Acute BDL mouse model
Christian H. Holland
2020-12-19
Last updated: 2020-12-20
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Knit directory: meta-liver/
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Introduction
Here we analysis a mouse model of BDL (Bile Duct Ligation) induced acute liver damage. The transcriptomic profiles were measured at 4 different time points ranging from 1 day to 21 days. For the time points 1, 3, and 7 days time-matched controls are available.
Libraries and sources
These libraries and sources are used for this analysis.
library(mogene20sttranscriptcluster.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-bdl"
output_path <- "output/mouse-acute-bdl"
# 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: 489-944wt 1d Sham_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-678wt 7d Sham_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-690wt 7d Sham_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-691wt 7d Sham_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-697wt 7d Sham_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-709wt 7d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-713wt 7d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-714wt 7d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-740wt 3d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-741wt 3d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-744wt 7d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-750wt 7d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-776wt 3d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-778wt 3d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-806wt 21d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-853wt 21d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-855wt 21d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-884wt 21d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-886wt 21d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-911wt 3d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-913wt 3d Sham_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-927wt 3d Sham_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-928wt 1d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-931wt 1d Sham_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-939wt 1d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-940wt 1d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-944wt 1d Sham_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-945wt 1d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-956wt 1d BDL_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-957wt 1d Sham_(MoGene-2_0-st).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-bdl/489-960wt 1d Sham_(MoGene-2_0-st).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)
# 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(
"tmp1", "mouse", "time", "treatment",
"tmp"
), remove = F, extra = "merge") %>%
dplyr::select(-starts_with("tmp")) %>%
mutate(
time = ordered(parse_number(time)),
mouse = str_remove(mouse, "wt"),
treatment = factor(str_to_lower(treatment), levels = c("sham", "bdl")),
group = str_c(treatment, str_c(time, "d"), sep = "_")
) %>%
mutate(group = factor(group, levels = c(
"sham_1d", "bdl_1d", "sham_3d",
"bdl_3d", "sham_7d", "bdl_7d",
"bdl_21d"
)))
#> mutate: changed 29 values (100%) of 'mouse' (0 new NA)
#> converted 'time' from character to ordered factor (0 new NA)
#> converted 'treatment' from character to factor (0 new NA)
#> new variable 'group' (character) with 7 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 and treatment. 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 (mouse, time, treatment, group)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 29
#> > ====
#> > rows total 29
saveRDS(pca_result, here(output_path, "pca_result.rds"))
plot_pca(pca_result, feature = "time") +
plot_pca(pca_result, feature = "treatment") &
my_theme()
Differential gene expression analysis
Running limma
Differential gene expression analysis via limma with the aim to identify the effect of BDL 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(
# time matched effect of bdl
bdl_vs_sham_1d = bdl_1d - sham_1d,
bdl_vs_sham_3d = bdl_3d - sham_3d,
bdl_vs_sham_7d = bdl_7d - sham_7d,
bdl_vs_sham_21d = bdl_21d - (sham_1d + sham_3d + sham_7d) / 3,
# pair-wise bdl comparison
bdl_3d_vs_1d = bdl_3d - bdl_1d,
bdl_7d_vs_1d = bdl_7d - bdl_1d,
bdl_21d_vs_1d = bdl_21d - bdl_1d,
bdl_7d_vs_3d = bdl_7d - bdl_3d,
bdl_21d_vs_3d = bdl_21d - bdl_3d,
bdl_21d_vs_7d = bdl_21d - bdl_7d,
# pair-wise sham comparison
sham_3d_vs_1d = sham_3d - sham_1d,
sham_7d_vs_1d = sham_7d - sham_1d,
sham_7d_vs_3d = sham_7d - sham_3d,
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 133,423 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(
"bdl_vs_sham_1d", "bdl_vs_sham_3d", "bdl_vs_sham_7d", "bdl_vs_sham_21d",
"bdl_3d_vs_1d",
"bdl_7d_vs_1d", "bdl_21d_vs_1d", "bdl_7d_vs_3d", "bdl_21d_vs_3d",
"bdl_21d_vs_7d", "sham_3d_vs_1d", "sham_7d_vs_1d", "sham_7d_vs_3d"
))) %>%
mutate(contrast_reference = case_when(
str_detect(contrast, "vs_sham") ~ "bdl",
str_detect(contrast, "bdl_\\d*") ~ "pairwise_bdl",
str_detect(contrast, "sham_\\d*") ~ "pairwise_sham"
))
#> mutate: changed 0 values (0%) of 'contrast' (0 new NA)
#> mutate: new variable 'contrast_reference' (character) with 3 unique values and 0% NA
saveRDS(deg_df, here(output_path, "limma_result.rds"))Volcano plots
Volcano plots visualizing the effect of BDL on gene expression.
df <- readRDS(here(output_path, "limma_result.rds"))
df %>%
filter(contrast_reference == "bdl") %>%
plot_volcano() +
my_theme(grid = "y", fsize = fz)
#> filter: removed 215,676 rows (69%), 95,856 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("Day ", parse_number(as.character(contrast)))) %>%
mutate(class = factor(class, levels = unique(.$class))) %>%
select(gene, class, logFC, contrast_reference)
#> mutate: new variable 'class' (character) with 4 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 == "bdl") %>%
select(-contrast_reference) %>%
pivot_wider(names_from = class, values_from = logFC) %>%
write_delim(here(output_path, "stem/input/bdl.txt"), delim = "\t")
#> filter: removed 215,676 rows (69%), 95,856 rows remaining
#> select: dropped one variable (contrast_reference)
#> pivot_wider: reorganized (class, logFC) into (Day 1, Day 3, Day 7, Day 21) [was 95856x3, now 23964x5]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(here(output_path, "stem"), clear_output = T)
#> distinct: no rows removed
#> mutate: new variable 'gene' (character) with 23,964 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, day_1, day_3, day_7, day_21) into (time, value) [was 1557x8, now 7785x5]
#> 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 7,785
#> > =======
#> > rows total 7,785
#> inner_join: added one column (symbol)
#> > rows only in x ( 0)
#> > rows only in y (22,407)
#> > matched rows 7,785
#> > ========
#> > rows total 7,785
#> transmute: dropped one variable (symbol)
#> changed 7,685 values (99%) of 'gene' (0 new NA)
saveRDS(stem_res, here(output_path, "stem_result.rds"))
stem_res %>%
filter(p <= 0.05) %>%
filter(key == "bdl") %>%
distinct() %>%
plot_stem_profiles(model_profile = F, ncol = 2) +
labs(x = "Time in Days", y = "logFC") +
my_theme(grid = "y", fsize = fz)
#> filter: removed 3,485 rows (45%), 4,300 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 3,485 rows (45%), 4,300 rows remaining
#> distinct: removed 3,440 rows (80%), 860 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 1,955 unique values and 0% NA
#> mutate: new variable 'stat' (list) with 1,852 unique values and 0% NA
#> group_by: one grouping variable (group)
#> mutate (grouped): new variable 'fdr' (double) with 1,494 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 1,209 unique values and 0% NA
#> mutate: new variable 'stat' (list) with 990 unique values and 0% NA
#> group_by: one grouping variable (group)
#> mutate (grouped): new variable 'fdr' (double) with 626 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 1,036 unique values and 0% NA
#> mutate: new variable 'stat' (list) with 670 unique values and 0% NA
#> group_by: one grouping variable (group)
#> mutate (grouped): new variable 'fdr' (double) with 343 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"))
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.mogene.2.0.st_3.14.1 DBI_1.1.0
#> [3] RSQLite_2.2.1 patchwork_1.1.1
#> [5] lemon_0.4.5 cowplot_1.1.0
#> [7] AachenColorPalette_1.1.2 msigdf_7.1
#> [9] janitor_2.0.1 dorothea_1.0.1
#> [11] progeny_1.10.0 biobroom_1.20.0
#> [13] broom_0.7.3 limma_3.44.3
#> [15] annotate_1.66.0 XML_3.99-0.5
#> [17] oligo_1.52.1 Biostrings_2.56.0
#> [19] XVector_0.28.0 oligoClasses_1.50.4
#> [21] here_1.0.1 tidylog_1.0.2
#> [23] forcats_0.5.0 stringr_1.4.0
#> [25] dplyr_1.0.2 purrr_0.3.4
#> [27] readr_1.4.0 tidyr_1.1.2
#> [29] tibble_3.0.4 ggplot2_3.3.2
#> [31] tidyverse_1.3.0 mogene20sttranscriptcluster.db_8.7.0
#> [33] org.Mm.eg.db_3.11.4 AnnotationDbi_1.50.3
#> [35] IRanges_2.22.2 S4Vectors_0.26.1
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