Chronic CCl4 mouse model
Last updated: 2021-02-28
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Knit directory: liver-disease-atlas/
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Introduction
Here we analysis a mouse model of CCl4 induced chronic liver disease. The transcriptomic profiles were measured at time point 0, 2, 6, and 12 month. For the time points 2 and 12 month time-matched oil controls are available.
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"
output_path <- "output/mouse-chronic-ccl4"
# 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 (I00001, I00002, I00003, I00004, I00005, …) into (sample, count) [was 32544x37, now 1171584x3]
#> left_join: added 3 columns (time, treatment, group)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 1,171,584
#> > ===========
#> > rows total 1,171,584
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$sample)
# remove constant expressed genes and transform to log2 scale
preprocessed_count_matrix <- preprocess_count_matrix(count_matrix)
#> Discarding 7711 genes
#> Keeping 24833 genes
pca_result <- do_pca(preprocessed_count_matrix, meta, top_n_var_genes = 1000)
#> left_join: added 3 columns (time, treatment, group)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 36
#> > ====
#> > rows total 36
plot_pca(pca_result, feature = "time") +
plot_pca(pca_result, feature = "treatment") &
my_theme(fsize = fz)
Data processing
Normalization
Raw read counts are normalized by first filtering out lowly expressed genes, TMM normalization and finally logCPM transformation.
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(meta$sample == colnames(count_matrix))
dge_obj <- DGEList(count_matrix, group = meta$group)
# filter low read counts, TMM normalization and logCPM transformation
norm <- voom_normalization(dge_obj)
#> Discarding 17206 genes
#> Keeping 15338 genes
saveRDS(norm, here(output_path, "normalized_expression.rds"))PCA of normalized data
PCA plot of normalized expression data contextualized based on the time point and treatment. Only the top 1000 most variable genes are used as features.
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
pca_result <- do_pca(expr, meta, top_n_var_genes = 1000)
#> left_join: added 3 columns (time, treatment, group)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 36
#> > ====
#> > rows total 36
saveRDS(pca_result, here(output_path, "pca_result.rds"))
plot_pca(pca_result, feature = "time") +
plot_pca(pca_result, feature = "treatment") &
my_theme(fsize = fz)
Differential gene expression analysis
Running limma
Differential gene expression analysis via limma with the aim to identify the effect of CCl4 intoxication while regression out the effect of the oil.
# load expression and meta data
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(colnames(expr) == meta$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
oil_2m_vs_0m = oil.2 - wt,
oil_12m_vs_0m = oil.12 - wt,
oil_12m_vs_2m = oil.12 - oil.2,
# treatment vs control ignoring the effect of oil
ccl_2m_vs_0m = ccl4.2 - wt,
ccl_6m_vs_0m = ccl4.6 - wt,
ccl_12m_vs_0m = ccl4.12 - wt,
# treatment vs control regressing out the effect of oil
pure_ccl_2m_vs_0m = (ccl4.2 - wt) - (oil.2 - wt),
pure_ccl_6m_vs_0m = (ccl4.6 - wt) - ((oil.2 + oil.12) / 2 - wt),
pure_ccl_12m_vs_0m = (ccl4.12 - wt) - (oil.12 - wt),
# consecutive time point comparison
consec_12m_vs_6m = ccl4.12 - ccl4.6,
consec_12m_vs_2m = ccl4.12 - ccl4.2,
# consec_48w_vs_8w_2 = (ccl4.48 - oil.48) - (ccl4.8 - oil.8),
consec_6m_vs_2m = ccl4.6 - ccl4.2,
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_2m_vs_0m", "ccl_6m_vs_0m",
"ccl_12m_vs_0m",
"pure_ccl_2m_vs_0m",
"pure_ccl_6m_vs_0m",
"pure_ccl_12m_vs_0m",
"consec_6m_vs_2m",
"consec_12m_vs_2m",
"consec_12m_vs_6m",
"oil_2m_vs_0m", "oil_12m_vs_0m",
"oil_12m_vs_2m"
))) %>%
mutate(contrast_reference = case_when(
str_detect(contrast, "oil") ~ "oil",
str_detect(contrast, "^pure_ccl") ~ "pure_ccl4",
