Acute APAP mouse model
Christian H. Holland
2020-12-19
Last updated: 2020-12-19
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Knit directory: meta-liver/
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|---|---|---|---|---|
| Rmd | 8954d60 | christianholland | 2020-12-19 | added apap analysis |
Introduction
Here we analysis a mouse model of APAP induced acute liver damage. The transcriptomic profiles were measured at 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(janitor)
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"))Global variables for this analysis
Definition of global variables that are used throughout the entire 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/Projects/meta-liver/data/mouse-acute-apap/12h_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/12h_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/12h_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/12h_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/12h_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/16d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/16d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/16d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/16d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/16d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/1d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/1d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/1d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/1d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/1d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/1h_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/1h_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/1h_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/1h_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/1h_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/2d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/2d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/2d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/2d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/2d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/4d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/4d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/4d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/4d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/4d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/6d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/6d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/6d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/6d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/6d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/6h_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/6h_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/6h_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/6h_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/6h_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/8d_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/8d_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/8d_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/8d_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/8d_m5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/control_m1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/control_m2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/control_m3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/data/mouse-acute-apap/control_m4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Projects/meta-liver/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 133,804 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 184,266 rows (50%), 184,266 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 184,266 rows (50%), 184,266 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 184266x3, now 20474x10]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 20,474 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 2527x13, now 25270x5]
#> 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 25,270
#> > ========
#> > rows total 25,270
#> inner_join: added one column (symbol)
#> > rows only in x ( 0)
#> > rows only in y (17,947)
#> > matched rows 25,270
#> > ========
#> > rows total 25,270
#> transmute: dropped one variable (symbol)
#> changed 25,040 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 4,900 rows (19%), 20,370 rows remaining
#> filter: no rows removed
#> distinct: no rows removed
#> group_by: 4 grouping variables (key, profile, p, time)
#> ungroup: no grouping variables
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 janitor_2.0.1 biobroom_1.22.0
#> [10] broom_0.7.3 limma_3.46.0 annotate_1.68.0
#> [13] XML_3.99-0.5 oligo_1.54.1 Biostrings_2.58.0
#> [16] XVector_0.30.0 oligoClasses_1.52.0 here_1.0.1
#> [19] tidylog_1.0.2 forcats_0.5.0 stringr_1.4.0
#> [22] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
#> [25] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
#> [28] tidyverse_1.3.0 mouse4302.db_3.2.3 org.Mm.eg.db_3.12.0
#> [31] AnnotationDbi_1.52.0 IRanges_2.24.1 S4Vectors_0.28.1
#> [34] Biobase_2.50.0 BiocGenerics_0.36.0 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_1.42.0 fs_1.5.0
#> [7] rstudioapi_0.13 farver_2.0.3
#> [9] affyio_1.60.0 bit64_4.0.5
#> [11] fansi_0.4.1 lubridate_1.7.9.2
#> [13] xml2_1.3.2 codetools_0.2-16
#> [15] splines_4.0.2 knitr_1.30
#> [17] jsonlite_1.7.2 dbplyr_2.0.0
#> [19] BiocManager_1.30.10 compiler_4.0.2
#> [21] httr_1.4.2 backports_1.2.1
#> [23] assertthat_0.2.1 Matrix_1.2-18
#> [25] cli_2.2.0 later_1.1.0.1
#> [27] htmltools_0.5.0 tools_4.0.2
#> [29] gtable_0.3.0 glue_1.4.2
#> [31] GenomeInfoDbData_1.2.4 affxparser_1.62.0
#> [33] Rcpp_1.0.5 cellranger_1.1.0
#> [35] vctrs_0.3.6 preprocessCore_1.52.0
#> [37] iterators_1.0.13 xfun_0.19
#> [39] rvest_0.3.6 lifecycle_0.2.0
#> [41] renv_0.12.3 zlibbioc_1.36.0
#> [43] scales_1.1.1 clisymbols_1.2.0
#> [45] hms_0.5.3 promises_1.1.1
#> [47] MatrixGenerics_1.2.0 SummarizedExperiment_1.20.0
#> [49] yaml_2.2.1 gridExtra_2.3
#> [51] memoise_1.1.0 stringi_1.5.3
#> [53] foreach_1.5.1 GenomeInfoDb_1.26.2
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#> [59] evaluate_0.14 lattice_0.20-41
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#> [65] magrittr_2.0.1 R6_2.5.0
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