Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders

• URL: http://arxiv.org/abs/2501.10124v1
• Date: Fri, 17 Jan 2025 11:27:58 GMT
• Title: Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
• Authors: Gongxu Luo, Haoyue Dai, Boyang Sun, Loka Li, Biwei Huang, Petar Stojanov, Kun Zhang,
• Abstract summary: Gene Regulatory Network Inference (GRNI) aims to identify causal relationships among genes using gene expression data. Gene expression is influenced by latent confounders, such as non-coding RNAs, which add complexity to GRNI. We propose GISL (Gene Regulatory Network Inference in the presence of Selection bias and Latent confounders) to infer true regulatory relationships in the presence of selection and confounding issues.
• Score: 14.626706466908386
• License:
• Abstract: Gene Regulatory Network Inference (GRNI) aims to identify causal relationships among genes using gene expression data, providing insights into regulatory mechanisms. A significant yet often overlooked challenge is selection bias, a process where only cells meeting specific criteria, such as gene expression thresholds, survive or are observed, distorting the true joint distribution of genes and thus biasing GRNI results. Furthermore, gene expression is influenced by latent confounders, such as non-coding RNAs, which add complexity to GRNI. To address these challenges, we propose GISL (Gene Regulatory Network Inference in the presence of Selection bias and Latent confounders), a novel algorithm to infer true regulatory relationships in the presence of selection and confounding issues. Leveraging data obtained via multiple gene perturbation experiments, we show that the true regulatory relationships, as well as selection processes and latent confounders can be partially identified without strong parametric models and under mild graphical assumptions. Experimental results on both synthetic and real-world single-cell gene expression datasets demonstrate the superiority of GISL over existing methods.

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