This repository contains key scripts and analyses accompanying the EpiDamID manuscript: bioRxiv
All raw data in the manuscript was processed using the workflow described in Markodimitraki, Rang et al., 2020, available at https://github.com/KindLab/scDamAndTools .
The following jupyter notebooks with analyses are included:
- LDA_classifier.applied_to_EB_data: Contains the analyses to train an LDA to predict membership to transcriptional clusters based on the single-cell DamID readout.
The following code is included:
- enrichment_over_regions.py: Custom code to compute average signal over regions of interest. Loosely based on the DeepTools functionalities (https://deeptools.readthedocs.io/en/develop/).
- InformationContent.py: Custom code to compute the Information Content of DamID samples.
Additional code is available upon request.