Acute LPS mouse model
Last updated: 2021-03-29
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Knit directory: liver-disease-atlas/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c5a4d0c | christianholland | 2021-03-29 | added pca of z-scores |
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| html | 5e36b25 | christianholland | 2021-02-28 | Build site. |
| Rmd | 7f331d0 | christianholland | 2021-02-28 | wflow_publish("analysis/*", delete_cache = TRUE, republish = TRUE) |
Introduction
Here we analysis a mouse model of LPS induced acute liver damage.
Libraries and sources
These libraries and sources are used for this analysis.
library(mouse4302.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-lps"
output_path <- "output/mouse-acute-lps"
# 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()
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/CCl4FHD1M1A_16_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/CCl4FHD1M2B_17_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/CCl4FHD1M3C_18_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/CCl4FHD3M1B_19_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/CCl4FHD3M2B_20_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/CCl4FHD3M3A_21_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/KD1m1_3_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/KD1m2_2_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/KD1m3_1_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/KFHD1M1C_9_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/KFHD1M2B_10_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/KFHD1M3B_11_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/LPSD1m2_4_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/LPSD1m3_5_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/LPSD1m4_6_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/LPSD1m5_7_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/LPSD1m6_8_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/LPSFHD1M1B_12_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/LPSFHD1M2A_13_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/LPSFHD1M3B_14_(Mouse430_2).CEL
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/mouse-acute-lps/LPSFHD1M4B_15_(Mouse430_2).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)
colnames(expr) <- str_remove_all(colnames(expr), "_\\(Mouse430_2\\).CEL")
# 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") %>%
mutate(treatment = case_when(
str_detect(sample, "CCl4") ~ "CCl4",
str_detect(sample, "LPS") ~ "LPS",
str_detect(sample, str_c(c("KD1", "KFHD"), collapse = "|")) ~ "control"
)) %>%
mutate(origin = case_when(
str_detect(sample, "FHD") ~ "HC",
TRUE ~ "liver"
)) %>%
mutate(time = case_when(
str_detect(sample, "D1") ~ 1,
str_detect(sample, "D3") ~ 3
)) %>%
unite(group, origin, treatment, time, remove = F) %>%
mutate(
treatment = factor(treatment, levels = c("control", "LPS", "CCl4")),
origin = factor(origin, levels = c("liver", "HC")),
time = ordered(time),
group = as_factor(group)
)
#> mutate: new variable 'treatment' (character) with 3 unique values and 0% NA
#> mutate: new variable 'origin' (character) with 2 unique values and 0% NA
#> mutate: new variable 'time' (double) with 2 unique values and 0% NA
#> mutate: converted 'group' from character to factor (0 new NA)
#> converted 'treatment' from character to factor (0 new NA)
#> converted 'origin' from character to factor (0 new NA)
#> converted 'time' from double to ordered 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 origin 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 (group, treatment, origin, time)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 21
#> > ====
#> > rows total 21
saveRDS(pca_result, here(output_path, "pca_result.rds"))
plot_pca(pca_result, feature = "origin") +
plot_pca(pca_result, feature = "treatment") &
my_theme()
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Differential gene expression analysis
Running limma
Differential gene expression analysis via limma with the aim to identify the effect of LPS intoxication.
# 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(
# LPS vs control in liver and hepatocytes
inLiver_lps_vs_ctrl = liver_LPS_1 - liver_control_1,
inHC_lps_vs_ctrl_day1 = HC_LPS_1 - HC_control_1,
# CCl4 vs control in hepatocytes for day 1 and 3
inHC_ccl_vs_ctrl_day1 = HC_CCl4_1 - HC_control_1,
inHC_ccl_vs_ctrl_day3 = HC_CCl4_3 - HC_control_1,
# LPS vs CCl4 in hepatocytes day 1 and 3
inHC_ccl_vs_lps_day1 = HC_CCl4_1 - HC_LPS_1,
inHC_ccl_vs_lps_day3 = HC_CCl4_3 - HC_LPS_1,
# liver tissue vs hepatocytes
liver_vs_hc_lps = liver_LPS_1 - HC_LPS_1,
hc_vs_liver_ctrl = HC_control_1 - liver_control_1,
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 87,622 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)
saveRDS(limma_result, here(output_path, "limma_result.rds"))Volcano plots
Volcano plots visualizing the effect of LPS on gene expression.
