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<p>Chromatin state annotations based on epigenetic marks are powerful resources, but many additional samples have gene expression data available while lacking epigenetic mark data and chromatin state annotations. Using the EpiATLAS resource of the International Human Epigenome Consortium (IHEC), we develop Gene Expression-based Chromatin State Imputation (GECSI), which uses an ensemble of multi-class logistic regression classifiers to predict chromatin state annotations from gene expression data. We show in cross-validation that GECSI accurately predicts observed chromatin state annotations. We also apply GECSI using gene expression data in 449 IHEC additional samples, providing an expanded chromatin state annotation resource.</p>

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GECSI: large-scale chromatin state imputation from gene expression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Jingyuan Fu,
  • Jason Ernst

摘要

Chromatin state annotations based on epigenetic marks are powerful resources, but many additional samples have gene expression data available while lacking epigenetic mark data and chromatin state annotations. Using the EpiATLAS resource of the International Human Epigenome Consortium (IHEC), we develop Gene Expression-based Chromatin State Imputation (GECSI), which uses an ensemble of multi-class logistic regression classifiers to predict chromatin state annotations from gene expression data. We show in cross-validation that GECSI accurately predicts observed chromatin state annotations. We also apply GECSI using gene expression data in 449 IHEC additional samples, providing an expanded chromatin state annotation resource.