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.
We recommend using causarray in a conda environment:
# create a new conda environment and install the necessary packages
conda create -n causarray python=3.12 -y
# activate the environment
conda activate causarrayThe module can be installed via PyPI:
pip install causarrayFor 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.
- (2025-01-30) Python package released on PyPI
- (2025-02-01) code for reproducing figures in paper
- (2025-02-02) Tutorial for Python and R
- Documentation
[Du25] Jin-Hong Du, Maya Shen, Hansruedi Mathys, and Kathryn Roeder (2025). Causal differential expression analysis under unmeasured confounders with causarray. bioRxiv, 2025-01.