yeast_causal_regulation — Yeast causal gene regulation probabilities (2007)
- Description
A directed network of inferred causal regulatory relationships among the genes of the budding yeast Saccharomyces cerevisiae, from Chen, Emmert-Streib and Storey. The relationships were inferred with the "Trigger" algorithm from genome-wide expression measurements and genotypes of 112 recombinant segregants of a cross between the laboratory strain BY4716 and the wild isolate RM11-1a: since the segregants inherit the parental alleles at random, a locus $L$ linked to the expression of two genes $i$ and $j$ can be used as a natural randomization to test whether $L \to i \to j$, i.e. whether the expression of gene $i$ causally affects that of gene $j$. Nodes are the 6,216 genes (open reading frames) assayed by the microarrays, and a directed edge $i \to j$ has
weightequal to the estimated probability that gene $i$ regulates gene $j$ (the regulatory probability matrix $P_{ij}$ of the authors). Only nonzero probabilities (rounded to three decimals in the source) are included as edges; the authors build sparse networks by thresholding the probabilities, e.g. at $P_{ij} \geq 0.9$ (about 4,400 edges, with an estimated false discovery rate of 6%).The network has the following properties:
name: the standard gene name, or the systematic ORF name if there is none.weight: the estimated probability of the causal regulatory relationship.
- Tags
- Biological Gene regulation Weighted Uncertain
- Citation
-
- L. S. Chen, F. Emmert-Streib, J. D. Storey, "Harnessing naturally randomized transcription to infer regulatory relationships among genes." Genome Biology 8(10), R219 (2007) 10.1186/gb-2007-8-10-r219 Sci-Hub
- Upstream URL OK
-
https://genomebiology.biomedcentral.com/articles/10.1186/gb-2007-8-10-r219
https://europepmc.org/article/PMC/PMC2246293 - Upstream license
- Creative Commons Attribution 2.0 (CC BY 2.0), https://creativecommons.org/licenses/by/2.0/
- Networks
-
Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties