tTEscanR tRNA-Specific Preprocessing Module
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.
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("tTEscanR")To illustrate the functionality of the tRNA specific 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.
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")library(dplyr)
library(Rsamtools)
#> Loading required package: Seqinfo
#> Loading required package: BiocGenerics
#> Loading required package: generics
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#> explain
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#> combine
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#> updateObject
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#> strsplit
# 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)
#> [1] "foo.fragments.txt.gz"
indexTabix("foo.fragments.txt.gz", format = "bed")
#> [1] "foo.fragments.txt.gz.tbi"
# Clean up the uncompressed temp file
file.remove(temp_txt)
#> [1] TRUElibrary(Signac)
library(Seurat)
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#> v5.6 adds significant improvements in speed and efficiency, particularly for large datasets.
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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
)
#> Computing hash
chrom_obj <- CreateSeuratObject(
counts = chrom_assay,
assay = "peaks"
)
print(chrom_obj)
#> An object of class Seurat
#> 100 features across 20 samples within 1 assay
#> Active assay: peaks (100 features, 0 variable features)
#> 2 layers present: counts, data2. Obtaining the tRNA matrix
The first step is to generate the tRNA matrix and translate the peak fragments into tRNA gene names.
tRNA_matrix <- tRNAGetMatrix(
data = chrom_obj, assay = "peaks", confidence_set = NULL, species = "hg38"
)
#> 1 . Importing the high-confidence tRNA annotations.
#> The default hg38 'confidence_set' will be used.
#> 1 . COMPLETED
#> 2 . Filtering tRNA genes with unknown annotations.
#> Loading required namespace: Structstrings
#> The default hg38 'tRNA_name_map' will be used.
#> 2 . COMPLETED
#> 3 . Finding overlaps and aggregating tRNA counts.
#> Extracting reads overlapping genomic regions
#> 3 . COMPLETED3. Identifying the optimal tRNA cutoff
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