---
title: "GSEAlens: Preparing Input Data from limma-voom and DESeq2 Workflows"
author: "Dudali"
date: "`r Sys.Date()`"
output:
  BiocStyle::html_document:
    toc_float: true
    number_sections: true
vignette: >
  %\VignetteIndexEntry{GSEAlens: Preparing Input Data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```

# Installation

GSEAlens is available on Bioconductor. Install the stable release with:

```{r install-package, eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("GSEAlens")
```

The development version can be installed from GitHub:

```{r install-github, eval=FALSE}
if (!requireNamespace("pak", quietly = TRUE))
    install.packages("pak")
pak::pkg_install("DDL095/GSEAlens")
```

# Introduction

This vignette describes how to prepare the input objects that `r Biocpkg("GSEAlens")`
consumes. **GSEAlens itself does not perform differential expression analysis (DEG)**;
it accepts already-fitted `MArrayLM` objects from `r Biocpkg("limma")` or `DESeqDataSet`
objects from `r Biocpkg("DESeq2")`. The following steps are based on the standard
workflows of those packages and are provided here for completeness.

For full DEG workflow documentation, please consult:

- `r Biocpkg("limma")` and `r Biocpkg("edgeR")` vignettes (limma-voom pipeline)

- `r Biocpkg("DESeq2")` vignette

- `r Biocpkg("airway")` package for the example dataset

# Example Data

We use the `r Biocpkg("airway")` dataset (4 vs 4 samples, dex treatment).

```{r setup-environment, results='hide'}
library(airway)
data(airway)
expression_data <- airway
```

# limma-voom Workflow

GSEAlens requires a **no-intercept design** (`~0+group`) so that column names
directly correspond to group names, enabling precise contrast construction.

```{r limma-voom-data-preparation}
library(edgeR)
library(limma)
group_level <- expression_data$dex
design <- model.matrix(~0+group_level)
colnames(design) <- levels(group_level)
compare_end <- combn(levels(group_level), 2, simplify = FALSE)
contrast_strings <- sapply(compare_end, function(x) paste(x[2], x[1], sep = " - "))
contrast_matrix <- makeContrasts(contrasts = contrast_strings, levels = design)
genes_df <- data.frame(
  gene_id = SummarizedExperiment::rowData(expression_data)$gene_id,
  symbol = SummarizedExperiment::rowData(expression_data)$symbol,
  gene_biotype = SummarizedExperiment::rowData(expression_data)$gene_biotype
)
genes_df$Length <- SummarizedExperiment::rowData(expression_data)$gene_seq_end -
                   SummarizedExperiment::rowData(expression_data)$gene_seq_start + 1
gsea_limma_voom_data <- edgeR::DGEList(
  counts = SummarizedExperiment::assay(expression_data, "counts"),
  genes = genes_df,
  norm.factors = NULL,
  group = group_level,
  remove.zeros = TRUE
)
# Filter low-expression genes, keep protein-coding RNAs, deduplicate symbols
total_counts <- edgeR::cpm(gsea_limma_voom_data) |> rowSums()
dup_symbols <- gsea_limma_voom_data$genes$symbol[duplicated(gsea_limma_voom_data$genes$symbol)]
keep <- rep(TRUE, nrow(gsea_limma_voom_data))
for (gene in dup_symbols) {
  idx <- which(gsea_limma_voom_data$genes$symbol == gene)
  best_idx <- idx[which.max(total_counts[idx])]
  remove_idx <- idx[idx != best_idx]
  keep[remove_idx] <- FALSE
}
gsea_limma_voom_data <- gsea_limma_voom_data[keep, ]
rownames(gsea_limma_voom_data) <- gsea_limma_voom_data$genes$symbol
keep_biotype <- gsea_limma_voom_data$genes$gene_biotype == "protein_coding"
gsea_limma_voom_data <- gsea_limma_voom_data[keep_biotype, ]
gsea_limma_voom_data <- edgeR::normLibSizes(gsea_limma_voom_data, method = "TMM")
isexpr <- rowSums(edgeR::cpm(gsea_limma_voom_data) > 1) >= 3
gsea_limma_voom_data <- gsea_limma_voom_data[isexpr, ]
```

Perform limma-voom fitting to obtain the `fit` object.

```{r limma-voom-processing}
VoomOutPut <- voom(gsea_limma_voom_data, design)
fit <- lmFit(object = VoomOutPut, design = design) |>
  contrasts.fit(contrasts = contrast_matrix) |>
  eBayes()
```

# DESeq2 Workflow (SummarizedExperiment Input)

```{r deseq2-se-workflow}
library("DESeq2")
dds_se <- DESeqDataSet(expression_data, design = ~ cell + dex)
# Keep protein-coding genes only
gene_biotypes <- SummarizedExperiment::rowData(dds_se)$gene_biotype
keep_protein_coding <- gene_biotypes == "protein_coding"
dds_se <- dds_se[keep_protein_coding, ]
# Remove low-expression genes (require >=10 reads in >=3 samples)
smallestGroupSize <- 3
keep <- rowSums(DESeq2::counts(dds_se) >= 10) >= smallestGroupSize
dds_se <- dds_se[keep, ]
# Deduplicate gene symbols, keeping the highest-count row
rownames(dds_se) <- SummarizedExperiment::rowData(dds_se)$gene_name
total_counts <- rowSums(SummarizedExperiment::assay(dds_se))
dup_genes <- rownames(dds_se)[duplicated(rownames(dds_se))]
keep <- rep(TRUE, nrow(dds_se))
for (gene in dup_genes) {
  idx <- which(rownames(dds_se) == gene)
  best_idx <- idx[which.max(total_counts[idx])]
  remove_idx <- idx[idx != best_idx]
  keep[remove_idx] <- FALSE
}
dds_se <- dds_se[keep, ]
# Run DESeq2 fitting
dds_se <- DESeq(dds_se)
```

# DESeq2 Workflow (Count Matrix Input)

```{r deseq2-matrix-workflow}
DDS_rawdata <- expression_data
# Keep protein-coding genes only
gene_biotypes <- SummarizedExperiment::rowData(DDS_rawdata)$gene_biotype
keep_protein_coding <- gene_biotypes == "protein_coding"
DDS_rawdata <- DDS_rawdata[keep_protein_coding, ]
# Remove low-expression genes
smallestGroupSize <- 3
keep_epd <- rowSums(SummarizedExperiment::assay(DDS_rawdata, "counts") >= 10) >= smallestGroupSize
DDS_rawdata <- DDS_rawdata[keep_epd, ]
# Deduplicate gene symbols
rownames(DDS_rawdata) <- SummarizedExperiment::rowData(DDS_rawdata)$gene_name
total_counts <- rowSums(SummarizedExperiment::assay(DDS_rawdata))
keep_name <- rep(TRUE, nrow(DDS_rawdata))
dup_genes <- rownames(DDS_rawdata)[duplicated(rownames(DDS_rawdata))]
for (gene in dup_genes) {
  idx <- which(rownames(DDS_rawdata) == gene)
  best_idx <- idx[which.max(total_counts[idx])]
  remove_idx <- idx[idx != best_idx]
  keep_name[remove_idx] <- FALSE
}
DDS_rawdata <- DDS_rawdata[keep_name, ]
# Extract counts and sample metadata into plain matrices
cts <- SummarizedExperiment::assay(DDS_rawdata, "counts")
coldata <- as.data.frame(SummarizedExperiment::colData(DDS_rawdata))
coldata <- coldata[, c("cell", "dex")]
coldata$cell <- factor(coldata$cell)
coldata$dex <- factor(coldata$dex)
# Build DESeqDataSet from matrices and run DESeq2
dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ dex)
dds <- DESeq(dds)
```

# Next Step

Once `fit`, `dds_se`, and `dds` are prepared, return to the main vignette:

```r

vignette("GSEAlens")

```

```{r sessionInfo}
sessionInfo()
```
