scTSS: Detection and couting alternative TSS in single cells
Project description
Installation
You can install from this GitHub repository for latest (often development) version by following command line
pip install -U git+https://github.com/StatBiomed/scTSS
In either case, add --user if you don’t have the write permission for your Python environment.
Quick start
scTSS-count
STEP1: Processing
scTSS mainly deal with the output from cellranger (a common alignment tool for 10x data).
The preprocessing procedure based on the output file of cellranger.
1. cd /cellranger_out/outs
2. samtools view possorted_genome_bam.bam | LC_ALL=C grep "xf:i:25" > body_filtered_sam
3. samtools view -H possorted_genome_bam.bam > header_filted_sam
4. cat header_filted_sam body_filtered_sam > possorted_genome_bam_filterd.sam
5. samtools view -b possorted_genome_bam_filterd.sam > possorted_genome_bam_filterd.bam
6. samtools index possorted_genome_bam_filterd.bam possorted_genome_bam_filterd.bam.bai
STEP2: Run scTSS-count
scTSS-count --gtf $gtfFile --refFastq $fastFile --bam $possorted_genome_bam_filterd.bam -c $cluster_toscTSS.tsv -o $output_fileFold --mode Unannotation
Want to learn about more parameter, you can use scTSS-count --help to check.
You can find out the example file in the test folder. Please make sure you also have the same column name.
Here, you can select one of the mode from annotation and unannotation.
Unannotation means that you can detect novel TSS. The distance between different TSS may be wide.
Annotation means that you can detect TSS based on the annotation. The distance between different TSS may be narrow.
You can check our paper to learn more detail.
scTSS-quant
scTSS-quant -g $filtered_feature_bc_matrix -c $cluster_toscTSS.tsv --countOut $scTSS_count_folder -m cluster -o $scTSS_quant_folder
Please use scTSS-quant --help to check more parameter.
Here, you can select one of the mode from cluster and disease.
Cluster means that you can detect cell type-specific TSS. Any multiple groups detection can use this mode.
Disease mode help you select disease-specific TSS. Any two groups detection can use this mode.
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