Sparse inference of the human hematopoietic system from heterogeneous and partially observed genomic data

TL;DR

This paper introduces a novel graphical model and an efficient inference algorithm to analyze complex regulatory networks in hematopoiesis from high-dimensional, heterogeneous, and partially observed genomic data, revealing key biological insights.

Contribution

It develops a sparse, shared network inference method tailored for high-dimensional, partially observed genomic data, implemented in the R package cglasso.

Findings

Identified regulatory networks driving red blood cell and platelet formation.

Demonstrated the method's ability to handle high-dimensional, incomplete data.

Revealed associations between membrane receptors and nuclear factors.

Abstract

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.