FateCompass

FateCompass is a flexible pipeline to estimate dynamic stochastic trajectories of gene expression and transcription factor activities during cell-fate decision using single-cell RNA seq data. We combined a probabilistic framework with RNA velocities (La Manno et al. 2018 and Bergen et al. 2020) or a differentiation potential to estimate transition probabilities and perform stochastic simulations. Also, we implemented a linear model of gene regulation (Balwierz et al.2014) to estimate transcription factor activities. Taking into account dynamic changes and correlations, we identified lineage-specific regulators. The FateCompass pipeline consist of three main steps summarized in the figure below:

Table of Contents

• Publication
• Tutorial
• Reproducibility

Publication

(Back to top)

Please check our publication Jimenez et al. 2023 to learn more.

Tutorials

(Back to top)

FateCompass is fully implemented in python. The main input of FateCompass are: (i) a pre-processed data object (adata), with annotations of at least cell types (‘clusters’) and optionally the RNA velocity field, and (ii) a TFs binding sites matrix, where the columns are the TFs or motifs and the rows are the expressed genes. We suggest performing basic pre-processing using Scanpy and scVelo.

To be able to run FateCompass, we suggest to create a conda environment with all the dependencies using the provided .yml file.

conda env create -f fatecompass.yml

To run jupyter notebooks please install jupyter in the environment

conda install notebook

The FateCompass module is provided in the fatecompass.py file.

We provide three main tutorials as documented jupyter notebooks:

1. fatecompass_basics_rna_velocity.ipynb
2. fatecompass_basics_differentiation_potential.ipynb
3. fatecompass_sanity_network.ipynb

The following flowchart illustrates the input and output of each one of the steps of the FateCompass pipeline.

Reproducibility

(Back to top)

The FateCompass methodology was tested with two datasets of pancreatic endocrine cell differentiation. Scripts to reproduce the results are available in the reproducibility folder and are implemented in Matlab. There are two scenarios: (i) RNA velocity driven trajectories and (ii) Differentiation potential driven trajectories.

In general, the scripts should be run in the following order:

1. differentiation_trajectories.m
2. tf_activities.m
3. bootstrapping.m
4. differential_motif_activity_analysis.m

FateCompass results for these datasets are summarized for easy visualization in the following Shiny App: https://jimenezs.shinyapps.io/FateCompass_results/.