In this overview, we describe the statistical methodology for inferring cell type–specific directed gene regulatory networks from single-cell (multi-ome) eQTL datasets: what it can be used for, how it compares to related approaches, and its main advantages and limitations.
What is the methodology good for?
We have developed a variety of statistical methods that take advantage of single-cell (multi-ome) eQTL datasets to infer cell-type specific directed gene regulatory networks, that can be used to study how sets of genetic variants and/or genes cooperate together.
What is/are the main advantages of this methodology over related technologies?
Our methodology provides genome-wide results, enabling inference of directed networks for many gene-pairs at once. We also have observed excellent overlap with recent Perturb-seq datasets, when comparing activating and repressing effects.
What are the most important limitations of the methodology?
Currently methodology is limited to cell-types and tissues for which for thousands of samples there is genotype and gene expression data available.
What type of samples are compatible with methodology (yes, no, possibly)?
Cancer cell lines | Primary cells in culture | Organoids | Primary tissue |
Yes | Yes | Yes | Yes |
What future develops to the methodology are you planning, in any?
Incorporate single-cell spatial transcriptomics to study infer regulatory relationships across cells.
If someone outside your lab wants to use the methodology, what is the best option?
Either have bioinformatics skills or use user-friendly alternatives GeneNetwork.nl and eQTLGen.org.
Name one or more people in your lab that are experienced with the methodology
Robert Warmerdam
Marc Jan Bonder
Who originally developed the methodology?
GeneNetwork is developed by several members of the Lude Franke Group with papers currently in preparation describing.
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