human-diehl-nafld.Rmd

---
title: "NAFLD patient cohort by Diehl et al."
output: 
  workflowr::wflow_html:
    code_folding: hide
editor_options:
  chunk_output_type: console
---

```{r chunk-setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  autodep = TRUE,
  cache = TRUE
)
```

```{r wall-time-start, cache=FALSE, include=FALSE}
# Track time spent on performing this analysis
start_time <- Sys.time()
```

## Introduction
Here we analysis a patient cohort covering patients with mild and advanced NAFLD generated by [Diehl et al.](https://doi.org/10.1002/hep.26661).

## Libraries and sources
These libraries and sources are used for this analysis.
```{r libs-and-src, message=FALSE, warning=FALSE, cache=FALSE}
library(hgu133plus2.db)

library(tidyverse)
library(tidylog)
library(here)

library(oligo)
library(annotate)
library(GEOquery)
library(limma)
library(biobroom)

library(janitor)
library(msigdf) # remotes::install_github("ToledoEM/msigdf@v7.1")

library(AachenColorPalette)
library(cowplot)
library(lemon)

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.
```{r analysis-specific-params, cache=FALSE}
# i/o
data_path <- "data/human-diehl-nafld"
output_path <- "output/human-diehl-nafld"

# 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).
```{r load-cel-files}
# 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()
```

### Normalization and probe annotation
Probe intensities are normalized with the `rma()` function. Probes are annotated with HGNC symbols.
```{r normalization-and-annotation}
eset <- rma(raw_eset)

# annotate microarray probes with hgnc symbols
expr <- ma_annotate(eset, platforms)
# remove file extension ".CEL.gz"
colnames(expr) <- str_remove(colnames(expr), ".CEL.gz")

# save normalized expression
saveRDS(expr, here(output_path, "normalized_expression.rds"))
```

### Build meta data
Meta information are downloaded from GEO with the accession ID [GSE49541](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49541).
```{r build-meta-data}
# build meta data
df <- getGEO("GSE49541")

meta <- df$GSE49541_series_matrix.txt.gz %>%
  pData() %>%
  rownames_to_column("sample") %>%
  as_tibble() %>%
  dplyr::select(sample, class = `Stage:ch1`) %>%
  mutate(
    class = str_extract(class, "[:alpha:]*"),
    class = factor(class, levels = c("mild", "advanced"))
  )

# 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 NAFLD stage. Only the top 1000 most variable genes are used as features.
```{r pca-norm-data}
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)

saveRDS(pca_result, here(output_path, "pca_result.rds"))

plot_pca(pca_result, feature = "class") +
  my_theme()
```

## Differential gene expression analysis
### Running limma
Differential gene expression analysis via limma with the aim to identify the signature of advanced NAFLD with respect to early stages.
```{r running-limma}
# 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 + class, data = meta)
rownames(design) <- meta$sample
colnames(design) <- levels(meta$class)

# define contrasts
contrasts <- makeContrasts(
  advanced_vs_mild = advanced - mild,
  levels = design
)

limma_result <- run_limma(expr, design, contrasts) %>%
  assign_deg()

saveRDS(limma_result, here(output_path, "limma_result.rds"))
```

### Volcano plots
Volcano plots visualizing the signature of advanced NAFLD with respect to early stages.
```{r volcano-plots}
df <- readRDS(here(output_path, "limma_result.rds"))

df %>%
  plot_volcano() +
  my_theme(grid = "y", fsize = fz)
```

```{r wall-time-end, cache=FALSE, include=FALSE}
duration <- abs(as.numeric(difftime(Sys.time(), start_time, units = "secs")))
t = print(sprintf("%02d:%02d", duration %% 3600 %/% 60,  duration %% 60 %/% 1))
```
Time spend to execute this analysis: `r t` minutes.