https://github.com/jaydu1/causarray

causarray is a Python module for simultaneous causal inference with an array of outcomes.
https://github.com/jaydu1/causarray

causal-inference differential-expression genomics single-cell unmeasured-confounding

Last synced: about 1 year ago
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causarray is a Python module for simultaneous causal inference with an array of outcomes.

• Host: GitHub
• URL: https://github.com/jaydu1/causarray
• Owner: jaydu1
• License: mit
• Created: 2024-01-12T18:34:04.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2025-03-20T08:19:23.000Z (about 1 year ago)
• Last Synced: 2025-03-20T09:27:49.023Z (about 1 year ago)
• Topics: causal-inference, differential-expression, genomics, single-cell, unmeasured-confounding
• Language: Jupyter Notebook
• Homepage:
• Size: 20.6 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# causarray

Advances in single-cell sequencing and CRISPR technologies have enabled detailed case-control comparisons and experimental perturbations at single-cell resolution. However, uncovering causal relationships in observational genomic data remains challenging due to selection bias and inadequate adjustment for unmeasured confounders, particularly in heterogeneous datasets. To address these challenges, we introduce `causarray` [Du25], a doubly robust causal inference framework for analyzing array-based genomic data at both bulk-cell and single-cell levels. `causarray` integrates a generalized confounder adjustment method to account for unmeasured confounders and employs semiparametric inference with flexible machine learning techniques to ensure robust statistical estimation of treatment effects.

## Usage

We recommend using `causarray` in a conda environment:

```cmd

# create a new conda environment and install the necessary packages

conda create -n causarray python=3.12 -y

# activate the environment

conda activate causarray

```

The module can be installed via PyPI:

```cmd

pip install causarray

```

For `R` users, `reticulate` can be used to call `causarray` from `R`.

The documentation and tutorials using both `Python` and `R` are available at [causarray.readthedocs.io](https://causarray.readthedocs.io/en/latest/).

## Logs

- [x] (2025-01-30) Python package released on PyPI

- [x] (2025-02-01) code for reproducing figures in paper

- [x] (2025-02-02) Tutorial for Python and R

- [ ] Documentation

## References

[Du25] Jin-Hong Du, Maya Shen, Hansruedi Mathys, and Kathryn Roeder (2025). Causal differential expression analysis under unmeasured confounders with causarray. bioRxiv, 2025-01.