Qadabra: Quantitative Analysis of Differential Abundance Ranks

(Pronounced ka-da-bra)

Qadabra is a Snakemake workflow for running and comparing several differential abundance (DA) methods on the same microbiome dataset.

Importantly, Qadabra focuses on both FDR corrected p-values and feature ranks and generates visualizations of differential abundance results.

Please note this software is currently a work in progress. Your patience is appreciated as we continue to develop and enhance its features. Please leave an issue on GitHub should you run into any errors.

Installation

Option 1: Pip install from PyPI

pip install qadabra

Qadabra requires the following dependencies:

• snakemake
• click
• biom-format
• pandas
• numpy
• cython
• iow

Check out the tutorial for more in-depth instructions on installation.

Option 2: Install from source (this GitHub repository)

Prerequisites

Before you begin, ensure you have Git and the necessary build tools installed on your system.

Clone the Repository

git clone https://github.com/biocore/qadabra.git

Navigate to repo root directory where the setup.py file is located and then install QADABRA in editable mode

cd qadabra
pip install -e .

Usage

1. Creating the workflow directory

Qadabra can be used on multiple datasets at once. First, we want to create the workflow directory to perform differential abundance with all methods:

qadabra create-workflow --workflow-dest <directory_name>

This command will initialize the workflow, but we still need to point to our dataset(s) of interest.

2. Adding a dataset

We can add datasets one-by-one with the add-dataset command:

qadabra add-dataset \
    --workflow-dest <directory_name> \
    --table <directory_name>/data/table.biom \
    --metadata <directory_name>/data/metadata.tsv \
    --tree <directory_name>/data/my_tree.nwk \
    --name my_dataset \
    --factor-name case_control \
    --target-level case \
    --reference-level control \
    --confounder confounding_variable(s) <confounding_var> \
    --verbose

Let's walkthrough the arguments provided here, which represent the inputs to Qadabra:

• workflow-dest: The location of the workflow that we created earlier
• table: Feature table (features by samples) in BIOM format
• metadata: Sample metadata in TSV format
• tree: Phylogenetic tree in .nwk or other tree format (optional)
• name: Name to give this dataset
• factor-name: Metadata column to use for differential abundance
• target-level: The value in the chosen factor to use as the target
• reference-level: The reference level to which we want to compare our target
• confounder: Any confounding variable metadata columns (optional)
• verbose: Flag to show all preprocessing performed by Qadabra

Your dataset should now be added as a line in my_qadabra/config/datasets.tsv.

You can use qadabra add-dataset --help for more details. To add another dataset, just run this command again with the new dataset information.

3. Running the workflow

The previous commands will create a subdirectory, my_qadabra in which the workflow structure is contained. From the command line, execute the following to start the workflow:

snakemake --use-conda --cores <number of cores preferred> <other options>

Please read the Snakemake documentation for how to run Snakemake best on your system.

When this process is completed, you should have directories figures, results, and log. Each of these directories will have a separate folder for each dataset you added.

4. Generating a report

After Qadabra has finished running, you can generate a Snakemake report of the workflow with the following command:

snakemake --report report.zip

This will create a zipped directory containing the report. Unzip this file and open the report.html file to view the report containing results and visualizations in your browser.

Tutorial

See the tutorial page for a walkthrough on using Qadabra workflow with a microbiome dataset.

FAQs

Coming soon: An FAQs page of commonly asked question on the statistics and code pertaining to Qadabra.

Citation

The manuscript for Qadabra is currently in progress. Please cite this GitHub page if Qadabra is used for your analysis. This project is licensed under the BSD-3 License. See the license file for details.