print))
source(here("code/utils-microarray.R"))
source(here("code/utils-utils.R"))
source(here("code/utils-plots.R"))Definition of global variables that are used throughout this analysis.
# i/o
data_path <- "data/mouse-acute-apap"
output_path <- "output/mouse-acute-apap"
# graphical parameters
# fontsize
fz <- 9Data processing
Load .CEL files and quality control
The array quality is controlled based on the relative log expression values (RLE) and the normalized unscaled standard errors (NUSE).
# load cel files and check quality
platforms <- readRDS(here("data/annotation/platforms.rds"))
raw_eset <- list.celfiles(here(data_path), listGzipped = T, full.names = T) %>%
read.celfiles() %>%
ma_qc() # Discarding in total 1 arrays: 6h_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/12h_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/12h_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/12h_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/12h_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/12h_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/16d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/16d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/16d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/16d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/16d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/1d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/1d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/1d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/1d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/1d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/1h_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/1h_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/1h_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/1h_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/1h_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/2d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/2d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/2d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/2d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/2d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/4d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/4d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/4d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/4d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/4d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/6d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/6d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/6d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/6d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/6d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/6h_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/6h_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/6h_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/6h_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/6h_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/8d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/8d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/8d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/8d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/8d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/control_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/control_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/control_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/control_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-apap/control_m5_(Mouse430_2).CELNormalization and probe annotation
Probe intensities are normalized with the rma() function. Probes are annotated with MGI symbols.
eset <- rma(raw_eset)
#> Background correcting
#> Normalizing
#> Calculating Expression
# annotate microarray probes with mgi symbols
expr <- ma_annotate(eset, platforms)
colnames(expr) <- str_c("sample",
str_remove(colnames(expr), "_\\(Mouse430_2\\).CEL"),
sep = "_"
)
# save normalized expression
saveRDS(expr, here(output_path, "normalized_expression.rds"))Build meta data
Meta information are parsed from the sample names.
# build meta data
meta <- colnames(expr) %>%
enframe(name = NULL, value = "sample") %>%
separate(sample, into = c("tmp", "key", "rep"), remove = F) %>%
dplyr::select(-tmp) %>%
mutate(
rep = parse_number(rep),
time = parse_number(key)
) %>%
mutate(time = case_when(
str_detect(key, "d") ~ time * 24,
str_detect(key, "h") ~ time,
str_detect(key, "control") ~ 0
)) %>%
mutate(time = ordered(time)) %>%
mutate(group = case_when(
str_detect(key, "d") ~ str_c("d", parse_number(key)),
str_detect(key, "h") ~ str_c("h", parse_number(key)),
str_detect(key, "control") ~ key
)) %>%
mutate(group = factor(group, levels = c(
"control", "h1", "h6", "h12", "d1", "d2",
"d4", "d6", "d8", "d16"
)))
#> mutate: converted 'rep' from character to double (0 new NA)
#> new variable 'time' (double) with 8 unique values and 10% NA
#> mutate: changed 35 values (71%) of 'time' (5 fewer NA)
#> mutate: converted 'time' from double to ordered factor (0 new NA)
#> mutate: new variable 'group' (character) with 10 unique values and 0% NA
#> mutate: converted 'group' from character to factor (0 new NA)
# save meta data
saveRDS(meta, here(output_path, "meta_data.rds"))Exploratory analysis
PCA of normalized data
PCA plot of normalized expression data contextualized based on the time point. Only the top 1000 most variable genes are used as features.
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(output_path, "meta_data.rds"))
pca_result <- do_pca(expr, meta, top_n_var_genes = 1000)
#> left_join: added 4 columns (key, rep, time, group)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 49
#> > ====
#> > rows total 49
saveRDS(pca_result, here(output_path, "pca_result.rds"))
plot_pca(pca_result, feature = "time") +
my_theme()
Differential gene expression analysis
Running limma
Differential gene expression analysis via limma with the aim to identify the effect of APAP intoxication for the different time points.
