sc-atacseq-explorer

This single cell ATAC-Seq analysis pipeline is designed for advanced analysis of dataset,
produced by 10X Genomics Cell Ranger ATAC.
Aggregated datasets are also supported!

In addition to 10x Genomics results it offers:

Capable to process aggregated data by 10X Genomics Cell Ranger ATAC.
Summary on different conditions in case of aggregated dataset
Flexible data processing with t-SNE/UMAP visualizations in low dimensions space
User defined markers visualization as a heatmap
Closest genes annotations for peaks and clusters
Annotated markers analysis
Bigwig and BED files for clusters and markers ready-to-be-visualized in JBR Genome Browser
Data preparation for Single Cell Explorer
Save all the figures to ready for publication PDF format

10x Genomic Cell Ranger ATAC

Launch batch cell ranger processing.\
NOTE: we don’t launch it in parallel because of martian framework used by Cell Ranger ATAC.

for SAMPLE in $(ls *.fastq.gz | sed -E 's/_S[0-9]_L00.*//g' | sort --unique); do
    cellranger-atac count --id=cra_${SAMPLE} --fastqs=${WORK_DIR} --sample ${SAMPLE} --reference ${REFERENCE}
done

Create aggregation file merged.csv

library_id,fragments,cells
<id1>,<path1>/outs/fragments.tsv.gz,<path1>/outs/singlecell.csv
...
<idN>,<pathN>/outs/fragments.tsv.gz,<pathN>/outs/singlecell.csv

Launch aggregation

cellranger-atac aggr --id=<id> --csv merged.csv --normalize=depth --reference=${REFERENCE}

Prerequisites

Conda environment sc-atac-explorer can be easily created for launching Jupyter Notebook:

```
conda env create -f environment.yml
conda activate sc-atac-explorer
```

Pipeline

Launch jupyter notebook to proceed with the pipeline.

```
conda activate sc-atac-explorer
jupyter notebook
```

sc-atacseq-explorer

10x Genomic Cell Ranger ATAC

Prerequisites

Pipeline

Other pipelines

References