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<p>Single-cell bisulfite sequencing (scBS-seq) has enabled the study of methylomic heterogeneity in complex tissues at an unprecedented resolution. Yet, fully leveraging these rich datasets poses a substantial challenge, as genuine epigenetic variability is confounded by a unique set of technical factors (for example, sparse coverage of cytosines). Here we present methylation variational inference (MethylVI), a probabilistic modelling framework for scBS-seq that explicitly disentangles meaningful biological signals from confounding nuisance variations. A key strength of our model is its flexibility: MethylVI supports multiple core analysis tasks out-of-the-box, including dimensionality reduction, integration of data from different scBS-seq protocols and identification of differentially methylated genes. Moreover, our implementation readily integrates with other single-cell analysis tools, which we demonstrate by extending MethylVI using previously proposed frameworks for single-cell reference atlas mapping and multiomic modelling.</p>

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Probabilistic modelling of single-cell bisulfite sequencing data with MethylVI

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Ethan Weinberger,
  • Wei Qiu,
  • Wei Tian,
  • Qiurui Zeng,
  • Can Ergen,
  • Ori Kronfeld,
  • Martin Kim,
  • Nir Yosef,
  • Joseph R. Ecker,
  • Su-In Lee

摘要

Single-cell bisulfite sequencing (scBS-seq) has enabled the study of methylomic heterogeneity in complex tissues at an unprecedented resolution. Yet, fully leveraging these rich datasets poses a substantial challenge, as genuine epigenetic variability is confounded by a unique set of technical factors (for example, sparse coverage of cytosines). Here we present methylation variational inference (MethylVI), a probabilistic modelling framework for scBS-seq that explicitly disentangles meaningful biological signals from confounding nuisance variations. A key strength of our model is its flexibility: MethylVI supports multiple core analysis tasks out-of-the-box, including dimensionality reduction, integration of data from different scBS-seq protocols and identification of differentially methylated genes. Moreover, our implementation readily integrates with other single-cell analysis tools, which we demonstrate by extending MethylVI using previously proposed frameworks for single-cell reference atlas mapping and multiomic modelling.