---
title: "<img src='tTEscanR_logo.png' style='height:60px; 
vertical-align:middle;'> tTEscanR tRNA-Specific Preprocessing Module"
output: 
  BiocStyle::html_document:
    toc: true
    toc_float: true
    theme: default
    css: style.css
vignette: >
  %\VignetteIndexEntry{2. tRNA-Specific Preprocessing Module}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
bibliography: references.bib
---

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

```{r notes_format, echo = FALSE, results = 'asis'}
cat("
<style>
.note {
    background-color: #f6e8c3;
    border-left: 5px solid #dfc27d;
    padding: 1em;
    margin: 1em 0;
    border-radius: 4px;
}
.warning {
    background-color: #fff3cd;
    border-left: 5px solid #ff9800;
    padding: 1em;
    margin: 1em 0;
    border-radius: 4px;
}
</style>
")
```

<hr>

# 1. Overview

**tTEscanR** includes a dedicated **preprocessing module** that provides 
multiple functions for generating ready-to-use data count matrices. The primary 
goal of this module is to facilitate the transformation from (sc)ATAC-seq count
matrices to tRNA abundance count matrices where tRNA genes are the rows and the 
conditions the columns. Additionally, it helps annotating the tRNA genes.

```{r setup, message = FALSE, warning = FALSE}
# install.packages("/avarassanchez/tTEscanR")
library(tTEscanR)
```

To illustrate the functionality of the tRNA speciic preprocessing module we 
generated a toy example dataset representing a peak count matrix where the 
fragment regions are placed as rows and the samples as columns. We have also
defined the corresponding metadata.

```{r 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 as required by your code
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 (more memory efficient for Seurat/Signac)
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")
```

```{r 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], "-")[[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)
```

```{r set_seurat_obj, message = TRUE, warning = FALSE}
library(Signac)
library(Seurat)

# Now your provided code should work:
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)
```

# 2. Obtaining the tRNA matrix

The first step is to generate the tRNA matrix and translate the peak fragments
into tRNA gene names.

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


# 3. Identifying the optimal tRNA cutoff

```{r session-info, echo=FALSE}
sessionInfo()
```