Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
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 standardized average treatment effects and 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 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine
Large Language Models (LLMs) show promise in biomedicine but lack true causal understanding, relying instead on correlations. This paper envisions causal LLM agents that integrate multimodal data (text, images, genomics,…
Causal InferenceDrug DiscoveryLanguage ModelingLanguage Modelling+1Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast
Causal gene networks model the flow of information within a cell, but reconstructing them from omics data is challenging because correlation does not imply causation. Combining genomics and transcriptomics data from a se…
Causal InferenceLearning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-task Learning Approach
Translating the high-dimensional data generated by genomic platforms into reliable predictions of clinical outcomes remains a critical challenge in realizing the promise of genomic medicine largely due to small number of…
Multi-Task LearningRepresentation LearningGranger causal inference on DAGs identifies genomic loci regulating transcription
When a dynamical system can be modeled as a sequence of observations, Granger causality is a powerful approach for detecting predictive interactions between its variables. However, traditional Granger causal inference ha…
Causal InferenceTRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data
Motivation: We introduce TRONCO (TRanslational ONCOlogy), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRO…