PreciCE

A unified workflow for data-driven precision cell fate engineering via highly multiplexed gene control

Installation

Using pip:

Ensure you have Python and pip installed on your system. Then run the following command:

pip install -r model/requirements.txt

Using conda:

Ensure you have Anaconda or Miniconda installed on your system. Create a new conda environment (optional but recommended):

conda create --name cell_reprogram_env
conda activate cell_reprogram_env

Install the dependencies:

conda install --file model/requirements.txt

Sample workflow

Worfklow for processing new dataset and running the model

Step 1. Initialize PreciCE data processing workflow and preprocess dataset

workflow = precice(adata=adata,
                   dir='./workflow_dir', 
                   path='./hesc_dataset.h5ad')

Step 2. Computing differential expression

## Add cell type transition information
workflow.set_transition(label_map, colname='celltype')
workflow.save_seurat()

## Run differential expression externally in R or internally in Python
workflow.get_DE(DE='../Data/DE/DE_hesc_dataset.csv')

Step 3. Network inference: Either load pre-existing network

workflow.get_network(cell_type='embryonic stem cell')

Or infer network and edge weights

## Set up PySCENIC for given dataset
workflow.set_up_pyscenic(species)

## Run PySCENIC (Takes several hours)
workflow.run_pyscenic()

## Post processing of learnt transcriptional network
workflow.learn_weights()

Step 4. Run PrecICE to identify optimal transcription factor perturbations

transition = source_name +'_to_' + target_name
workflow.run_precice(species='human',
                     network_path=self.network_path,
                     DE_path=workflow.DE_filenames[transition])

Step 5. Plot results

## Plot ranked list of perturbations and relative perturbation magnitude
workflow.perturbation_plot(k=15)

## Plot transcriptional network with applied perturbations
workflow.network_plot()