df <- readRDS(here(output_path, "limma_result.rds"))
df %>%
plot_volcano() +
my_theme(grid = "y", fsize = fz)
#> rename: renamed one variable (p)
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
z-scores
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(output_path, "meta_data.rds")) %>%
filter(origin == "liver")
#> filter: removed 13 rows (62%), 8 rows remaining
# extract name of control samples
ctrl_samples <- meta %>%
filter(treatment == "control") %>%
pull(sample)
#> filter: removed 5 rows (62%), 3 rows remaining
treated_samples <- meta %>%
filter(treatment != "control") %>%
pull(sample)
#> filter: removed 3 rows (38%), 5 rows remaining
# compute mean and standard deviation of gene expresion in control ssample
ctrl_mean <- expr[, ctrl_samples] %>%
apply(1, mean)
ctrl_sd <- expr[, ctrl_samples] %>%
apply(1, sd)
# check whether genes are in correct order
stopifnot(names(ctrl_mean) == colnames(t(expr)))
stopifnot(names(ctrl_mean) == colnames(t(expr)))
# z-score transformation of gene expression w.r.t control samples
z_scores <- expr[, treated_samples] %>%
t() %>%
scale(center = ctrl_mean, scale = ctrl_sd) %>%
t() %>%
data.frame(check.names = FALSE)
saveRDS(z_scores, here(output_path, "z_scores.rds"))Time spend to execute this analysis: 01:06 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] parallel stats4 stats graphics grDevices datasets utils
#> [8] methods base
#>
#> other attached packages:
#> [1] pd.mouse430.2_3.12.0 DBI_1.1.0 RSQLite_2.2.1
#> [4] patchwork_1.1.1 lemon_0.4.5 cowplot_1.1.0
#> [7] AachenColorPalette_1.1.2 janitor_2.0.1 biobroom_1.20.0
#> [10] broom_0.7.3 limma_3.44.3 annotate_1.66.0
#> [13] XML_3.99-0.5 oligo_1.52.1 Biostrings_2.56.0
#> [16] XVector_0.28.0 oligoClasses_1.50.4 here_1.0.1
#> [19] tidylog_1.0.2 forcats_0.5.0 stringr_1.4.0
#> [22] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
#> [25] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
#> [28] tidyverse_1.3.0 mouse4302.db_3.2.3 org.Mm.eg.db_3.11.4
#> [31] AnnotationDbi_1.50.3 IRanges_2.22.2 S4Vectors_0.26.1
#> [34] Biobase_2.48.0 BiocGenerics_0.34.0 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_1.40.0 fs_1.5.0
#> [7] rstudioapi_0.13 farver_2.0.3
#> [9] affyio_1.58.0 bit64_4.0.5
#> [11] fansi_0.4.1 lubridate_1.7.9.2
#> [13] xml2_1.3.2 codetools_0.2-18
#> [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_1.3-2
#> [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_1.2.3 affxparser_1.60.0
#> [33] Rcpp_1.0.5 cellranger_1.1.0
#> [35] vctrs_0.3.6 preprocessCore_1.50.0
#> [37] iterators_1.0.13 xfun_0.19
#> [39] rvest_0.3.6 lifecycle_0.2.0
#> [41] renv_0.12.3 zlibbioc_1.34.0
#> [43] scales_1.1.1 clisymbols_1.2.0
#> [45] hms_0.5.3 promises_1.1.1
#> [47] SummarizedExperiment_1.18.2 yaml_2.2.1
#> [49] gridExtra_2.3 memoise_1.1.0
#> [51] stringi_1.5.3 foreach_1.5.1
#> [53] GenomeInfoDb_1.24.2 rlang_0.4.9
#> [55] pkgconfig_2.0.3 bitops_1.0-6
#> [57] matrixStats_0.57.0 evaluate_0.14
#> [59] lattice_0.20-41 labeling_0.4.2
#> [61] bit_4.0.4 tidyselect_1.1.0
#> [63] plyr_1.8.6 magrittr_2.0.1
#> [65] R6_2.5.0 generics_0.1.0
#> [67] DelayedArray_0.14.1 pillar_1.4.7
#> [69] haven_2.3.1 whisker_0.4
#> [71] withr_2.3.0 RCurl_1.98-1.2
#> [73] modelr_0.1.8 crayon_1.3.4
#> [75] rmarkdown_2.6 grid_4.0.2
#> [77] readxl_1.3.1 blob_1.2.1
#> [79] git2r_0.27.1 reprex_0.3.0
#> [81] digest_0.6.27 xtable_1.8-4
#> [83] ff_4.0.4 httpuv_1.5.4
#> [85] munsell_0.5.0