Chronic CCl4+EtOH mouse model
Last updated: 2021-07-06
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Knit directory: liver-disease-atlas/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 69f7892 | christianholland | 2021-07-06 | analysis of two additional chronic mouse models |
Introduction
Here we analysis a mouse model of CCl4 in combination with alcohol induced chronic liver disease.
Libraries and sources
These libraries and sources are used for this analysis.
library(tidyverse)
library(tidylog)
library(here)
library(edgeR)
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)
library(VennDiagram)
library(gridExtra)
library(ggpubr)
options("tidylog.display" = list(print))
source(here("code/utils-rnaseq.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-chronic-ccl4-etoh"
output_path <- "output/mouse-chronic-ccl4-etoh"
# graphical parameters
# fontsize
fz <- 9Preliminary exploratory analysis
Library size
Barplot of the library size (total counts) for each of the samples.
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
plot_libsize(count_matrix) +
my_theme(fsize = fz)
Count distribution
Violin plots of the raw read counts for each of the samples.
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
count_matrix %>%
tdy("gene", "sample", "count", meta) %>%
arrange(treatment) %>%
ggplot(aes(
x = fct_reorder(sample, as.numeric(treatment)), y = log10(count + 1),
group = sample, fill = treatment
)) +
geom_violin() +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
legend.position = "top"
) +
labs(x = NULL) +
my_theme(grid = "no", fsize = fz)
#> gather: reorganized (GSM3375272, GSM3375273, GSM3375279, GSM3375280, GSM3375281, …) into (sample, count) [was 24504x11, now 245040x3]
#> left_join: added 3 columns (time, treatment, group)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 245,040
#> > =========
#> > rows total 245,040
PCA of raw data
PCA plot of raw read counts contextualized based on the time point and treatment. Before gene with a constant expression across all samples are removed and count values are transformed to log2 scale. Only the top 1000 most variable genes are used as features.
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(colnames(count_matrix) == meta$sample)
# remove constant expressed genes and transform to log2 scale
preprocessed_count_matrix <- preprocess_count_matrix(count_matrix)
#> Discarding 5120 genes
#> Keeping 19384 genes
pca_result <- do_pca(preprocessed_count_matrix, meta, top_n_var_genes = 1000)
#> left_join: added 3 columns (time, treatment, group)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 10
#> > ====
#> > rows total 10
plot_pca(pca_result, feature = "treatment") &
my_theme(fsize = fz)
Data processing
Normalization
Raw read counts are normalized by first filtering out lowly expressed genes, TMM normalization and finally logCPM transformation.
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(meta$sample == colnames(count_matrix))
dge_obj <- DGEList(count_matrix, group = meta$group)
# filter low read counts, TMM normalization and logCPM transformation
norm <- voom_normalization(dge_obj)
#> Discarding 10325 genes
#> Keeping 14179 genes
saveRDS(norm, here(output_path, "normalized_expression.rds"))PCA of normalized data
PCA plot of normalized expression data contextualized based on the time point and treatment. Only the top 1000 most variable genes are used as features.
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
pca_result <- do_pca(expr, meta, top_n_var_genes = 1000)
#> left_join: added 3 columns (time, treatment, group)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 10
#> > ====
#> > rows total 10
saveRDS(pca_result, here(output_path, "pca_result.rds"))
plot_pca(pca_result, feature = "treatment") &
my_theme(fsize = fz)
Differential gene expression analysis
### Running limma Differential gene expression analysis via limma with the aim to identify the effect of CCl4 intoxication while regression out the effect of the oil.
# load expression and meta data
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(colnames(expr) == meta$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 olive oil
ccl_effect = ccl4_20 - control_0,
etoh_effect = etoh_20 - control_0,
combined_effect = ccl4_etoh_20 - control_0,
levels = design
)
limma_result <- run_limma(expr, design, contrasts) %>%
assign_deg()
#> Warning: `tbl_df()` is deprecated as of dplyr 1.0.0.
#> Please use `tibble::as_tibble()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> select: renamed 3 variables (contrast, logFC, pval) and dropped one variable
#> ungroup: no grouping variables
deg_df <- limma_result %>%
mutate(contrast = factor(contrast, levels = c(
"ccl_effect", "etoh_effect", "combined_effect"
))) %>%
mutate(contrast_reference = "effect")
saveRDS(deg_df, here(output_path, "limma_result.rds"))Volcano plots
Volcano plots visualizing the effect of CCl4 on gene expression.
df <- readRDS(here(output_path, "limma_result.rds"))
df %>%
filter(contrast_reference == "effect") %>%
plot_volcano() +
my_theme(grid = "y", fsize = fz)
#> filter: no rows removed
#> rename: renamed one variable (p)
Overlap of genes
df <- readRDS(here(output_path, "limma_result.rds"))
t <- df %>%
filter(contrast_reference == "effect") %>%
rename(class = contrast) %>%
select(-contrast_reference) %>%
group_split(class)
#> filter: no rows removed
#> rename: renamed one variable (class)
#> select: dropped one variable (contrast_reference)
plot_venn_diagram(t)
#> distinct: removed 14,178 rows (>99%), one row remaining
#> distinct: removed 14,178 rows (>99%), one row remaining
#> distinct: removed 14,178 rows (>99%), one row remaining
#> count: now 3 rows and 2 columns, ungrouped
#> count: now 3 rows and 2 columns, ungrouped
#> count: now 3 rows and 2 columns, ungrouped
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#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 14,054 rows (99%), 125 rows remaining
#> filter: removed 14,144 rows (>99%), 35 rows remaining
#> filter: removed 14,144 rows (>99%), 35 rows remaining
#> filter: removed 13,882 rows (98%), 297 rows remaining
#> filter: removed 14,054 rows (99%), 125 rows remaining
#> filter: removed 13,882 rows (98%), 297 rows remaining
#> filter: removed 14,054 rows (99%), 125 rows remaining
#> filter: removed 14,144 rows (>99%), 35 rows remaining
#> filter: removed 13,882 rows (98%), 297 rows remaining
Translation to HGNC symbols
For later comparisons to human data the mouse gene symbols are mapped to their human orthologs.
