---
title: "RFGeneRank: Cross-validated, stable predictive gene ranking for transcriptomics"
author: "Abdulaziz Albeshri"
date: "`r Sys.Date()`"
output: BiocStyle::html_document
vignette: >
  %\VignetteIndexEntry{RFGeneRank}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE
)
```

# Abstract

RFGeneRank is a Bioconductor package that provides a leakage-aware, machine-learning workflow for gene ranking and 
classification using bulk RNA-seq–derived expression matrices. The package emphasizes reproducible evaluation with 
cross-validation and out-of-fold (OOF) preprocessing, and provides interpretable gene importance outputs for downstream 
biological interpretation.

# Introduction and Motivation

High-throughput transcriptomic studies are often collected across different cohorts, sequencing runs, or laboratories. 
When datasets are integrated, batch effects and confounding can inflate predictive performance if preprocessing is performed 
outside cross-validation (information leakage). RFGeneRank addresses this by providing a fold-safe workflow for gene ranking 
and classification that integrates clean inputs, batch-aware options, and convenient reporting.

# Relation to existing Bioconductor packages

Bioconductor provides well-established methodologies for differential expression analysis and batch effect correction. 
RFGeneRank is not intended to replace these methodologies. Instead, it is designed to complement existing analytical workflows 
by adding a machine-learning–based gene ranking layer, with leakage-aware evaluation and diagnostic capabilities, implemented 
in a Bioconductor-compatible framework using standard data containers.

# Installation

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

# A small runnable example

This vignette demonstrates the core protocol used in RFGeneRank:

1. Generate a small transcriptomics-like dataset (genes × samples).
2. Run `prepare_data()`.
3. Run `rank_genes()`.
4. Run a small set of downstream functions and basic plots.

```{r package-overview}
suppressPackageStartupMessages({
  library(RFGeneRank)
  library(SummarizedExperiment)
  library(S4Vectors)
})

set.seed(42)

n_genes <- 300
n_samples <- 60

genes   <- paste0("Gene", seq_len(n_genes))
samples <- paste0("Sample", seq_len(n_samples))

# Sample metadata (rows must match sample names)
meta_df <- data.frame(
  state = factor(rep(c("CTRL","CASE"), each = n_samples/2), levels = c("CTRL","CASE")),
  batch = factor(rep(c("B1","B2"), length.out = n_samples)),
  sex   = factor(rep(c("M","F"), length.out = n_samples)),
  age   = round(stats::runif(n_samples, 25, 65)),
  stringsAsFactors = FALSE,
  check.names = TRUE
)

# Transcriptomics-like expression: strictly positive values (log-normal)
expr <- matrix(
  exp(rnorm(n_genes * n_samples, mean = 2.5, sd = 0.6)),
  nrow = n_genes, ncol = n_samples,
  dimnames = list(genes, samples)
)

# Inject signal in CASE for a subset of genes
signal_genes <- genes[1:25]
case_cols <- meta_df$state == "CASE"
expr[signal_genes, case_cols] <- expr[signal_genes, case_cols] * 1.8

# Critical alignment: metadata rownames must match expression colnames
rownames(meta_df) <- colnames(expr)
stopifnot(identical(colnames(expr), rownames(meta_df)))

# Build SummarizedExperiment
se <- SummarizedExperiment(
  assays  = list(expr = expr),
  colData = DataFrame(meta_df)
)

se
```

# Step 1: prepare_data()

```{r simulate-expression}
# Detect whether the matrix is count-like (integer); our simulated data are continuous.
is_integerish <- function(x) all(abs(x - round(x)) < 1e-8, na.rm = TRUE)
counts_flag <- is_integerish(expr)

se_prep <- prepare_data(
  mats   = list(SummarizedExperiment::assay(se, "expr")),
  metas  = list(meta_df),   # use data.frame for robustness in vignettes
  label_col  = "state",
  batch_col  = "batch",
  log1p      = counts_flag,
  batch_method = "combat",
  batch_correction_scope = "global"
)

se_prep
```

# Step 2: rank_genes()

```{r build-se}
cw <- c(CTRL = 1, CASE = 2)

fit <- rank_genes(
  se_prep,
  label_col = "state",
  cv = "kfold", k = 3,
  n_top = 100,
  trees = 300,
  fold_batch_correction = FALSE,
  batch_col = "batch",
  class_weights = cw,
  auto_confounds = FALSE,
  seed = 42
)

fit
```

# Step 3: downstream utilities

## Top genes

```{r run-rank-genes}
top_genes(fit, n = 10)
```

## Signed importance (directionality)

```{r inspect-fit}
tab_signed <- sign_importance(
  fit, se_prep,
  y = SummarizedExperiment::colData(se_prep)[["state"]],
  method = "mean"
)

head(tab_signed, 10)
```

## Basic plots

```{r plotting-example}
plot_importance(
  fit,
  top = 20,
  map_to_symbol = FALSE
)

plot_roc(fit)

plot_sign_importance(
  fit,
  tab = tab_signed,
  top = 20
)
```

# Session information

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