Scholarlink

<p>Time course single-cell RNA sequencing (scRNA-seq) enables researchers to probe expression dynamics at the resolution of individual cells. Analyzing this data poses several challenges, including deconvolving the contributions of time and cell type, discriminating true dynamics from batch effects, and inferring per-cell dynamics despite cells being destroyed at each time point. We present VIST (Variational Inference for Single cell Time series), a deep-learning algorithm that deconvolves single-cell time series into time-dependent and time-independent components. Using both synthetic and real scRNA-seq data, we show that VIST constructs biologically meaningful latent spaces, removes batch effects, and generates realistic single-cell time series.</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

VIST: variational inference for single cell time series

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月15日

查看全文
  • Bingxian Xu,
  • Rosemary Braun

摘要

Time course single-cell RNA sequencing (scRNA-seq) enables researchers to probe expression dynamics at the resolution of individual cells. Analyzing this data poses several challenges, including deconvolving the contributions of time and cell type, discriminating true dynamics from batch effects, and inferring per-cell dynamics despite cells being destroyed at each time point. We present VIST (Variational Inference for Single cell Time series), a deep-learning algorithm that deconvolves single-cell time series into time-dependent and time-independent components. Using both synthetic and real scRNA-seq data, we show that VIST constructs biologically meaningful latent spaces, removes batch effects, and generates realistic single-cell time series.