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

Transcriptional regulatory network inference methods have been studied foryears. Most of them relie on complex mathematical and algorithmic concepts,making them hard to adapt, re-implement or integrate with other methods. Toaddress this problem, we introduce a novel method based on a minimalstatistical model for observing transcriptional regulatory interactions innoisy expression data, which assumes that transcription factors (TFs) and theirtargets are both differentially expressed in a gene-specific, critical samplecontrast, as measured by repeated two-way t-tests. This method is conceptuallysimple and easy to implement and integrate in any statistical softwareenvironment. Benchmarking on standard E. coli and yeast reference datasetsshowed that it performs equally well as the best existing methods. Analysis ofthe predicted interactions suggested that it works best to infercontext-specific TF-target interactions which only co-express locally. Weconfirmed this hypothesis on a dataset of more than 1,000 normal human tissuesamples, where we found that our method predicts highly tissue-specific andfunctionally relevant interactions, whereas a global co-expression method onlyassociates general TFs to non-specific biological processes.

Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests