CCAdge is an R package for detecting differenital genes associated with phenotype from single cell profiles. This tool is developed and maintained by Ken chen's lab in MDACC. There is a pressing need for computatinal tools to enable the detection of genes associated with phenotypes in single-cell level. This task is very challening in single-cell studies due to the existence of: 1) changes in cell type composition among samples and 2) unknow technological batch effects. The proposed high-order gene detection method first leverages the canonical correlation analysis (CCA) to align cell populations from two sample into shared space (a), and then detects gene changed in each latent space (b). The gene difference in latent spaces are finally aggregated into the population-level mismatching score, enabling to capture associated gene due to subtle cell state change (c-d).
- Release test version.
The package requires only a standard computer with enough RAM to support the in-memory operations. For minimal performance, please make sure that the computer has at least about 10 GB of RAM. For optimal performance, we recommend a computer with the following specs:
- RAM: 10+ GB
- CPU: 4+ cores, 2.3 GHz/core
- Seurat (>=V3.0)
- R library: irlba, umap, rdist, EnhancedVolcano, gtools, ggpubr
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
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More to be added ...
This project is covered under the GNU General Public License 3.0.