This document contains scripts for analyzing transposable element (TE) expression in bulk and single-cell RNA sequencing (scRNA-seq) datasets. It contains four scripts.

• VIRTUS (v2.0.1) (https://github.com/yyoshiaki/VIRTUS2)

• Sample list, include all the samples to be analyzed
• Raw fastq file directory

• Single-end RNA-seq data: nohup sh work_VIRTUS.SE.sh sample.lst fastq_dir > log 2>&1 &
• Paired-end RNA-seq data: nohup sh work_VIRTUS.PE.sh sample.lst fastq_dir > log 2>&1 &

• umi_tools (v1.1.2): https://github.com/CGATOxford/UMI-tools
• Viral-Track: https://github.com/PierreBSC/Viral-Track

• Sample list: include all the samples to be analyzed
• Raw fastq file directory
• Parameters.txt: consists in a list of rows where the name of each variable is followed by an equal symbol and then the value of the parameter
• Target_file.txt: contains the list of the file paths generated by umi_tools. The files can be either .fastq or .fastq.gz files

• nohup sh 01_umi_tools_work.sh sample.lst fastq_dir > log 2>&1 &
• nohup Rscript Viral_Track_scanning.R ./Parameters.txt ./Target_file.txt > log 2>&1 &

• scTE (v1.0.0): https://github.com/JiekaiLab/scTE
• scanpy: https://github.com/scverse/scanpy

• Sample list, include all the samples to be analyzed
• Raw bam file directory
• Use scTE generated csv.gz files as input files for scanpy

• nohup sh 0.scTE.sh sample.lst > log 2>&1 &
• nohup python 1.pack.py > log 2>&1 &
• nohup python 2.norm_and_learn.py > log 2>&1 &
• nohup python 3.diffexp_01.py > log1 2>&1 &
• nohup python 3.diffexp_02.py > log2 2>&1 &

• WGCNA: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/

• locusTE_gene_FPKM.txt: This dataset contains expression matrix for each brain biobank. Note that each raw corresponds to a gene or a locus TE and column to a sample
• Metadata.csv: this table contains metadata information for the samples. Note that each raw corresponds to a sample and column to a clinical-pathologic parameter

• nohup Rscript wgcna_pipeline.R > log 2>&1 &