Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression

This repository contains scripts for data processing, analysis and figure generation using scRNA-Seq, bulk RNA-seq and ChIP-seq data for our paper:

Cuomo*, Seaton*, McCarthy* et al. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression, Nature Communications, 2020.

Analysis scripts

The following folders contain scripts for data processing and analysis.

A short description can be found below:

Preprocessing steps contains snakemake files to process the sequencing data (including alignment, donor assignment etc.).
QC and merging steps contains jupyter notebooks to merge experiment-level SCE objects and perform QC and normalization steps to obtain the final SCE object used for all following analyses.
Plotting Notebooks contains all jupyter notebooks to reproduce the individual figures (main and supplements).

Data availability

All HipSci data can be accessed from http://www.hipsci.org.

Bulk RNA-seq

Bulk RNA-seq data are available under accession numbers: ERP007111 (ENA project) and EGAS00001001137, EGAS00001000593 (EGA projects).

Single cell RNA-seq

Single cell RNA-seq data are available under the accession numbers ERP016000 (ENA project) and EGAS00001002278, EGAD00001005741(EGA project: study ID, dataset ID).

ChIP-seq

All Chip-seq data used is available at PRJNA593217.

Processed Data

Processed and raw single cell count data, metadata, as well as donor-level allele-specific expression (ASE) data are available at this Zenodo link.

Note that raw counts are not integer numbers due to feature quantification being performed using Salmon.

See details of preprocessing steps in the Snakefile provided.