scOVAR_SE_Screen

1. Cell Ranger was run for the initial processing of the two samples (both scRNA-seq and scATAC-seq). Visit https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome for more details on the CellRanger workflow.

scRNA-seq processing

1. Run ovar_3BAE2L_RNA-4.R to process the first tumor sample
2. Run ovar_3E5CFL_RNA-4.R to process the second tumor sample
3. Run ovar_HGSOC_RNA.R to merge the two scRNA-seq datasets and reprocess

scATAC-seq processing

1. Run HGSOC_ATAC.R to process the scATAC-seq data for both samples

Figure making

1. Run HGSOC_Differential_Genes_And_Peaks.R to generate the figure visuals
2. Run HGSOC-Write_Enhancer_Coords.R to write cancer-enriched enhancer bed files for input into motif analysis
3. Run Motif_Analysis_SE60_SE14.sh to perform the motif analysis
4. Run FIMO_TF_rank.R to process the motif analysis results

Quality control histograms

Run HGSOC_QC_Samples.R to generate histrograms of log2(nCount_RNA) in scRNA-seq and log2(unique fragments) in scATAC-seq

Overlap peaks called in the malignant fraction with Super Enhancer (SE) regions

1. Run HGSOC_SE_Overlap. R to generate a bed file of peaks called in the malignant fraction (subset to clusters: "0-Epithelial cell","2-Epithelial cell","3-Epithelial cell","7-Epithelial cell","11-Epithelial cell",and "16-Epithelial cell")
2. Run Intersect_SE_regions.sh to find SE regions that overlap with peaks called in the malignant fraction

NOTE: Similar analyses were conducted at https://github.com/RegnerM2015/scENDO_scOVAR_2020.