Here is the analysis code for SEDR project.
We tested SEDR on DLPFC dataset (12 slices) and compared it with 5 state-of-the art methods:
To run analyses code properly, we recommend you to organize working folder as shown below and download the scripts into the folder.
SEDR
├── data
│ └── DLPFC
│ └── 151507
│ ├── filtered_feature_bc_matrix.h5
│ ├── metadata.tsv
│ └── spatial
│ ├── scalefactors_json.json
│ ├── tissue_positions_list.csv
│ ├── full_image.tif
│ ├── tissue_hires_image.png
│ └── tissue_lowres_image.png
├── output
│ └── DLPFC
│ └── 151507
└── SEDR_analyses
├── DLPFC_Seurat.R
└── ...
DLPFC data can be downloaded from SpatialLIBD.
Extract and put data within data/DLPFC folder.
Please notice that the scale_factors_json.json and tissue_positions_list.csv can be found in 10X folder in SpatialLIBD.
Besides, the metadata.tsv we used in SEDR is consistant with BayesSpace.
For convenient, we have put three files within data folder here. You need to move the data folder to where we recommend.
- Follow the instructions in SEDR to run SEDR for 12 slices.
- Move the results to output/DLPFC/sample.name/SEDR for further comparison.
- Rscript DLPFC_Seurat.R sample n_clusters
- python DLPFC_stLearn.py sample
- python DLPFC_SpaGCN.py sample n_clusters
- Rscript DLPFC_BayesSpace.R sample n_clusters
- Rscript DLPFC_Giotto.R sample n_clusters
Table of n_clsuters:
Sample_ID | n_clusters |
---|---|
151507 | 7 |
151508 | 7 |
151509 | 7 |
151510 | 7 |
151669 | 5 |
151670 | 5 |
151671 | 5 |
151672 | 5 |
151673 | 7 |
151674 | 7 |
151675 | 7 |
151676 | 7 |
- Rscript DLPFC_comp.R sample
- Rscript DLPFC.ARI_boxplot.R
Stero-seq data in SEDR project is included in data folder.