domains / families / science / grn-inference

grn-inference

separate

Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.

When to use

• inferring a regulatory network from a bulk expression matrix
• finding master regulators
• scoring TF activity from a signature
• reverse-engineer TF -> target edges
• assemble regulons
• score the protein activity of each TF
• compare TF activity between conditions
• ensemble multiple inferences

When not to use

• single-cell motif-pruned regulons
• co-expression modules

Skill page

Source