mouse-chronic-ccl4
christianholland
2020-12-18
Last updated: 2020-12-19
Checks: 7 0
Knit directory: meta-liver/
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Introduction
TODO
Libraries and sources
These libraries and sources are used in this analysis
library(tidyverse)
library(tidylog)
library(here)
library(edgeR)
#> Warning: package 'edgeR' was built under R version 4.0.3
#> Warning: package 'limma' was built under R version 4.0.3
library(biobroom)
#> Warning: package 'biobroom' was built under R version 4.0.3
library(AachenColorPalette)
library(cowplot)
library(lemon)
options("tidylog.display" = list(print))
source(here("code/utils-rnaseq.R"))
source(here("code/utils-wrapper.R"))
source(here("code/utils-plots.R"))Analysis specific options
# i/o
data_path <- "data/mouse-chronic-ccl4"
output_path <- "output/mouse-chronic-ccl4"
figure_path <- "output/mouse-chronic-ccl4/figures"
# graphical parameters
# fontsize
fz <- 9Preliminary exploratory analysis
PCA of raw data
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(colnames(count_matrix) == meta$sample)
# remove constant expressed genes and transform to log2 scale
preprocessed_count_matrix <- preprocess_count_matrix(count_matrix)
#> Discarding 7711 genes
#> Keeping 24833 genes
pca_result <- do_pca(preprocessed_count_matrix, meta, top_n_var_genes = 2000)
#> left_join: added 3 columns (time, treatment, group)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 36
#> > ====
#> > rows total 36
plot_pca(pca_result, feature = "time") +
my_theme()
| Version | Author | Date |
|---|---|---|
| 7bb9aab | christianholland | 2020-12-18 |
plot_pca(pca_result, feature = "treatment") +
my_theme()
| Version | Author | Date |
|---|---|---|
| 7bb9aab | christianholland | 2020-12-18 |
Data processing
Normalization
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(meta$sample == colnames(count_matrix))
dge_obj <- DGEList(count_matrix, group = meta$group)
# filter low read counts, TMM normalization and logCPM transformation
norm <- voom_normalization(dge_obj)
#> Discarding 17206 genes
#> Keeping 15338 genes
saveRDS(norm, here(output_path, "normalized_expression.rds"))PCA of normalized data
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
pca_result <- do_pca(expr, meta, top_n_var_genes = 1000)
#> left_join: added 3 columns (time, treatment, group)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 36
#> > ====
#> > rows total 36
saveRDS(pca_result, here(output_path, "pca_result.rds"))
### PC1 vs PC2
plot_pca(pca_result, feature = "time") +
my_theme()
| Version | Author | Date |
|---|---|---|
| 7bb9aab | christianholland | 2020-12-18 |
plot_pca(pca_result, feature = "treatment") +
my_theme()
| Version | Author | Date |
|---|---|---|
| 7bb9aab | christianholland | 2020-12-18 |
Differential gene expression analysis
Running limma
# load expression and meta data
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(colnames(expr) == meta$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
#> group_by: one grouping variable (contrast)
#> mutate (grouped): new variable 'fdr' (double) with 115,674 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(
"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"
))
#> mutate: changed 0 values (0%) of 'contrast' (0 new NA)
#> mutate: new variable 'contrast_reference' (character) with 4 unique values and 0% NA
saveRDS(deg_df, here(output_path, "limma_result.rds"))Volcano plots
df <- readRDS(here(output_path, "limma_result.rds"))
df %>%
filter(contrast_reference == "pure_ccl4") %>%
plot_volcano() +
my_theme(grid = "y")
#> filter: removed 138,042 rows (75%), 46,014 rows remaining
#> rename: renamed one variable (p)
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
#> group_by: 3 grouping variables (contrast_reference, contrast, gene)
#> 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"))
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] stats graphics grDevices datasets utils methods base
#>
#> other attached packages:
#> [1] lemon_0.4.5 cowplot_1.1.0 AachenColorPalette_1.1.2
#> [4] biobroom_1.22.0 broom_0.7.3 edgeR_3.32.0
#> [7] limma_3.46.0 here_1.0.1 tidylog_1.0.2
#> [10] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
#> [13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
#> [16] tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0
#> [19] workflowr_1.6.2
#>
#> loaded via a namespace (and not attached):
#> [1] Biobase_2.50.0 httr_1.4.2 viridisLite_0.3.0
#> [4] jsonlite_1.7.2 modelr_0.1.8 assertthat_0.2.1
#> [7] renv_0.12.3 cellranger_1.1.0 yaml_2.2.1
#> [10] pillar_1.4.7 backports_1.2.1 lattice_0.20-41
#> [13] glue_1.4.2 digest_0.6.27 promises_1.1.1
#> [16] rvest_0.3.6 colorspace_2.0-0 htmltools_0.5.0
#> [19] httpuv_1.5.4 plyr_1.8.6 clisymbols_1.2.0
#> [22] pkgconfig_2.0.3 haven_2.3.1 scales_1.1.1
#> [25] whisker_0.4 later_1.1.0.1 git2r_0.27.1
#> [28] farver_2.0.3 generics_0.1.0 ellipsis_0.3.1
#> [31] withr_2.3.0 BiocGenerics_0.36.0 cli_2.2.0
#> [34] magrittr_2.0.1 crayon_1.3.4 readxl_1.3.1
#> [37] evaluate_0.14 fs_1.5.0 fansi_0.4.1
#> [40] xml2_1.3.2 tools_4.0.2 hms_0.5.3
#> [43] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0
#> [46] locfit_1.5-9.4 compiler_4.0.2 rlang_0.4.9
#> [49] grid_4.0.2 rstudioapi_0.13 labeling_0.4.2
#> [52] rmarkdown_2.6 gtable_0.3.0 DBI_1.1.0
#> [55] R6_2.5.0 gridExtra_2.3 lubridate_1.7.9.2
#> [58] knitr_1.30 rprojroot_2.0.2 stringi_1.5.3
#> [61] parallel_4.0.2 Rcpp_1.0.5 vctrs_0.3.6
#> [64] dbplyr_2.0.0 tidyselect_1.1.0 xfun_0.19