ATAC pipeline

This pipeline performs peak calling for ATAC data (only cisTopic option is available at the moment)

Contents of Repo

• main.nf - the Nextflow pipeline that runs the whole pipeline
• modules/ - a collection pipeline's of processes for different components
  • modules/pycistopic/main.nf - a collection of processes for cisTopic component
• workflows/ - a collection pipeline's of workflows for different components
  • workflows/pycistopic/main.nf - a collection of processes for cisTopic component
• bin - a colection of python scripts
  • bin/colored_logger.py - a custom python logger with colored output
  • bin/make_pseudobulk.py - a script to make a pseubobulks from fragments file
  • bin/peak_calling.py - a script to call peaks for celltypes' pseudobulks
  • bin/infer_consensus.py - a script to infer consensus peaks from pseudobulks' peak calling results
  • bin/create_cistopic.py - script that creates cisTopic object from consensus .bed and fragments files
• nextflow.config - the configuration script that controls everything

Pipeline Arguments

• --sample_table: Path to .csv file with sample names and paths to the CellRanger-arc output directories
• --celltypes: Path to .csv file with celltype annotation or path to pseudobulk_peaks.csv file with selected celltypes for consensus peak calling
• --callPeaks: Run peak calling for provided celltypes (needs --sample_table and --celltypes)
• --inferConsensus: Run consensus peak calling (needs --sample_table and --celltypes)

Examples of use:

1. Perform peak calling

To run peak calling for each celltype you need to specify sample table (see example/sample_table.csv) and celltype annotation (see example/celltype_annotation.csv):

shell nextflow run main.nf --callPeaks --sample_table ./example/sample_table.csv --celltypes ./example/celltypes.csv

This creates results directory with the following files: results/ ├── log │ ├── fragments_celltype_x_sample.csv # fragment counts matrix with shape (n_celltypes, n_samples) │ ├── fragments_per_celltype.csv # total fragment counts for each celltype │ ├── pseudobulk.audiovisual_neuroepithelium.log │ ├── pseudobulk.craniofacial.log ├── narrowPeaks │ ├── audiovisual_neuroepithelium_peaks.narrowPeak │ ├── craniofacial_peaks.narrowPeak ├── pseudobulk_peaks.csv # contains fragment, peak counts and path to .narrowPeak file for each celltype (see example/pseudobulk_peaks.csv) └── updated_sample_table.csv # updated sample table which contains fragment counts (see example/updated_sample_table.csv)

2. Infer consensus peaks and calculate features

To run consensus peak calling and feature calculation you need to specify an updated sample table generated on previous step (it is essential to use updated table with fragment counts to set appropriate memory limits for jobs) and pseudobulk peaks table generated on previous step with selected celltypes: shell nextflow run main.nf --inferConsensus --sample_table ./example/updated_sample_table.csv --celltypes ./example/pseudobulk_peaks.tsv

This will create a consensus_paeks.bed file, cisTopic and .h5ad objects for each sample and combined cisTopic and .h5ad objects for whole dataset: results ├── consensus_peaks.bed # consensus peaks ├── combined_cistopic_object.pkl ├── combined.h5ad ├── WS_wEMB13400228 │ ├── qc │ ├── WS_wEMB13400228_cistopic_obj.pkl │ └── WS_wEMB13400228.h5ad └── WS_wEMB13400229 ├── qc ├── WS_wEMB13400229_cistopic_obj.pkl └── WS_wEMB13400229.h5ad

3. Perform peak calling, infer consensus peaks and calculate features

To run all steps together you can use the following command: shell nextflow run main.nf --callPeaks --inferConsensus --sample_table ./example/sample_table.csv --celltypes example/celltypes.csv