dce - Pathway Enrichment Based on Differential Causal Effects
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.
Last updated 4 months ago
softwarestatisticalmethodgraphandnetworkregressiongeneexpressiondifferentialexpressionnetworkenrichmentnetworkkegg
10 stars 1.38 score 233 dependenciespareg - Pathway enrichment using a regularized regression approach
Compute pathway enrichment scores while accounting for term-term relations. This package uses a regularized multiple linear regression to regress differential expression p-values obtained from multi-condition experiments on a pathway membership matrix. By doing so, it is able to incorporate additional biological knowledge into the enrichment analysis and to estimate pathway enrichment scores more robustly.
Last updated 4 months ago
softwarestatisticalmethodgraphandnetworkregressiongeneexpressiondifferentialexpressionnetworkenrichmentnetwork
7 stars 1.16 score 198 dependencies