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New framework integrates single-cell data for causal gene pathway analysis

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework to analyze causal pathways in gene regulation by integrating single-cell perturbation experiments with population-scale single-cell studies. This method uses externally learned ancestral relationships to constrain network topology and re-estimates edge effects from population data. The framework also incorporates a surrogate-variable procedure to address multiscale confounding and measurement error, offering theoretical guarantees for confounder recovery and network estimation. An application to acute myeloid leukemia data identified distinct regulatory pathways linked to blast count. AI

IMPACT This framework could advance biological research by enabling more accurate causal inference from complex biological data.

RANK_REASON The item is a research paper published on arXiv detailing a new methodological framework. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework integrates single-cell data for causal gene pathway analysis

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The item is a research paper published on arXiv detailing a new methodological framework. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Kwangmoon Park, Hongzhe Li ·

    Causal Path Analysis from Perturbational and Population-Scale Single-Cell Data with Multiscale Confounding and Measurement Error

    arXiv:2609.16510v1 Announce Type: cross Abstract: Single-cell perturbation experiments provide causal information on gene regulation, whereas population-scale single-cell studies characterize gene expression and phenotypes in human populations. We develop a framework that integra…

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