# load expression and meta data
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(output_path, "meta_data.rds"))
stopifnot(colnames(expr) == meta$sample)
# build design matrix
design <- model.matrix(~ 0 + group, data = meta)
rownames(design) <- meta$sample
colnames(design) <- levels(meta$group)
# define contrasts
contrasts <- makeContrasts(
# effect of apap treatment
apap_1h_vs_0h = h1 - control,
apap_6h_vs_0h = h6 - control,
apap_12h_vs_0h = h12 - control,
apap_24h_vs_0h = d1 - control,
apap_48h_vs_0h = d2 - control,
apap_96h_vs_0h = d4 - control,
apap_144h_vs_0h = d6 - control,
apap_192h_vs_0h = d8 - control,
apap_384h_vs_0h = d16 - control,
# consecutive time point comparison
consec_1h_vs_0h = h1 - control,
consec_6h_vs_1h = h6 - h1,
consec_12h_vs_6h = h12 - h6,
consec_24h_vs_12h = d1 - h12,
consec_48h_vs_24h = d2 - d1,
consec_96h_vs_48h = d4 - d2,
consec_144h_vs_96h = d6 - d4,
consec_192h_vs_144h = d8 - d6,
consec_384h_vs_192h = d16 - d8,
levels = design
)
limma_result <- run_limma(expr, design, contrasts) %>%
assign_deg()
#> select: renamed 3 variables (contrast, logFC, pval) and dropped one variable
#> group_by: one grouping variable (contrast)
#> mutate (grouped): new variable 'fdr' (double) with 133,908 unique values and 0% NA
#> ungroup: no grouping variables
#> mutate: new variable 'regulation' (character) with 3 unique values and 0% NA
#> mutate: converted 'regulation' from character to factor (0 new NA)
deg_df <- limma_result %>%
mutate(contrast = factor(contrast, levels = c(
"apap_1h_vs_0h", "apap_6h_vs_0h", "apap_12h_vs_0h", "apap_24h_vs_0h",
"apap_48h_vs_0h", "apap_96h_vs_0h", "apap_144h_vs_0h", "apap_192h_vs_0h",
"apap_384h_vs_0h",
"consec_1h_vs_0h", "consec_6h_vs_1h", "consec_12h_vs_6h",
"consec_24h_vs_12h", "consec_48h_vs_24h", "consec_96h_vs_48h",
"consec_144h_vs_96h", "consec_192h_vs_144h", "consec_384h_vs_192h"
))) %>%
mutate(contrast_reference = case_when(
str_detect(contrast, "apap") ~ "apap",
str_detect(contrast, "consec") ~ "consec"
))
#> mutate: changed 0 values (0%) of 'contrast' (0 new NA)
#> mutate: new variable 'contrast_reference' (character) with 2 unique values and 0% NA
saveRDS(deg_df, here(output_path, "limma_result.rds"))Volcano plots
Volcano plots visualizing the effect of APAP on gene expression.
df <- readRDS(here(output_path, "limma_result.rds"))
df %>%
filter(contrast_reference == "apap") %>%
plot_volcano() +
my_theme(grid = "y", fsize = fz)
#> filter: removed 184,581 rows (50%), 184,581 rows remaining
#> rename: renamed one variable (p)
Time series clustering
Gene expression trajectories are clustered using the STEM software. The cluster algorithm is described here.
Prepare input
# prepare input for stem analysis
df = readRDS(here(output_path, "limma_result.rds"))
stem_inputs = df %>%
mutate(class = str_c("Hour ", parse_number(as.character(contrast)))) %>%
mutate(class = factor(class, levels = unique(.$class))) %>%
select(gene, class, logFC, contrast_reference)
#> mutate: new variable 'class' (character) with 9 unique values and 0% NA
#> mutate: converted 'class' from character to factor (0 new NA)
#> select: dropped 5 variables (contrast, statistic, pval, fdr, regulation)
stem_inputs %>%
filter(contrast_reference == "apap") %>%
select(-contrast_reference) %>%
pivot_wider(names_from = class, values_from = logFC) %>%
write_delim(here(output_path, "stem/input/apap.txt"), delim = "\t")
#> filter: removed 184,581 rows (50%), 184,581 rows remaining
#> select: dropped one variable (contrast_reference)
#> pivot_wider: reorganized (class, logFC) into (Hour 1, Hour 6, Hour 12, Hour 24, Hour 48, …) [was 184581x3, now 20509x10]Run STEM
STEM is implemented in Java. The .jar file is called from R. Only significant time series clusters are visualized.
# execute stem
stem_res <- run_stem(file.path(output_path, "stem"), clear_output = T)
#> distinct: no rows removed
#> mutate: new variable 'gene' (character) with 20,509 unique values and 0% NA
#> distinct: no rows removed
#> mutate: new variable 'key' (character) with one unique value and 0% NA
#> select: dropped one variable (spot)
#> gather: reorganized (x0, hour_1, hour_6, hour_12, hour_24, …) into (time, value) [was 2528x13, now 25280x5]
#> mutate: converted 'time' from character to double (0 new NA)
#> mutate: new variable 'key' (character) with one unique value and 0% NA
#> select: renamed 4 variables (profile, y_coords, size, p) and dropped 2 variables
#> inner_join: added 3 columns (y_coords, size, p)
#> > rows only in x ( 0)
#> > rows only in y ( 0)
#> > matched rows 25,280
#> > ========
#> > rows total 25,280
#> inner_join: added one column (symbol)
#> > rows only in x ( 0)
#> > rows only in y (17,981)
#> > matched rows 25,280
#> > ========
#> > rows total 25,280
#> transmute: dropped one variable (symbol)
#> changed 25,020 values (99%) of 'gene' (0 new NA)
saveRDS(stem_res, here(output_path, "stem_result.rds"))
stem_res %>%
filter(p <= 0.05) %>%
filter(key == "apap") %>%
distinct() %>%
plot_stem_profiles(model_profile = F, ncol = 2) +
labs(x = "Time in Hours", y = "logFC") +
my_theme(grid = "y", fsize = fz)
#> filter: removed 3,340 rows (13%), 21,940 rows remaining
#> filter: no rows removed
#> distinct: no rows removed
#> group_by: 4 grouping variables (key, profile, p, time)
#> ungroup: no grouping variables
Cluster characterization
STEM clusters are characterized by GO terms, PROGENy’s pathways and DoRothEA’s TFs. As statistic over-representation analysis is used.
stem_res = readRDS(here(output_path, "stem_result.rds"))
signatures = stem_res %>%
filter(p <= 0.05) %>%
distinct(profile, gene, p_profile = p)
#> filter: removed 3,340 rows (13%), 21,940 rows remaining
#> distinct: removed 19,746 rows (90%), 2,194 rows remaining
genesets = load_genesets() %>%
filter(confidence %in% c(NA,"A", "B", "C"))
#> filter: removed 2,340,732 rows (80%), 597,560 rows remaining
#> select: renamed one variable (gene) and dropped 2 variables
#> mutate: new variable 'group' (character) with one unique value and 0% NA
#> gather: reorganized (Androgen, EGFR, Estrogen, Hypoxia, JAK-STAT, …) into (geneset, weight) [was 1299x15, now 18186x3]
#> filter: removed 16,785 rows (92%), 1,401 rows remaining
#> select: dropped one variable (weight)
#> mutate: new variable 'group' (character) with one unique value and 0% NA
#> select: renamed 2 variables (geneset, gene) and dropped one variable
#> mutate: new variable 'group' (character) with one unique value and 0% NA
#> filter: removed 396,818 rows (39%), 612,694 rows remaining
ora_res = signatures %>%
nest(sig = c(-profile)) %>%
dplyr::mutate(ora = sig %>% map(run_ora, sets = genesets, min_size = 10,
options = list(alternative = "greater"),
background_n = 20000)) %>%
select(-sig) %>%
unnest(ora)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> mutate: new variable 'contingency_table' (list) with 1,774 unique values and 0% NA
#> mutate: new variable 'stat' (list) with 1,682 unique values and 0% NA
#> group_by: one grouping variable (group)
#> mutate (grouped): new variable 'fdr' (double) with 1,160 unique values and 0% NA
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> mutate: new variable 'contingency_table' (list) with 1,670 unique values and 0% NA
#> mutate: new variable 'stat' (list) with 1,562 unique values and 0% NA
#> group_by: one grouping variable (group)
#> mutate (grouped): new variable 'fdr' (double) with 1,171 unique values and 0% NA
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> mutate: new variable 'contingency_table' (list) with 1,352 unique values and 0% NA
#> mutate: new variable 'stat' (list) with 1,200 unique values and 0% NA
#> group_by: one grouping variable (group)
#> mutate (grouped): new variable 'fdr' (double) with 191 unique values and 0% NA
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> mutate: new variable 'contingency_table' (list) with 1,173 unique values and 0% NA
#> mutate: new variable 'stat' (list) with 929 unique values and 0% NA
#> group_by: one grouping variable (group)
#> mutate (grouped): new variable 'fdr' (double) with 387 unique values and 0% NA
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> mutate: new variable 'contingency_table' (list) with 1,299 unique values and 0% NA
#> mutate: new variable 'stat' (list) with 1,138 unique values and 0% NA
#> group_by: one grouping variable (group)
#> mutate (grouped): new variable 'fdr' (double) with 674 unique values and 0% NA
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> mutate: new variable 'contingency_table' (list) with 1,400 unique values and 0% NA
#> mutate: new variable 'stat' (list) with 1,175 unique values and 0% NA
#> group_by: one grouping variable (group)
#> mutate (grouped): new variable 'fdr' (double) with 543 unique values and 0% NA
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> mutate: new variable 'contingency_table' (list) with 1,261 unique values and 0% NA
#> mutate: new variable 'stat' (list) with 1,034 unique values and 0% NA
#> group_by: one grouping variable (group)
#> mutate (grouped): new variable 'fdr' (double) with 504 unique values and 0% NA
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> select: dropped one variable (sig)
saveRDS(ora_res, here(output_path, "stem_characterization.rds"))Time spend to execute this analysis: 02:50 minutes.
