This is the GitHub repository describing the scRNA-seq analysis carried out for the publication: "Inducible stem cell-derived embryos capture mouse morphogenetic events in vitro", Amadei et al. Developmental Cell 2020 (Figure 7 and Supplementary Figure 7). Please visit the Gene Expression Omnibus GSE161947 entry to find raw and processed datasets. The Natural embryo at E4.5 dataset can be found at the Gene Expression Omnibus GSE134240 from a previous submission.
1. Pre-processing the data
Note: All the steps can be concatenated in master scripts if automation is needed, the files presented here are intended to be showcased for each step independently.
a) Generating the STAR index
The Mus musculus STAR indexing was ran using the run_STAR_indexer.sh script.
b) bcl2fastq conversion
The BCL files were converted fastq files using the bcl2fastq.sh script.
c) De-multiplexing using the Pheniqs tool from biosails/pheniqs
The run_pheniqs.sh script was used to de-multiplex the fastq files according the run_pheniqs.json json file. The data posted on GSE161947 is already de-multiplexed and corresponds to the data obtained after this step.
d) Running zUMIs to produce count matrices
The zUMIs pipeline was ran using the script run_zumis.sh for each of the samples. To convert the resulting produced dgecounts.rds files to txt matrices counting both intronic and exonic UMI counts using the extract_inex_matrix.R file. The Ensembl names were then converted to gene names using the convert_names.ipynb script.
2. Data analysis
a) Filtering the data in scanpy
Scanpy was used to preprocess the data, along with the Scrublet package for doublet removal. This step eliminates the noise indtroduced by empty droplets, dead cells and cell doublets mainly and can be found in the Jupyter notebook iETX_filter_the_data.ipynb.
b) Plotting the data
The scripts for plotting from the h5ad object is the iETX_plotting.ipynb jupyter notebook.
c) Perform differential gene expression (DGE) analysis with Seurat
The script for performing pairwise DGE analysis can be found in the iETX_DGE_Seurat.R file. It requires a conversion of the scanpy raw adata matrix to a Seurat compatible matrix with the following command: pd.DataFrame(data=adata.raw.X.A, index=adata.obs_names, columns=adata.var_names).T.to_csv("iETX_cat_R.csv").
