## ----file_settings, include = FALSE-------------------------------------------
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    fig.width = 6,
    fig.height = 4,
    dpi = 96,
    dev = "png"
)

## ----setup, message = FALSE, warning = FALSE, eval = FALSE--------------------
# if (!requireNamespace("BiocManager", quietly = TRUE)) {
#     install.packages("BiocManager")
# }
# 
# BiocManager::install("tTEscanR")

## ----load_setup, message = FALSE, warning = FALSE-----------------------------
library(tTEscanR)

## ----set_peak_matrix, message = TRUE, warning = FALSE-------------------------
library(Matrix)

# Parameters
num_peaks <- 100
num_cells <- 20 # Increased to pass min.cells = 10
set.seed(42)

# Create peak names with "-" separator
chroms <- sample(paste0("chr", 1:3), num_peaks, replace = TRUE)
starts <- seq(1000, by = 5000, length.out = num_peaks)
ends <- starts + 300
peak_names <- paste(chroms, starts, ends, sep = "-")

# Create a sparse matrix
counts <- Matrix(
    rpois(num_peaks * num_cells, lambda = 5),
    nrow = num_peaks,
    ncol = num_cells,
    sparse = TRUE
)

rownames(counts) <- peak_names
colnames(counts) <- paste0("Cell_", seq_len(num_cells))

saveRDS(counts, "foo.RDS")

## ----set_fragmets_file, message = TRUE, warning = FALSE-----------------------
library(dplyr)
library(Rsamtools)

# Generate fragments based on our counts matrix
fragment_list <- list()
for (i in 1:ncol(counts)) {
    cell_name <- colnames(counts)[i]
    for (j in 1:nrow(counts)) {
        cnt <- counts[j, i]
        if (cnt > 0) {
            parts <- strsplit(rownames(counts)[j], "-", fixed = TRUE)[[1]]
            for (k in 1:cnt) {
                fragment_list[[length(fragment_list) + 1]] <- data.frame(
                    chr = parts[1],
                    start = as.numeric(parts[2]) + k,
                    end = as.numeric(parts[3]) - k,
                    cell = cell_name,
                    count = 1
                )
            }
        }
    }
}

fragments_df <- bind_rows(fragment_list) %>%
    arrange(chr, start) # Sorting is mandatory for indexing

# Write to a temporary text file
temp_txt <- "foo.fragments.txt"
write.table(
    fragments_df, temp_txt,
    sep = "\t",
    row.names = FALSE, col.names = FALSE, quote = FALSE
)

# Bgzip compress and index (Requires Rsamtools)
# This creates foo.fragments.txt.gz and foo.fragments.txt.gz.tbi
bgzip(temp_txt, dest = "foo.fragments.txt.gz", overwrite = TRUE)
indexTabix("foo.fragments.txt.gz", format = "bed")

# Clean up the uncompressed temp file
file.remove(temp_txt)

## ----set_seurat_obj, message = TRUE, warning = FALSE--------------------------
library(Signac)
library(Seurat)

counts_loaded <- readRDS("foo.RDS")
fragment_file <- "foo.fragments.txt.gz"

chrom_assay <- CreateChromatinAssay(
    counts = counts_loaded,
    sep = c("-", "-"),
    fragments = fragment_file,
    min.cells = 10,
    min.features = 0
)

chrom_obj <- CreateSeuratObject(
    counts = chrom_assay,
    assay = "peaks"
)

print(chrom_obj)

## ----getMatrix, message = TRUE, warning = FALSE-------------------------------
tRNA_matrix <- tRNAGetMatrix(
    data = chrom_obj, assay = "peaks", confidence_set = NULL, species = "hg38"
)

## ----session-info, echo=FALSE-------------------------------------------------
sessionInfo()