The way cell differentiate from bone marrow to peripheral blood level plays a crucial role in understanding and treating rare diseases and more common tumours. The main goal of this paper is to introduce a flexible statistical framework able to describe the cell differentiation process and to reconstruct a dependence structure along different levels of differentiation. We use next generation sequencing data on haematological diseases (severe combined immunodeficiency) within a gene therapy framework. The proposed statistical approach is based on Bayesian networks (BNs) and aims at finding a probabilistic model to describe the most important features of cell differentiation, without requiring specific detailed assumptions concerning the interactions among genes or the confounding effects of experimental conditions. Bayesian networks enable analyses on gene therapy‐treated patients in a data‐driven fashion and allow for exploring all relationships among different blood cell types integrating biological information, subject‐matter knowledge, and probabilistic principles.

中文翻译：

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贝叶斯网络用于细胞分化过程评估

细胞从骨髓分化为外周血的方式在理解和治疗罕见疾病和更常见的肿瘤中起着至关重要的作用。本文的主要目的是引入一个灵活的统计框架，该框架能够描述细胞分化过程并沿着不同的分化水平重建依赖性结构。我们在基因治疗框架内使用有关血液系统疾病（严重合并免疫缺陷）的下一代测序数据。拟议的统计方法基于贝叶斯网络（BNs），旨在找到一种概率模型来描述细胞分化的最重要特征，而无需关于基因之间的相互作用或实验条件的混杂影响的具体详细假设。