Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
arxiv(2024)
摘要
With the evolution of single-cell RNA sequencing techniques into a standard
approach in genomics, it has become possible to conduct cohort-level causal
inferences based on single-cell-level measurements. However, the individual
gene expression levels of interest are not directly observable; instead, only
repeated proxy measurements from each individual's cells are available,
providing a derived outcome to estimate the underlying outcome for each of many
genes. In this paper, we propose a generic semiparametric inference framework
for doubly robust estimation with multiple derived outcomes, which also
encompasses the usual setting of multiple outcomes when the response of each
unit is available. To reliably quantify the causal effects of heterogeneous
outcomes, we specialize the analysis to the standardized average treatment
effects and the quantile treatment effects. Through this, we demonstrate the
use of the semiparametric inferential results for doubly robust estimators
derived from both Von Mises expansions and estimating equations. A multiple
testing procedure based on the Gaussian multiplier bootstrap is tailored for
doubly robust estimators to control the false discovery exceedance rate.
Applications in single-cell CRISPR perturbation analysis and individual-level
differential expression analysis demonstrate the utility of the proposed
methods and offer insights into the usage of different estimands for causal
inference in genomics.
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