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 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 <- 9

Data 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).CEL

Normalization 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()

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)

Time spend to execute this analysis: 01:13 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        
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#> 
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#>  [7] rstudioapi_0.13             farver_2.0.3               
#>  [9] affyio_1.58.0               bit64_4.0.5                
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#> [23] assertthat_0.2.1            Matrix_1.2-18              
#> [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                  
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