Scholarlink

<p>Cell differentiation is a fundamental biological process, where cells progress through different stages of maturation to become specialised cell types. Understanding this process is important, and has led to the development of various mathematical models to represent cell behaviour during maturation. Advancements in these models are owed to researchers’ ability to obtain high-throughput genome-scale single-cell gene expression data, which has revolutionised our understanding of complex processes like haematopoiesis. Here we introduce BAGEL: <b>B</b>ayesian <b>A</b>nalysis of <b>G</b>ene <b>E</b>xpression <b>L</b>ineages, a novel statistical model. BAGEL offers new insights into cell differentiation through (i) a robust Bayesian inference approach that models cell differentiation as a continuous process; and (ii) a powerful projection method that enables visualisation and investigation of similarities and differences between intra- and inter-species single-cell gene expression datasets. The ability of BAGEL’s projection method to harness the collective power of multiple datasets is enormous as it can potentially accelerate and enhance our understanding of cell differentiation. Although this manuscript focuses on haematopoiesis, BAGEL should apply to various single-cell gene expression datasets, providing a deeper understanding of cellular complexity.</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

Bayesian inference of haematopoietic stem/progenitor cell differentiation phenotypic manifolds and their bifurcation points using Gaussian processes and Gibbs sampling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

查看全文
  • R. D. Dowling,
  • J. Mellet,
  • E. Wolmarans,
  • C. Durandt,
  • F. Joubert,
  • J. P. de Villiers,
  • M. S. Pepper

摘要

Cell differentiation is a fundamental biological process, where cells progress through different stages of maturation to become specialised cell types. Understanding this process is important, and has led to the development of various mathematical models to represent cell behaviour during maturation. Advancements in these models are owed to researchers’ ability to obtain high-throughput genome-scale single-cell gene expression data, which has revolutionised our understanding of complex processes like haematopoiesis. Here we introduce BAGEL: Bayesian Analysis of Gene Expression Lineages, a novel statistical model. BAGEL offers new insights into cell differentiation through (i) a robust Bayesian inference approach that models cell differentiation as a continuous process; and (ii) a powerful projection method that enables visualisation and investigation of similarities and differences between intra- and inter-species single-cell gene expression datasets. The ability of BAGEL’s projection method to harness the collective power of multiple datasets is enormous as it can potentially accelerate and enhance our understanding of cell differentiation. Although this manuscript focuses on haematopoiesis, BAGEL should apply to various single-cell gene expression datasets, providing a deeper understanding of cellular complexity.