Tested on macOS
- R
- R packages:
- viridis
- RColorBrewer
- gplots
- Matrix
- Matrix.utils
- seriation
- scDissector
- Downloaded and unzipped version of this repository on a local path.
- Assuming martin_et_al_cell_2019 is the local path of the repository we need to load the script files:
source("martin_et_al_cell_2019/scripts/figures_main.R")
- And then make figures 1-5 and supplumentary figures:
make_martin_et_al_figures("martin_et_al_cell_2019/",download_data = F)
Figure will be generated in:
- martin_et_al_cell_2019/output/main_figures/
- martin_et_al_cell_2019/output/supp_figures/
Tables will be generated in:
- martin_et_al_cell_2019/output/tables/
Tested on linux LSF HPC. Due to lack of support of some of the depdendencies, the script cannot run on macOS.
- R
- R packages:
- Matrix
- Matrix.utils
- gplots
- seriation
- tglkmeans
- scDissector
- Downloaded and unzipped version of this repository on a local path.
Assuming martin_et_al_cell_2019 is the local path of the repository, the following script will run the clustering distributedly on LSF:
source("martin_et_al_cell_2019/scripts/clustering/run_clustering_ileum.r")
Alternatively, clustering can be run locally:
source("martin_et_al_cell_2019/scripts/clustering/run_clustering_ileum_local.r")
Note: Each run of the clustering might produce slightly different results due to different random seeds.