StemnesScoRe: An R package to estimate the stemness of glioma cancer cells at single- cell resolution

We then identified promoter and enhancer regions associated with these genes. After downloading the scATAC-seq peaks and their read counts for each patient, we identified the overlapping regions between the single-cell peaks and the peaks of genes obtained through machine-learning algorithms. Then, we calculated read counts that were mapped to these overlapping regions.

We finally developed a model capable of estimating the stemness score for each glioma cell using overlapping regions and the importance of genes predictive of glioblastoma cellular states. We also created an R package, accessible to all researchers, regardless of their coding proficiency. Results: Our results showed that mesenchymal-like stem cells display the highest stemness scores compared to neural-progenitor, oligodendrocyte-progenitor, and astrocyte-like cells.

Conclusion: We conclude that scATAC-seq can be used to assess heterogeneity in glioblastoma and identify cells with high stemness characteristics. The package is publicly available at github.com/Necla/StemnesScoRe and includes documentation with an implementation of a real-data experiment.