str_detect(contrast, "^ccl") ~ "ccl4",
str_detect(contrast, "consec") ~ "consec"
))
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 == "pure_ccl4") %>%
plot_volcano() +
my_theme(grid = "y", fsize = fz)
#> filter: removed 138,042 rows (75%), 46,014 rows remaining
#> rename: renamed one variable (p)
Overlap of genes
df <- readRDS(here(output_path, "limma_result.rds"))
t = df %>%
filter(contrast_reference == "pure_ccl4") %>%
mutate(class = str_c("Month ", parse_number(as.character(contrast)))) %>%
select(-contrast_reference, -contrast) %>%
mutate(class = factor(class, levels = c("Month 2", "Month 6", "Month 12"))) %>%
group_split(class)
#> filter: removed 138,042 rows (75%), 46,014 rows remaining
#> select: dropped 2 variables (contrast, contrast_reference)
plot_venn_diagram(t)
#> distinct: removed 15,337 rows (>99%), one row remaining
#> distinct: removed 15,337 rows (>99%), one row remaining
#> distinct: removed 15,337 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
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 15,060 rows (98%), 278 rows remaining
#> filter: removed 15,078 rows (98%), 260 rows remaining
#> filter: removed 15,078 rows (98%), 260 rows remaining
#> filter: removed 13,713 rows (89%), 1,625 rows remaining
#> filter: removed 15,060 rows (98%), 278 rows remaining
#> filter: removed 13,713 rows (89%), 1,625 rows remaining
#> filter: removed 15,060 rows (98%), 278 rows remaining
#> filter: removed 15,078 rows (98%), 260 rows remaining
#> filter: removed 13,713 rows (89%), 1,625 rows remaining
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 15,210 rows (99%), 128 rows remaining
#> filter: removed 15,196 rows (99%), 142 rows remaining
#> filter: removed 15,196 rows (99%), 142 rows remaining
#> filter: removed 14,764 rows (96%), 574 rows remaining
#> filter: removed 15,210 rows (99%), 128 rows remaining
#> filter: removed 14,764 rows (96%), 574 rows remaining
#> filter: removed 15,210 rows (99%), 128 rows remaining
#> filter: removed 15,196 rows (99%), 142 rows remaining
#> filter: removed 14,764 rows (96%), 574 rows remaining
Top genes of the overlap
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "pure_ccl4") %>%
filter(regulation != "ns")
#> filter: removed 138,042 rows (75%), 46,014 rows remaining
#> filter: removed 43,007 rows (93%), 3,007 rows remaining
top_genes_ranked = df %>%
# filter for genes that are deregulated at all time points
group_by(gene, regulation) %>%
filter(n() == 3) %>%
summarise(mean_logfc = mean(logFC)) %>%
group_by(regulation) %>%
mutate(rank = row_number(-abs(mean_logfc))) %>%
ungroup()
#> filter (grouped): removed 2,449 rows (81%), 558 rows remaining
#> summarise: now 186 rows and 3 columns, one group variable remaining (gene)
#> ungroup: no grouping variables
top_genes = df %>%
inner_join(top_genes_ranked, by=c("gene", "regulation"))
#> inner_join: added 2 columns (mean_logfc, rank)
#> > rows only in x (2,449)
#> > rows only in y ( 0)
#> > matched rows 558
#> > =======
#> > rows total 558
top_genes %>%
filter(rank <= 5) %>%
ggplot(aes(x=fct_reorder(gene, mean_logfc), y=logFC, group = contrast, fill = contrast)) +
geom_col(position = "dodge") +
facet_rep_wrap(~regulation, ncol = 1, scales = "free") +
my_theme(grid = "y", fsize = fz) +
labs(x="Gene", y="logFC")
#> filter: removed 528 rows (95%), 30 rows remaining
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 %>%
filter(contrast_reference %in% c("pure_ccl4")) %>%
mutate(class = str_c("Month ", parse_number(as.character(contrast)))) %>%
mutate(class = factor(class, levels = c("Month 2", "Month 6", "Month 12"))) %>%
select(gene, class, logFC, contrast_reference)
#> filter: removed 138,042 rows (75%), 46,014 rows remaining
#> select: dropped 5 variables (contrast, statistic, pval, fdr, regulation)
stem_inputs %>%
select(-contrast_reference) %>%
pivot_wider(names_from = class, values_from = logFC) %>%
write_delim(here(output_path, "stem/input/pure_ccl4.txt"), delim = "\t")