df <- readRDS(here(output_path, "limma_result.rds"))
mapped_df <- df %>%
translate_gene_ids(from = "symbol_mgi", to = "symbol_hgnc") %>%
drop_na() %>%
# for duplicated genes, keep the one with the highest absolute logFC
group_by(contrast_reference, contrast, gene) %>%
slice_max(order_by = abs(logFC), n = 1, with_ties = F) %>%
ungroup()
#> select: dropped 6 variables (ensembl_mgi, ensembl_v_mgi, entrez_mgi, ensembl_hgnc, ensembl_v_hgnc, …)
#> drop_na: removed 1,905 rows (6%), 30,461 rows remaining
#> rename: renamed one variable (symbol_mgi)
#> left_join: added one column (symbol_hgnc)
#> > rows only in x 5,037
#> > rows only in y (17,348)
#> > matched rows 39,339 (includes duplicates)
#> > ========
#> > rows total 44,376
#> select: renamed one variable (gene) and dropped one variable
#> drop_na: removed 5,037 rows (11%), 39,339 rows remaining
#> slice_max (grouped): removed 1,590 rows (4%), 37,749 rows remaining
#> ungroup: no grouping variables
saveRDS(mapped_df, here(output_path, "limma_result_hs.rds"))Time spend to execute this analysis: 00:16 minutes.
sessionInfo()
#> R version 4.0.5 (2021-03-31)
#> 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] grid stats graphics grDevices datasets utils methods
#> [8] base
#>
#> other attached packages:
#> [1] ggpubr_0.4.0 gridExtra_2.3 VennDiagram_1.6.20
#> [4] futile.logger_1.4.3 patchwork_1.1.1 lemon_0.4.5
#> [7] cowplot_1.1.0 AachenColorPalette_1.1.2 msigdf_7.1
#> [10] janitor_2.0.1 dorothea_1.0.1 progeny_1.10.0
#> [13] biobroom_1.20.0 broom_0.7.3 edgeR_3.30.3
#> [16] limma_3.44.3 here_1.0.1 tidylog_1.0.2
#> [19] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
#> [22] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
#> [25] tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0
#> [28] workflowr_1.6.2
#>
#> loaded via a namespace (and not attached):
#> [1] colorspace_2.0-0 ggsignif_0.6.0 ellipsis_0.3.1
#> [4] rio_0.5.16 rprojroot_2.0.2 snakecase_0.11.0
#> [7] fs_1.5.0 rstudioapi_0.13 farver_2.0.3
#> [10] ggrepel_0.9.0 fansi_0.4.1 lubridate_1.7.9.2
#> [13] xml2_1.3.2 codetools_0.2-18 knitr_1.30
#> [16] jsonlite_1.7.2 bcellViper_1.24.0 dbplyr_2.0.0
#> [19] compiler_4.0.5 httr_1.4.2 backports_1.2.1
#> [22] assertthat_0.2.1 cli_2.2.0 later_1.1.0.1
#> [25] formatR_1.7 htmltools_0.5.0 tools_4.0.5
#> [28] gtable_0.3.0 glue_1.4.2 Rcpp_1.0.5
#> [31] carData_3.0-4 Biobase_2.48.0 cellranger_1.1.0
#> [34] vctrs_0.3.6 xfun_0.19 openxlsx_4.2.3
#> [37] rvest_0.3.6 lifecycle_0.2.0 renv_0.12.3
#> [40] rstatix_0.6.0 scales_1.1.1 clisymbols_1.2.0
#> [43] hms_0.5.3 promises_1.1.1 parallel_4.0.5
#> [46] lambda.r_1.2.4 yaml_2.2.1 curl_4.3
#> [49] stringi_1.5.3 BiocGenerics_0.34.0 zip_2.1.1
#> [52] rlang_0.4.9 pkgconfig_2.0.3 evaluate_0.14
#> [55] lattice_0.20-41 labeling_0.4.2 tidyselect_1.1.0
#> [58] plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
#> [61] generics_0.1.0 DBI_1.1.0 pillar_1.4.7
#> [64] haven_2.3.1 whisker_0.4 foreign_0.8-81
#> [67] withr_2.3.0 abind_1.4-5 modelr_0.1.8
#> [70] crayon_1.3.4 car_3.0-10 futile.options_1.0.1
#> [73] rmarkdown_2.6 locfit_1.5-9.4 readxl_1.3.1
#> [76] data.table_1.13.4 git2r_0.27.1 reprex_0.3.0
#> [79] digest_0.6.27 httpuv_1.5.4 munsell_0.5.0