Bayesian networks for cell differentiation process assessment

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.

[1]  Alessandro Aiuti,et al.  Penalized inference of the hematopoietic cell differentiation network via high-dimensional clonal tracking , 2019, Applied Network Science.

[2]  L. Biasco,et al.  Dynamics of genetically engineered hematopoietic stem and progenitor cells after autologous transplantation in humans , 2018, Nature Medicine.

[3]  M. Stratton,et al.  Population dynamics of normal human blood inferred from somatic mutations , 2018, Nature.

[4]  C. von Kalle,et al.  In Vivo Tracking of Human Hematopoiesis Reveals Patterns of Clonal Dynamics during Early and Steady-State Reconstitution Phases , 2016, Cell stem cell.

[5]  Marloes H. Maathuis,et al.  Structure Learning in Graphical Modeling , 2016, 1606.02359.

[6]  Jan-Willem Romeijn,et al.  ‘All models are wrong...’: an introduction to model uncertainty , 2012 .

[7]  L. Biasco,et al.  Retroviral Integrations in Gene Therapy Trials , 2012, Molecular therapy : the journal of the American Society of Gene Therapy.

[8]  Igor Jurisica,et al.  Isolation of Single Human Hematopoietic Stem Cells Capable of Long-Term Multilineage Engraftment , 2011, Science.

[9]  L. Naldini.  Ex vivo gene transfer and correction for cell-based therapies , 2011, Nature Reviews Genetics.

[10]  T. Ikawa,et al.  A map for lineage restriction of progenitors during hematopoiesis: the essence of the myeloid‐based model , 2010, Immunological reviews.

[11]  M. Roncarolo,et al.  Hematopoietic stem cell gene therapy for adenosine deaminase deficient-SCID , 2009, Immunologic research.

[12]  Alexander Schliep,et al.  Inferring differentiation pathways from gene expression , 2008, ISMB.

[13]  Uffe Kjærulff,et al.  Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis , 2007, Information Science and Statistics.

[14]  R. Neapolitan.  Learning Bayesian networks , 2007, KDD '07.

[15]  Luca Biasco,et al.  Multilineage hematopoietic reconstitution without clonal selection in ADA-SCID patients treated with stem cell gene therapy. , 2007, The Journal of clinical investigation.

[16]  Tom Burr,et al.  Causation, Prediction, and Search , 2003, Technometrics.

[17]  Robert G. Cowell,et al.  Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models , 2001, UAI.

[18]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.

[19]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[20]  D. Geiger,et al.  Learning Bayesian networks: The combination of knowledge and statistical data , 1994, Machine Learning.

[21]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[22]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.