Harnessing naturally randomized transcription to infer regulatory relationships among genes

By Lin S. Chen, Frank Emmert-Streib, John D. Storey. 2007

143 citations recorded by the source corpus.

We develop an approach utilizing randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level, based on experiments in which genotyping and expression profiling are performed. This approach can be used to build transcriptional regulatory networks and to identify putative regulators of genes. We apply the method to an experiment in yeast, in which genes known to be in the same processes and functions are recovered in the resulting transcriptional regulatory network.

Subfields: Molecular Biology

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Citations in the corpus

Causal Inference through a Witness Protection Program (2014, methodology): “This simple procedure for identifying chains W → X → Y is useful in exploratory data analysis [4], where a large number of possible causal relations X → Y are unquantified but can be screened using observational data before experiments are performed.”
Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression (2016, methodology): “Similar search strategies have previously been considered to identify mediating genes; see for example [20].”
Data-driven covariate selection for nonparametric estimation of causal effects (2013, background): “Another open problem is how to incorporate further background knowledge, such as that of VanderWeele and Shpitser (2011) (having information on which variables are causes of x or y), or of Spirtes and Cooper (1999), and Chen et al. (2007) (knowing that certain variables are exogenous).”
Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example (2014, methodology): “The model we are interested in arises from the work of Cooper [1], who proposed the “LCD” algorithm for causal discovery in observational databases and the more recent paper of Chen et al.[8], who proposed the “Trigger” algorithm to infer transcriptional regulatory networks among genes.”
Joint Causal Inference from Multiple Contexts (2016, methodology): “l all data and construct a single context-independent causal graph directly from the pooled data (Cooper, 1997; Cooper and Yoo, 1999; Tian and Pearl, 2001; Sachs et al., 2005; Eaton and Murphy, 2007; Chen et al., 2007; Hauser and Buhlmann, 2012; Mooij and Heskes, 2013; Peters et al., 2016; Oates et ”