SingleEffect

datasets

Detecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms or epistatic interactions, especially high-order epistatic interactions, are important topics in bioinformatics because of their significant mathematical and computational challenges. We present CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer) for detecting, visualizing, and analyzing high-order epistatic interactions by introducing virtual vertices into the construction of a hypergraph. CINOEDV was developed as an alternative to existing software to build a global picture of epistatic interactions and unexpected high-order epistatic interactions, which might provide useful clues for understanding the underlying genetic architecture of complex diseases.

Junliang Shang

CINOEDV

Co-Information based N-Order Epistasis Detector and Visualizer

SingleEffect function

The format is:
 chr [1:20, 1:2] "rs00013" "rs00010" "rs00006" "rs00001" ...
 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:2] "SNP" "Value"

SingleEffect: Main Effects of SNPs

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Examples