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

• URL: http://arxiv.org/abs/2501.10124v2
• Date: Tue, 04 Nov 2025 18:50:46 GMT
• Title: Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
• Authors: Gongxu Luo, Haoyue Dai, Loka Li, Chengqian Gao, Boyang Sun, Kun Zhang,
• Abstract summary: We show that selection is ubiquitous and, when ignored or conflated with true regulations, can lead to flawed causal interpretation and misguided intervention recommendations.<n>We propose GISL (Gene regulatory network Inference in the presence of Selection bias and Latent confounders), a principled algorithm that leverages perturbation data to uncover true gene regulatory relations and non-regulatory mechanisms of selection and confounding up to the equivalence class.
• Score: 16.010515254476626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gene regulatory network inference (GRNI) aims to discover how genes causally regulate each other from gene expression data. It is well-known that statistical dependencies in observed data do not necessarily imply causation, as spurious dependencies may arise from latent confounders, such as non-coding RNAs. Numerous GRNI methods have thus been proposed to address this confounding issue. However, dependencies may also result from selection--only cells satisfying certain survival or inclusion criteria are observed--while these selection-induced spurious dependencies are frequently overlooked in gene expression data analyses. In this work, we show that such selection is ubiquitous and, when ignored or conflated with true regulations, can lead to flawed causal interpretation and misguided intervention recommendations. To address this challenge, a fundamental question arises: can we distinguish dependencies due to regulation, confounding, and crucially, selection? We show that gene perturbations offer a simple yet effective answer: selection-induced dependencies are symmetric under perturbation, while those from regulation or confounding are not. Building on this motivation, we propose GISL (Gene regulatory network Inference in the presence of Selection bias and Latent confounders), a principled algorithm that leverages perturbation data to uncover both true gene regulatory relations and non-regulatory mechanisms of selection and confounding up to the equivalence class. Experiments on synthetic and real-world gene expression data demonstrate the effectiveness of our method.

Related papers

• Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction [48.80217316452559]
  scBIG is a module-inductive prediction framework that explicitly models coordinated gene programs.<n> scBIG consistently outperforms state-of-the-art methods, particularly on unseen and perturbation settings.
  arXiv  Detail & Related papers  (2026-02-03T16:43:40Z)
• Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data [16.805686863404077]
  Post-treatment selection can introduce spurious dependencies and distributional changes under interventions.<n>We introduce a novel causal formulation that explicitly models post-treatment selection and reveals how its differential reactions to interventions can distinguish causal relations from selection patterns.<n>We develop a provably sound and complete algorithm, F-FCI, to identify causal relations, latent confounders, and post-treatment selection up to $mathcalFI$-Markov equivalence.
  arXiv  Detail & Related papers  (2025-09-30T05:18:15Z)
• Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges [68.98973318553983]
  We propose a framework based on Dual Diffusion Implicit Bridges (DDIB) to learn the mapping between different data distributions.<n>We integrate gene regulatory network (GRN) information to propagate perturbation signals in a biologically meaningful way.<n>We also incorporate a masking mechanism to predict silent genes, improving the quality of generated profiles.
  arXiv  Detail & Related papers  (2025-06-26T09:05:38Z)
• Multi-omic Causal Discovery using Genotypes and Gene Expression [0.0]
  We introduce GENESIS, a constraint-based causal algorithm to infer ancestral relationships in transcriptomic data.<n>By integrating genotypes as fixed causal anchors, GENESIS provides a principled head start'' to classical causal discovery algorithms.<n>This framework offers a powerful avenue for uncovering causal pathways in complex traits, with promising applications to functional genomics, drug discovery, and precision medicine.
  arXiv  Detail & Related papers  (2025-05-21T11:52:23Z)
• GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype [51.58774936662233]
  Building gene regulatory networks (GRN) is essential to understand and predict the effects of genetic perturbations.<n>In this work, we leverage pre-trained large language model and DNA sequence model to extract features from gene descriptions and DNA sequence data.<n>We introduce gene biotype information for the first time in genetic perturbation, simulating the distinct roles of genes with different biotypes in regulating cellular processes.
  arXiv  Detail & Related papers  (2025-05-06T03:35:24Z)
• Learning to Discover Regulatory Elements for Gene Expression Prediction [59.470991831978516]
  Seq2Exp is a Sequence to Expression network designed to discover and extract regulatory elements that drive target gene expression. Our approach captures the causal relationship between epigenomic signals, DNA sequences and their associated regulatory elements.
  arXiv  Detail & Related papers  (2025-02-19T03:25:49Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters. Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks. It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution [9.919024883502322]
  Cross-Attention Complex Dual Graph Embedding Model (XATGRN)<n>Our model consistently outperforms existing state-of-the-art methods across various datasets.
  arXiv  Detail & Related papers  (2024-12-18T10:56:40Z)
• Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures [49.1574468325115]
  Causal Discovery is a technique that tackles the challenge by analyzing the statistical properties of the constituent variables. We question the current (possibly misleading) baseline results on the basis that they were obtained through supervised learning. In consequence, we approach this problem in an unsupervised way, using robust Mutual Information measures.
  arXiv  Detail & Related papers  (2024-08-01T09:11:08Z)
• Gene Regulatory Network Inference from Pre-trained Single-Cell Transcriptomics Transformer with Joint Graph Learning [10.44434676119443]
  Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is a complex challenge. In this study, we tackle this challenge by leveraging the single-cell BERT-based pre-trained transformer model (scBERT) We introduce a novel joint graph learning approach that combines the rich contextual representations learned by single-cell language models with the structured knowledge encoded in GRNs.
  arXiv  Detail & Related papers  (2024-07-25T16:42:08Z)
• Detecting and Identifying Selection Structure in Sequential Data [53.24493902162797]
  We argue that the selective inclusion of data points based on latent objectives is common in practical situations, such as music sequences. We show that selection structure is identifiable without any parametric assumptions or interventional experiments. We also propose a provably correct algorithm to detect and identify selection structures as well as other types of dependencies.
  arXiv  Detail & Related papers  (2024-06-29T20:56:34Z)
• Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification [119.13058298388101]
  We develop a Biological-knowledge enhanced PathGenomic multi-label Transformer to improve genetic mutation prediction performances. BPGT first establishes a novel gene encoder that constructs gene priors by two carefully designed modules. BPGT then designs a label decoder that finally performs genetic mutation prediction by two tailored modules.
  arXiv  Detail & Related papers  (2024-06-05T06:42:27Z)
• Identifying the Attractors of Gene Regulatory Networks from Expression Data under Uncertainty: An Interpretable Approach [0.0]
  Given a temporal gene expression profile of a real gene regulatory network, how can the attractors be robustly identified? This paper addresses this question using a novel approach based on Zadeh Computing with Words. The proposed scheme could effectively identify the attractors from temporal gene expression data in terms of both fuzzy logic-based and linguistic descriptions.
  arXiv  Detail & Related papers  (2024-03-16T20:56:22Z)
• GENER: A Parallel Layer Deep Learning Network To Detect Gene-Gene Interactions From Gene Expression Data [0.7660368798066375]
  We introduce a parallel-layer deep learning network designed exclusively for the identification of gene-gene relationships using gene expression data. Our model achieved an average AUROC score of 0.834 on the combined BioGRID&DREAM5 dataset, outperforming competing methods in predicting gene-gene interactions.
  arXiv  Detail & Related papers  (2023-10-05T15:45:53Z)
• Genetic heterogeneity analysis using genetic algorithm and network science [2.6166087473624318]
  Genome-wide association studies (GWAS) can identify disease susceptible genetic variables. Genetic variables intertwined with genetic effects often exhibit lower effect-size. This paper introduces a novel feature selection mechanism for GWAS, named Feature Co-selection Network (FCSNet)
  arXiv  Detail & Related papers  (2023-08-12T01:28:26Z)
• Nonlinear Permuted Granger Causality [0.6526824510982799]
  Granger causal inference is a contentious but widespread method used in fields ranging from economics to neuroscience. To allow for out-of-sample comparison, a measure of functional connectivity is explicitly defined using permutations of the covariate set. Performance of the permutation method is compared to penalized variable selection, naive replacement, and omission techniques via simulation.
  arXiv  Detail & Related papers  (2023-08-11T16:44:16Z)
• DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets [81.75973217676986]
  Gene regulatory networks (GRN) describe interactions between genes and their products that control gene expression and cellular function. Existing methods either focus on challenge (1), identifying cyclic structure from dynamics, or on challenge (2) learning complex Bayesian posteriors over DAGs, but not both. In this paper we leverage the fact that it is possible to estimate the "velocity" of gene expression with RNA velocity techniques to develop an approach that addresses both challenges.
  arXiv  Detail & Related papers  (2023-02-08T16:36:40Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data [61.088705993848606]
  We introduce CausalBench, a benchmark suite for evaluating causal inference methods on real-world interventional data. CaulBench incorporates biologically-motivated performance metrics, including new distribution-based interventional metrics.
  arXiv  Detail & Related papers  (2022-10-31T13:04:07Z)
• Granger causal inference on DAGs identifies genomic loci regulating transcription [77.58911272503771]
  GrID-Net is a framework based on graph neural networks with lagged message passing for Granger causal inference on DAG-structured systems. Our application is the analysis of single-cell multimodal data to identify genomic loci that mediate the regulation of specific genes.
  arXiv  Detail & Related papers  (2022-10-18T21:15:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.