## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(ImmuneSigR)

# Search for B cell related records
b_cell_records <- Search_ImmuneSigR("B cell", search_by = "Cell_Type", fixed = TRUE)
head(b_cell_records[, c("cell_name", "Title", "PMID")])

# Retrieve marker genes (filtering for signatures with at least 5 genes)
t_nk_markers <- Get_Markers(c("T cell", "NK cell"), min_genes = 5)
length(t_nk_markers)

## ----base-scoring-------------------------------------------------------------
# Create a dummy expression matrix for demonstration
demo_genes <- unique(unlist(Get_Markers(c("B cell", "T cell"), min_genes = 5)[1:8]))
demo_genes <- demo_genes[seq_len(min(120, length(demo_genes)))]

set.seed(1)
expr_matrix_dummy <- matrix(
  stats::rpois(length(demo_genes) * 12, lambda = 2),
  nrow = length(demo_genes),
  dimnames = list(demo_genes, paste0("cell_", seq_len(12)))
)

# Calculate Rank Scores (UCell-like)
matrix_rank_scores <- Score_ImmuneSigR(
  expr_matrix_dummy, 
  target_cells = c("B cell", "T cell"), 
  min_genes = 5, 
  method = "rank"
)
head(matrix_rank_scores)

## ----seurat, eval = FALSE-----------------------------------------------------
# library(Seurat)
# library(SeuratData)
# library(ggplot2)
# 
# # 1. Load and process pbmc3k dataset
# data("pbmc3k")
# pbmc <- UpdateSeuratObject(pbmc3k)
# pbmc <- NormalizeData(pbmc) |> FindVariableFeatures() |> ScaleData() |> RunPCA() |> RunUMAP(dims = 1:10)
# 
# # 2. Extract expression matrix and define targets
# expr_matrix_real <- as.matrix(pbmc[["RNA"]]$data)
# real_targets <- c("Plasma cell_PMID_33208946", "Conventional Plasma cells_PMID_39406187")
# 
# # 3. Score using precise targets curated from literature
# real_scores <- Score_ImmuneSigR(expr_matrix_real, target_cells = real_targets, min_genes = 5, method = "rank")
# 
# # 4. Add to metadata and visualize
# pbmc <- AddMetaData(pbmc, metadata = real_scores)
# score_cols <- colnames(real_scores)
# 
# p_umap <- FeaturePlot(pbmc, features = score_cols[1:2], ncol = 2, pt.size = 0.8) &
#   scale_colour_gradientn(colours = rev(RColorBrewer::brewer.pal(n = 11, name = "RdYlBu")))
# 
# # --- Plot Title Optimization (Publication Standard) ---
# # Restructure "ImmuneSigR_CellName_PMID_xxxx_score" to "CellName Signature Score\n(PMID: xxxx)"
# clean_titles <- gsub("^ImmuneSigR_(.+)_PMID_([0-9]+)_score$", "\\1 Signature Score\n(PMID: \\2)", score_cols[1:2])
# 
# p_umap[[1]] <- p_umap[[1]] + ggtitle(clean_titles[1]) + theme(plot.title = element_text(hjust = 0.5, size = 12, face = "bold"))
# p_umap[[2]] <- p_umap[[2]] + ggtitle(clean_titles[2]) + theme(plot.title = element_text(hjust = 0.5, size = 12, face = "bold"))
# 
# # Display the plot
# p_umap

## ----gmt-management-----------------------------------------------------------
# We use tempdir() here for CRAN compliance. 
# In practice, you can replace this with your desired output folder (e.g., getwd()).
out_dir <- tempdir()

# Export built-in GMT
exported_gmt <- Export_ImmuneSigR_GMT(out_dir = out_dir)

# Create a custom signature GMT
custom_gmt <- Create_Custom_GMT(
  marker_list = list(
    Custom_T = c("CD3D", "CD3E", "CD8A"),
    Custom_B = c("CD19", "MS4A1", "CD79A")
  ),
  file_name = file.path(out_dir, "custom_demo_signatures.gmt")
)

cat("Exported GMT to:", exported_gmt, "\n")
cat("Created Custom GMT at:", custom_gmt, "\n")