English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Background: Inference of gene regulatory networks from transcriptomic datahas been a wide research area in recent years. Proposed methods are mainlybased on the use of graphical Gaussian models for observational wild-type dataand provide undirected graphs that are not able to accurately highlight thecausal relationships among genes. In the present work, we seek to improveestimation of causal effects among genes by jointly modeling observationaltranscriptomic data with intervention data obtained by performing knock-outs orknock-downs on a subset of genes. By examining the impact of such expressionperturbations on other genes, a more accurate reflection of regulatoryrelationships may be obtained than through the use of wild-type data alone.Results: Using the framework of Gaussian Bayesian networks, we propose a Markovchain Monte Carlo algorithm with a Mallows model and an analytical likelihoodmaximization to sample from the posterior distribution of causal nodeorderings, and in turn, to estimate causal effects. The main advantage of theproposed algorithm over previously proposed methods is that it has theflexibility to accommodate any kind of intervention design, including partialor multiple knock-out experiments. Methods were compared on simulated data aswell as data from the DREAM 2007 challenge. Conclusions: The simulation studyconfirmed the impossibility of estimating causal orderings of genes withobservation data only. The proposed algorithm was found, in most cases, toperform better than the previously proposed methods in terms of accuracy forthe estimation of causal effects. In addition, multiple knock-outs proved tobring valuable additional information compared to single knock-outs. The choiceof optimal intervention design therefore appears to be a crucial aspect forcausal inference and an interesting challenge for future research.

Joint estimation of causal effects from observational and intervention gene expression data