Acute BDL 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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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 78a25d5 | christianholland | 2020-12-19 | minor changes |
| html | 3a3aef8 | christianholland | 2020-12-19 | Build site. |
| Rmd | 0a70042 | christianholland | 2020-12-19 | added acute bdl study |
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(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"))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()
| Version | Author | Date |
|---|---|---|
| 3a3aef8 | christianholland | 2020-12-19 |
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 133,303 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 215,577 rows (69%), 95,812 rows remaining
#> rename: renamed one variable (p)
| Version | Author | Date |
|---|---|---|
| 3a3aef8 | christianholland | 2020-12-19 |
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 215,577 rows (69%), 95,812 rows remaining
#> select: dropped one variable (contrast_reference)
#> pivot_wider: reorganized (class, logFC) into (Day 1, Day 3, Day 7, Day 21) [was 95812x3, now 23953x5]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 23,953 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 1556x8, now 7780x5]
#> 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 7,780
#> > =======
#> > rows total 7,780
#> inner_join: added one column (symbol)
#> > rows only in x ( 0)
#> > rows only in y (22,397)
#> > matched rows 7,780
#> > ========
#> > rows total 7,780
#> transmute: dropped one variable (symbol)
#> changed 7,695 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%), 4,295 rows remaining
#> filter: no rows removed
#> distinct: no rows removed
#> group_by: 4 grouping variables (key, profile, p, time)
#> ungroup: no grouping variables
| Version | Author | Date |
|---|---|---|
| 3a3aef8 | christianholland | 2020-12-19 |
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 janitor_2.0.1
#> [9] biobroom_1.22.0 broom_0.7.3
#> [11] limma_3.46.0 annotate_1.68.0
#> [13] XML_3.99-0.5 oligo_1.54.1
#> [15] Biostrings_2.58.0 XVector_0.30.0
#> [17] oligoClasses_1.52.0 here_1.0.1
#> [19] tidylog_1.0.2 forcats_0.5.0
#> [21] stringr_1.4.0 dplyr_1.0.2
#> [23] purrr_0.3.4 readr_1.4.0
#> [25] tidyr_1.1.2 tibble_3.0.4
#> [27] ggplot2_3.3.2 tidyverse_1.3.0
#> [29] mogene20sttranscriptcluster.db_8.7.0 org.Mm.eg.db_3.12.0
#> [31] AnnotationDbi_1.52.0 IRanges_2.24.1
#> [33] S4Vectors_0.28.1 Biobase_2.50.0
#> [35] 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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#> [57] bitops_1.0-6 matrixStats_0.57.0
#> [59] evaluate_0.14 lattice_0.20-41
#> [61] labeling_0.4.2 bit_4.0.4
#> [63] tidyselect_1.1.0 plyr_1.8.6
#> [65] magrittr_2.0.1 R6_2.5.0
#> [67] generics_0.1.0 DelayedArray_0.16.0
#> [69] pillar_1.4.7 haven_2.3.1
#> [71] whisker_0.4 withr_2.3.0
#> [73] RCurl_1.98-1.2 modelr_0.1.8
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