scte

name	scte
display_name	scTE
description	Quantify transposable element expression from single-cell RNA/ATAC-seq BAM files at locus or family level.
license	MIT
metadata	null

scTE

Overview

scTE allocates aligned reads from single-cell RNA-seq or ATAC-seq BAM files to a unified gene + transposable element (TE) reference, producing a per-cell count matrix that contains both protein-coding genes and individual TE loci (or TE family aggregates). It uses prebuilt or custom genome indices that encode gene/exon coordinates alongside RepeatMasker-style TE annotations, and resolves the overlap between TE elements and gene bodies via configurable allocation modes (exclusive, inclusive, nointron). Output is a CSV table by default, or an AnnData .h5ad ready for Scanpy/Seurat workflows.

When to Use This Skill

• Analyzing 10x Genomics or STARsolo scRNA-seq BAM files for TE expression alongside gene counts.
• Studying individual TE loci vs. family-level abundance in single cells.
• Processing C1 Fluidigm or other barcode-less scRNA-seq formats with UMI disabled.

Prerequisites

git clone https://github.com/JiekaiLab/scTE.git
cd scTE
python setup.py install

# Recommended companion tools
conda install -c bioconda samtools
pip install anndata h5py pysam numpy

Requires Python ≥ 3.6. Prebuilt indices for mm10, hg38, panTro6, macFas5, dm6, danRer11, and xenTro9 are bundled with the repository; custom genomes are built with scTE_build -te <BED> -gene <GTF> -o <prefix> -g <genome>.

Input Format

• Aligned BAM/SAM file with cell barcodes in CR:Z or CB:Z tags and UMIs in UR:Z or UB:Z tags.
• Prebuilt genome index (mm10.exclusive.idx, hg38.exclusive.idx, etc.) or custom index from GTF genes and BED TEs.
• Optional: custom gene GTF and TE BED files for scTE_build.

Quick Start

scTE -i inp.bam -o out -x mm10.exclusive.idx --hdf5 True -CB CB -UMI UB

Parameters

Name	Default	Description
-i	(required)	Input aligned BAM/SAM file.
-o	(required)	Output file prefix.
-x	(required)	Path to prebuilt or custom genome index (.idx).
-p	1	Number of threads (~10 GB RAM per thread).
--hdf5	False	Emit .h5ad AnnData instead of CSV when True.
-CB	CR	Cell barcode BAM tag name, or False to disable.
-UMI	UR	UMI BAM tag name, or False to disable.
-m / --mode	exclusive	TE/gene overlap allocation: exclusive, inclusive, or nointron.

Output

• CSV (default) or HDF5 (--hdf5 True) count matrix with cells as rows and genes/TEs as columns.
• HDF5 output is an AnnData .h5ad file directly loadable in Scanpy (sc.read_h5ad) or convertible to Seurat via SeuratDisk::Convert.

Sharp Edges

• Memory ~10 GB per thread; cap -p on modest systems.
• BAM must carry correct barcode/UMI tags: Cell Ranger uses CB:Z/UB:Z, STARsolo uses CR:Z/UR:Z — wrong flags silently produce empty matrices.
• By default, TEs inside exon/UTR regions are assigned only to the gene; use -m inclusive to count both.
• Prebuilt indices ship for mm10, hg38, panTro6, macFas5, dm6, danRer11, xenTro9; other genomes require scTE_build.
• Input BAM should be coordinate-sorted and indexed (samtools sort + samtools index) for best performance; very large BAMs (>50 GB) may take hours.

References

He, J. et al. (2021). "scTE: identifying the activity of transposable elements at single-cell resolution." Nature Communications 12, 1456. DOI: 10.1038/s41467-021-21808-x.