PEAKQC

Periodicity Evaluation As Key aspect of ATAC-seq Quality Control

A python tool for ATAC-seq quality control in single cells. On the bulk level quality control approaches rely on four key aspects:

- signal-to-noise ratio 
- library complexity
- mitochondrial DNA nuclear DNA ratio 
- fragment length distribution 

Hereby relies PEAKQC on the evaluation of the fragment length distribution. While on the bulk level the evaluation is done visually, it is not possible to do that on the single cell level. PEAKQC solves this constraint with an convolution based algorithmic approach.

Workflow

To execute the tool an anndata object and fragments, corresponding to the cells in the anndata have to be provided. The fragments can be either determined from a bamfile directly or by an fragments file in the bed format. If a fragments bedfile is available this is recommended to shorten the runtime.

Installation

PyPi

pip install peakqc

From Source

1. Enviroment & Package Installation

1. Download the repository. This will download the repository to the current directory

[email protected]:loosolab/software/peakqc.git

2. Change the working directory to the newly created repository directory.

cd sc_framework

3. Install analysis environment. Note: using mamba is faster than conda, but this requires mamba to be installed.

mamba env create -f peakqc_env.yml

4. Activate the environment.

conda activate peakqc

5. Install PEAKQC into the enviroment.

pip install .

2. Package Installation

1. Download the repository. This will download the repository to the current directory

[email protected]:loosolab/software/peakqc.git

2. Change the working directory to the newly created repository directory.

cd sc_framework

3. Install PEAKQC into the enviroment.

pip install .

Quickstart

Import the add_fld_metrics() function from the fld_scoring module. Assure that the anndata.obs table contains the same barcodes as the provided fragment source. This includes that the barcodes are in the same format.

The anndata.obs column where the barcodes are stored can be specified with the barcode_col argument. When the barcodes are the index of the table leave the barcode_col argument to be None.

Next provide either a bedfile containing the fragments or a bamfile by the fragments argument. If available using a bedfile is recommended due to shorter runtime.

from peakqc.fld_scoring import *

adata = add_fld_metrics(adata=anndata,
                        fragments=fragments_bedfile,
                        barcode_col=None,
                        plot=True,
                        save_density=None,
                        save_overview=None,
                        sample=0,
                        n_threads=8)