TFA inference with perturbation data

Code and data for:
Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data
Ma & Brent, 2020 (Bioinformatics)

NetworkConstruction - create the TF-target gene regulatory networks from ChIP, PWM, and DE data
TFA_Optimization - optimize TFA and CS values using a network and gene expression data (new added input datasets)
evaluateTFA.py - run evaluation metrics on inferred TFA values

TFA inference with perturbation data

Includes the necessary code and input files to replicate Fig2A of
Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data
Ma & Brent, 2020

NetworkConstruction - create the TF-target gene regulatory networks from ChIP, PWM, and DE data
TFA_Optimization - optimize TFA and CS values using a network and gene expression data
evaluateTFA.py - run evaluation metrics on inferred TFA values

TFA-evaluation

This release includes code for evaluating TFA inference results as described in the paper
Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data, Ma and Brent, 2020

Example input files are included to replicate results shown in Figure 2A