EPEE

Effectors and Perturbation Estimation Engine (EPEE) a sparse linear model with graph constrained lasso regularization for differential analysis of RNA-seq data. The inputs are transcriptomic data for the two conditions under comparison, and context-specific TF-gene networks. If transcriptomic data is sequencing based, then data needs to be normalized to either TPM/FPKM/RPKM. EPEE is implemented in Python, using TensorFlow.

Citation

Viren Amin, Didem Agac, Spencer D Barnes, Murat Can Cobanoglu, "Accurate differential analysis of transcription factor activity from gene expression", Bioinformatics, May 2019.

https://doi.org/10.1093/bioinformatics/btz398

Inputs

• conditionA.txt and conditionB.txt

EPEE requires expression data matrix for the two conditions. Input is a tab delimited file in which columns are the samples and rows are the genes. First column of the file needs to be gene names. Please do not log normalize the dataset before running EPEE. EPEE log transforms the data to log(TPM/FPKM/RPKM + 1).

• networkA and networkB

EPEE requires context specific networks. Currently EPEE supports 426 context-specific networks published by Marbach et al. Nature Methods 2016.

Setup

1. Install Anaconda

2. Download the networks and example data to run EPEE.

3. Clone the git repository and set up the conda environment to run EPEE. We provided the environment files in env directory. If your machine has GPU card then we recommend that you use epee_GPU.txt file to create environment, otherwise create environment using epee_CPU.txt. To create conda environment use following command

conda env create -f epee_CPU.txt -n epee

Activate the new environment: Windows: activate epee, macOS and Linux: source activate epee

4. View the available human networks, and determine the network appropriate for your context.

5. Usage to run EPEE

python run_epee.py -a <conditionA.txt>
                   -b <conditionB.txt>
                   -na <networkA.txt>
                   -nb <networkB.txt>
                   -o <output_directory>

Example

CD4 Naive vs Th2 differential analysis

python run_epee_v0.1.4.3.py -a ../data/rnaseq/immune/CD4_Naive.txt.gz
                            -b ../data/rnaseq/immune/CD4_Th2.txt.gz
                            -na ../data/network/cd4+_t_cells.txt.gz
                            -nb ../data/network/cd4+_t_cells.txt.gz
                            -o /path/to/output_directory/
                            -prefix Th2

Normal Colon vs Colorectal Adenocarcinoma (COAD) differential analysis

python run_epee_v0.1.4.3.py -a ../data/rnaseq/tcga/TCGA_COAD_SolidTissueNormal_FPKM_UQ.txt
                            -b ../data/rnaseq/tcga/TCGA_COAD_PrimaryTumor_FPKM_UQ.txt
                            -na ../data/network/20_gastrointestinal_system.txt.gz
                            -nb ../data/network/20_gastrointestinal_system.txt/gz
                            -o /path/to/output_directory/
                            -prefix COAD