Chronic CCl4+WTD 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 western type diet 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-wtd"
output_path <- "output/mouse-chronic-ccl4-wtd"
# 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 = interaction(time, diet, 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 (GSM2630025, GSM2630026, GSM2630027, GSM2630028, GSM2630029, …) into (sample, count) [was 32544x9, now 260352x3]
#> left_join: added 4 columns (group, diet, treatment, time)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 260,352
#> > =========
#> > rows total 260,352
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$sample)
# remove constant expressed genes and transform to log2 scale
preprocessed_count_matrix <- preprocess_count_matrix(count_matrix)
#> Discarding 12863 genes
#> Keeping 19681 genes
pca_result <- do_pca(preprocessed_count_matrix, meta, top_n_var_genes = 1000)
#> left_join: added 4 columns (group, diet, treatment, time)
#> > rows only in x 0
#> > rows only in y (0)
#> > matched rows 8
#> > ===
#> > rows total 8
plot_pca(pca_result, feature = "time") +
plot_pca(pca_result, feature = "diet") +
plot_pca(pca_result, feature = "treatment") &
my_theme(fsize = fz)
Data processing
Normalization
Raw read counts are normalized by first filtering out lowly expressed genes, TMM normalization and finally logCPM transformation.
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(meta$sample == colnames(count_matrix))
dge_obj <- DGEList(count_matrix, group = meta$group)
# filter low read counts, TMM normalization and logCPM transformation
norm <- voom_normalization(dge_obj)
#> Warning in voom(.): The experimental design has no replication. Setting weights
#> to 1.
#> Discarding 18711 genes
#> Keeping 13833 genes
saveRDS(norm, here(output_path, "normalized_expression.rds"))PCA of normalized data
PCA plot of normalized expression data contextualized based on the time point and treatment. Only the top 1000 most variable genes are used as features.
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
pca_result <- do_pca(expr, meta, top_n_var_genes = 1000)
#> left_join: added 4 columns (group, diet, treatment, time)
#> > rows only in x 0
#> > rows only in y (0)
#> > matched rows 8
#> > ===
#> > rows total 8
saveRDS(pca_result, here(output_path, "pca_result.rds"))
plot_pca(pca_result, feature = "time") +
plot_pca(pca_result, feature = "diet") +
plot_pca(pca_result, feature = "treatment") &
my_theme(fsize = fz)
Differential gene expression analysis
Since there are no replicates we cannot perform standard differential gene expression analysis. Instead we compute manually the logFC.
# load expression and meta data
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(colnames(expr) == meta$sample)
deg_df <- expr %>%
data.frame() %>%
rownames_to_column("gene") %>%
as_tibble() %>%
transmute(
gene,
ccl_effect_12w = GSM2630026 - GSM2630025,
wd_effect_12w = GSM2630027 - GSM2630025,
combined_effect_12w = GSM2630028 - GSM2630025,
ccl_effect_24w = GSM2630030 - GSM2630029,
wd_effect_24w = GSM2630031 - GSM2630029,
combined_effect_24w = GSM2630032 - GSM2630029,
) %>%
pivot_longer(-gene, names_to = "contrast", values_to = "logFC") %>%
mutate(contrast_reference = "combined_effect")
#> transmute: dropped 8 variables (GSM2630025, GSM2630026, GSM2630027, GSM2630028, GSM2630029, …)
#> new variable 'ccl_effect_12w' (double) with 10,709 unique values and 0% NA
#> new variable 'wd_effect_12w' (double) with 10,175 unique values and 0% NA
#> new variable 'combined_effect_12w' (double) with 10,874 unique values and 0% NA
#> new variable 'ccl_effect_24w' (double) with 11,461 unique values and 0% NA
#> new variable 'wd_effect_24w' (double) with 10,644 unique values and 0% NA
#> new variable 'combined_effect_24w' (double) with 11,579 unique values and 0% NA
#> pivot_longer: reorganized (ccl_effect_12w, wd_effect_12w, combined_effect_12w, ccl_effect_24w, wd_effect_24w, …) into (contrast, logFC) [was 13833x7, now 82998x3]
saveRDS(deg_df, here(output_path, "limma_result.rds"))Distribution of logFC
df <- readRDS(here(output_path, "limma_result.rds"))
df %>%
ggplot(aes(x = contrast, y = logFC)) +
geom_violin() +
my_theme(grid = "y", fsize = fz)
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 8,694
#> > rows only in y (17,422)
#> > matched rows 78,234 (includes duplicates)
#> > ========
#> > rows total 86,928
#> select: renamed one variable (gene) and dropped one variable
#> drop_na: removed 8,694 rows (10%), 78,234 rows remaining
#> slice_max (grouped): removed 3,690 rows (5%), 74,544 rows remaining
#> ungroup: no grouping variables
saveRDS(mapped_df, here(output_path, "limma_result_hs.rds"))Time spend to execute this analysis: 00:15 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
#> [82] viridisLite_0.3.0