  Hematopoiesis is the process of blood cell formation, through which
progenitor stem cells differentiate into mature forms, such as white and red
blood cells or mature platelets. While the precursors of the mature forms share
many regulatory pathways involving common cellular nuclear factors, specific
networks of regulation shape their fate towards one lineage or another. In this
study, we aim to analyse the complex regulatory network that drives the
formation of mature red blood cells and platelets from their common precursor.
To this aim, we develop a dedicated graphical model which we infer from the
latest RT-qPCR genomic data. The model also accounts for the effect of external
genomic data. A computationally efficient Expectation-Maximization algorithm
allows regularised network inference from the high-dimensional and often only
partially observed RT-qPCR data. A careful combination of alternating direction
method of multipliers algorithms allows achieving sparsity in the individual
lineage networks and a high sharing between these networks, together with the
detection of the associations between the membrane-bound receptors and the
nuclear factors. The approach will be implemented in the R package cglasso and
can be used in similar applications where network inference is conducted from
high-dimensional, heterogeneous and partially observed data.