sessionInfo()
#> R version 4.0.2 (2020-06-22)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Mojave 10.14.5
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] parallel stats4 stats graphics grDevices datasets utils
#> [8] methods base
#>
#> other attached packages:
#> [1] pd.mouse430.2_3.12.0 DBI_1.1.0 RSQLite_2.2.1
#> [4] patchwork_1.1.1 lemon_0.4.5 cowplot_1.1.0
#> [7] AachenColorPalette_1.1.2 msigdf_7.1 janitor_2.0.1
#> [10] dorothea_1.0.1 progeny_1.10.0 biobroom_1.20.0
#> [13] broom_0.7.3 limma_3.44.3 annotate_1.66.0
#> [16] XML_3.99-0.5 oligo_1.52.1 Biostrings_2.56.0
#> [19] XVector_0.28.0 oligoClasses_1.50.4 here_1.0.1
#> [22] tidylog_1.0.2 forcats_0.5.0 stringr_1.4.0
#> [25] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
#> [28] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
#> [31] tidyverse_1.3.0 mouse4302.db_3.2.3 org.Mm.eg.db_3.11.4
#> [34] AnnotationDbi_1.50.3 IRanges_2.22.2 S4Vectors_0.26.1
#> [37] Biobase_2.48.0 BiocGenerics_0.34.0 workflowr_1.6.2
#>
#> loaded via a namespace (and not attached):
#> [1] colorspace_2.0-0 ellipsis_0.3.1
#> [3] rprojroot_2.0.2 snakecase_0.11.0
#> [5] GenomicRanges_1.40.0 fs_1.5.0
#> [7] rstudioapi_0.13 farver_2.0.3
#> [9] affyio_1.58.0 ggrepel_0.9.0
#> [11] bit64_4.0.5 fansi_0.4.1
#> [13] lubridate_1.7.9.2 xml2_1.3.2
#> [15] codetools_0.2-16 splines_4.0.2
#> [17] knitr_1.30 jsonlite_1.7.2
#> [19] bcellViper_1.24.0 dbplyr_2.0.0
#> [21] BiocManager_1.30.10 compiler_4.0.2
#> [23] httr_1.4.2 backports_1.2.1
#> [25] assertthat_0.2.1 Matrix_1.2-18
#> [27] cli_2.2.0 later_1.1.0.1
#> [29] htmltools_0.5.0 tools_4.0.2
#> [31] gtable_0.3.0 glue_1.4.2
#> [33] GenomeInfoDbData_1.2.3 affxparser_1.60.0
#> [35] Rcpp_1.0.5 cellranger_1.1.0
#> [37] vctrs_0.3.6 preprocessCore_1.50.0
#> [39] iterators_1.0.13 xfun_0.19
#> [41] rvest_0.3.6 lifecycle_0.2.0
#> [43] renv_0.12.3 zlibbioc_1.34.0
#> [45] scales_1.1.1 clisymbols_1.2.0
#> [47] hms_0.5.3 promises_1.1.1
#> [49] SummarizedExperiment_1.18.2 yaml_2.2.1
#> [51] gridExtra_2.3 memoise_1.1.0
#> [53] stringi_1.5.3 foreach_1.5.1
#> [55] GenomeInfoDb_1.24.2 rlang_0.4.9
#> [57] pkgconfig_2.0.3 bitops_1.0-6
#> [59] matrixStats_0.57.0 evaluate_0.14
#> [61] lattice_0.20-41 labeling_0.4.2
#> [63] bit_4.0.4 tidyselect_1.1.0
#> [65] plyr_1.8.6 magrittr_2.0.1
#> [67] R6_2.5.0 generics_0.1.0
#> [69] DelayedArray_0.14.1 pillar_1.4.7
#> [71] haven_2.3.1 whisker_0.4
#> [73] withr_2.3.0 RCurl_1.98-1.2
#> [75] modelr_0.1.8 crayon_1.3.4
#> [77] rmarkdown_2.6 grid_4.0.2
#> [79] readxl_1.3.1 blob_1.2.1
#> [81] git2r_0.27.1 reprex_0.3.0
#> [83] digest_0.6.27 xtable_1.8-4
#> [85] ff_4.0.4 httpuv_1.5.4
#> [87] munsell_0.5.0 viridisLite_0.3.0