#> select: dropped one variable (contrast_reference)
#> pivot_wider: reorganized (class, logFC) into (Month 2, Month 6, Month 12) [was 46014x3, now 15338x4]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(file.path(output_path, "stem"), clear_output = T)
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> gene = col_character(),
#> `Month 2` = col_double(),
#> `Month 6` = col_double(),
#> `Month 12` = col_double()
#> )
#> distinct: no rows removed
#> distinct: no rows removed
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> gene = col_character(),
#> SPOT = col_character(),
#> Profile = col_double(),
#> `0` = col_double(),
#> `Month 2` = col_double(),
#> `Month 6` = col_double(),
#> `Month 12` = col_double()
#> )
#> select: dropped one variable (spot)
#> gather: reorganized (x0, month_2, month_6, month_12) into (time, value) [was 3193x7, now 12772x5]
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> `Profile ID` = col_double(),
#> `Profile Model` = col_character(),
#> `Cluster (-1 non-significant)` = col_double(),
#> `# Genes Assigned` = col_double(),
#> `# Gene Expected` = col_double(),
#> `p-value` = col_double()
#> )
#> select: renamed 4 variables (profile, y_coords, size, p) and dropped 2 variables
#> Joining, by = c("profile", "key")
#> inner_join: added 3 columns (y_coords, size, p)
#> > rows only in x ( 0)
#> > rows only in y ( 0)
#> > matched rows 12,772
#> > ========
#> > rows total 12,772
#> inner_join: added one column (symbol)
#> > rows only in x ( 0)
#> > rows only in y (12,145)
#> > matched rows 12,772
#> > ========
#> > rows total 12,772
#> transmute: dropped one variable (symbol)
#> changed 12,644 values (99%) of 'gene' (0 new NA)
saveRDS(stem_res, here(output_path, "stem_result.rds"))
stem_res %>%
filter(p <= 0.05) %>%
filter(key == "pure_ccl4") %>%
distinct() %>%
plot_stem_profiles(model_profile = F) +
labs(x = "Time in Month", y = "logFC") +
my_theme(grid = "y", fsize = fz)
#> filter: removed 3,080 rows (24%), 9,692 rows remaining
#> filter: no rows removed
#> distinct: no rows removed
#> `summarise()` regrouping output by 'key', 'profile', 'p' (override with `.groups` argument)
#> 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 3,080 rows (24%), 9,692 rows remaining
#> distinct: removed 7,269 rows (75%), 2,423 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
#> 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)
#> select: renamed 2 variables (geneset, gene) and dropped one variable
#> 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)
#> 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)
#> 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)
#> 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)
#> 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)
#> 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)
#> 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)
#> 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"))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 22,992
#> > rows only in y ( 16,094)
#> > matched rows 172,404 (includes duplicates)
#> > =========
#> > rows total 195,396
#> select: renamed one variable (gene) and dropped one variable
#> drop_na: removed 22,992 rows (12%), 172,404 rows remaining
#> slice_max (grouped): removed 11,328 rows (7%), 161,076 rows remaining
#> ungroup: no grouping variables
saveRDS(mapped_df, here(output_path, "limma_result_hs.rds"))Time spend to execute this analysis: 01:18 minutes.
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] 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_0.2-16 knitr_1.30
#> [16] jsonlite_1.7.2 bcellViper_1.24.0 dbplyr_2.0.0
#> [19] compiler_4.0.2 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_4.0.2
#> [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_4.0.2
#> [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_0.8-80
#> [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