This repository contains the code for reproducing simulation and real data analysis results of the paper "Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes".

• causarray: The main module for the proposed causal inference method.

• ex1: Simulation with Poisson DGP with sample splitting
  • generate_simu_data.py: Utility function that generates simulated data.
  • run_simu.py: Run simulation.
  • simu.ipynb: Plot the simulation results.
• ex2: LUHMES CRISPR data
  • LUHMES.ipynb: LUHMES data analysis.
• ex3: Lupus data
  • Lupus.ipynb: Lupus data analysis.

The following Python packages are required for the reproducibility workflow.

For simulation studies, the workflow is as follows:

• Run script run_simu.py to perform simulation. The script takes two arguments: the simulation scenarios (0 for mean shifts and 1 for median shifts) and the number of folds (1 for no splitting and 5 for 5-fold cross-fitting). The results will be stored in the folder result/simu/.
• Use simu.ipynb to reproduce the figures (Figures 1 and E1-E2) based on the previous results.

For real data analysis on LUHMES data, the workflow is as follows:

• (Optional) Preprocessing
  • Store the raw data file as the folder data/LUHMES/GSE142078_RAW from the original paper [1].
  • Store the supplemental code as the folder data/LUHMES/Supplemental_Code from the original paper [1].
  • Run data/LUHMES/preprocess_data.R to preprocess the LUHMES data, and the resulting data will be stored as data/LUHMES/LUHMES.h5, which is available in the current project folder.
• Use LUHMES.ipynb to run the proposed causal inference method and reproduce the figures (Figures 3-4 and G3-G7) and tables (Tables E1-E2).

For real data analysis on Lupus data, the workflow is as follows:

• Obtain the h5ad file of the lupus data from the authors of the original paper [2] and store it in the folder data/lupus/GSE174188_CLUES1_adjusted.h5ad.
• Run ex4_preprocess_lupus.py of the GitHub repo of paper [3] to preprocess the lupus data.
• Use Lupus.ipynb to run the proposed causal inference method and reproduce the figures (Figures 5-6, G3-G7, and E3).

• [1] Lalli, M. A., Avey, D., Dougherty, J. D., Milbrandt, J., & Mitra, R. D. (2020). High-throughput single-cell functional elucidation of neurodevelopmental disease–associated genes reveals convergent mechanisms altering neuronal differentiation. Genome research, 30(9), 1317-1331.
• [2] Perez, Richard K., et al. "Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus." Science 376.6589 (2022): eabf1970.
• [3] Du, Jin-Hong, Larry Wasserman, and Kathryn Roeder. "Simultaneous inference for generalized linear models with unmeasured confounders." arXiv preprint arXiv:2309.07261 (2023).