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 [Du26], 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:

# 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:

pip install causarray

For optimal parallel performance, we recommend installing llvm-openmp if using conda:

conda install -c conda-forge llvm-openmp

For R users, reticulate can be used to call causarray from R while keeping NumPy >2 in the Python environment. Create the separate R environment with a current NumPy-2-compatible reticulate build:

conda env create -f environment-r.yaml

The R tutorial runs from causarray-r and connects to the Python package in the causarray environment. The documentation and tutorials using both Python and R are available at causarray.readthedocs.io.

Tutorials

Batch fitting API

For screens with hundreds to thousands of perturbations, use gcate_lfc_batch so that peak memory is bounded by one batch at a time:

from causarray import gcate_lfc_batch

df_res = gcate_lfc_batch(
    Y, X, A, r,
    batch_size=10,    # perturbations per batch (or use n_batches= for a fixed count)
    max_cells=2000,   # max pert cells per batch (ctrl added on top)
    n_ctrl=2000,      # fixed ctrl subsample shared across batches
    cache_path='results.h5',   # resume if interrupted
    verbose=True,
)

See the Replogle-E-K562 tutorial for a demonstration on 200 perturbations from a genome-wide CRISPRi screen.

Diagnostic masks without refitting effects

Treatment-by-gene support or quality-control rules can be aligned to an existing causarray result table by label, even when its rows are reordered:

from causarray import align_test_mask

keep = align_test_mask(
    df_res,
    support_mask,                 # treatments × genes, Boolean
    treatment_names=perturbations,
    gene_names=genes,
)
df_res_flagged = df_res.assign(support_keep=keep)

This operation only annotates or subsets existing results. It does not refit the causarray LFC or change standard errors and p-values. If a diagnostic rule was selected after inspecting the outcomes, retain the original adjusted p-values rather than redefining the multiple-testing family post hoc. The Replogle tutorial compares several expression-support rules with marginal Wilcoxon results.

Changelog

See CHANGELOG for a full version history.

References

[Du26] Jin-Hong Du, Maya Shen, Hansruedi Mathys, and Kathryn Roeder. "Uncovering causal relationships in single cell omic studies with causarray". In: Briefings in Bioinformatics (2026).

[Gabitto24] Mariano I. Gabitto et al. "Integrated multimodal cell atlas of Alzheimer's disease". In: Nature Neuroscience (2024).

[Jin20] Xin Jin et al. "In vivo Perturb-seq reveals neuronal and glial abnormalities associated with autism risk genes". In: Nature Neuroscience (2020).

[Replogle22] Joseph M. Replogle et al. "Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq". In: Cell (2022).