Single-cell sequencing (SCS) is an emerging technology in the across the diverse array of biological fields. Part of the struggle with the high-resolution approach of SCS, is distilling the data down to meaningful scientific hypotheses. escape was created to bridge SCS results, either from raw counts or from the popular Seurat R package, with gene set enrichment analyses (GSEA), allowing users to simply and easily graph outputs. The package accesses the entire Molecular Signature Database v7.0 and enables users to select single, multiple gene sets, and even libraries to perform enrichment analysis on.
- grDevices
- dplyr
- ggplot2
- GSEABase
- GSVA
- SingleCellExperiment
- ggridges
- msigdbr
- BiocParallel
- Matrix
- UCell
- broom
- reshape2
- patchwork
- MatrixGenerics
devtools::install_github("ncborcherding/escape")
devtools::install_github("ncborcherding/escape@dev")
Vignette available here, includes 2,000 malignant and nonmalignant peripheral blood T cells from a patient with cutaenous T cell lymphoma.
If using escape, please cite the article: Borcherding, N., Vishwakarma, A., Voigt, A.P. et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Commun Biol 4, 122 (2021). https://doi.org/10.1038/s42003-020-01625-6.
Questions, comments, suggestions, please feel free to contact Nick Borcherding via this repository, email, or using twitter.