Causal differential expression analysis under unmeasured confounders with causarray.

Area of Science:

• Genomics
• Computational Biology
• Causal Inference

Background:

• Single-cell sequencing and CRISPR technologies offer high-resolution biological insights.
• Analyzing observational genomic data for causal relationships is hindered by bias and unmeasured confounders, especially in complex, heterogeneous datasets.

Purpose of the Study:

• To introduce causarray, a robust causal inference framework for genomic data analysis.
• To address challenges in identifying causal effects in both bulk and single-cell genomic data.
• To improve the accuracy of treatment effect estimation in the presence of unmeasured confounders.

Main Methods:

• Developed causarray, a doubly robust causal inference framework for array-based genomic data.
• Integrated a generalized confounder adjustment method to handle unmeasured confounders.
• Employed semiparametric inference and machine learning for robust statistical estimation.

Main Results:

• Causarray effectively separates treatment effects from confounders while preserving biological signals across various data types.
• Applied to single-cell Perturb-seq data for autism risk genes, causarray identified clustered causal effects.
• Analysis of Alzheimer's disease transcriptomic data revealed consistently affected genes and relevant pathways.

Conclusions:

• Causarray provides a robust method for causal inference in genomic studies, enhancing the analysis of complex diseases.
• The framework successfully identified key genes and pathways involved in neuronal development and synaptic function relevant to autism and Alzheimer's disease.
• Causarray offers a powerful tool for dissecting complex biological systems and uncovering disease mechanisms at single-cell resolution.