Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG.
|Bioconductor views||DifferentialExpression GeneExpression GraphAndNetwork KEGG Network NetworkEnrichment Regression Software StatisticalMethod|
|Package repository||View on GitHub|
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