Scholarlink

<p>Single-cell RNA sequencing enables high-resolution analysis of cellular heterogeneity, yet disentangling biological signal from batch effects remains challenging. Existing batch-correction algorithms suppress or discard batch-related variation rather than modeling it. We propose scMEDAL—single-cell Mixed Effects Deep Autoencoder Learning—a framework that separately and independently models batch-invariant and batch-specific effects. The principal innovation, scMEDAL-RE, is a random-effects Bayesian autoencoder that learns batch-specific representations while preserving biologically meaningful information confounded with batch effects, signal often lost under standard correction. Across diverse conditions (autism, leukemia, cardiovascular), cell types, and technical and biological effects, scMEDAL-RE produces interpretable, batch-specific embeddings that complement multiple batch correction methods, improving prediction of disease status, donor group, and tissue. scMEDAL also provides generative visualizations—including counterfactual reconstructions of a cell’s expression as if acquired in another batch. Overall, scMEDAL is a versatile, interpretable framework that complements existing correction, providing insight into cellular heterogeneity and data acquisition.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

查看全文
  • Aixa X. Andrade,
  • Son N. Nguyen,
  • Austin Marckx,
  • Albert A. Montillo

摘要

Single-cell RNA sequencing enables high-resolution analysis of cellular heterogeneity, yet disentangling biological signal from batch effects remains challenging. Existing batch-correction algorithms suppress or discard batch-related variation rather than modeling it. We propose scMEDAL—single-cell Mixed Effects Deep Autoencoder Learning—a framework that separately and independently models batch-invariant and batch-specific effects. The principal innovation, scMEDAL-RE, is a random-effects Bayesian autoencoder that learns batch-specific representations while preserving biologically meaningful information confounded with batch effects, signal often lost under standard correction. Across diverse conditions (autism, leukemia, cardiovascular), cell types, and technical and biological effects, scMEDAL-RE produces interpretable, batch-specific embeddings that complement multiple batch correction methods, improving prediction of disease status, donor group, and tissue. scMEDAL also provides generative visualizations—including counterfactual reconstructions of a cell’s expression as if acquired in another batch. Overall, scMEDAL is a versatile, interpretable framework that complements existing correction, providing insight into cellular heterogeneity and data acquisition.