SEDR Analyses

We tested SEDR on DLPFC dataset (12 slices) and compared it with 7 state-of-the art methods:

• BayesSpace
• Giotto
• stLearn
• SpaGCN
• Seurat
• DeepST
• STAGATE

We highly recommend you to organize the folder as shown below and download the data and scripts into the correct folder.

data folder structure

SEDR_analyses
├── 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
├── DLPFC_Seurat.R  
└── ...  

Download data

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.

Run state-of-the-art methods

• Rscript DLPFC_Seurat.R sample n_clusters
• Rscript DLPFC_Giotto.R sample n_clusters
• python DLPFC_stLearn.py sample
• python DLPFC_SpaGCN.py sample n_clusters
• Rscript DLPFC_BayesSpace.R sample n_clusters
• python DLPFC_DeepST.py sample n_clusters
• python DLPFC_STAGATE.py 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

Compare SEDR and other methods

For each sample, run following code

• Rscript DLPFC_comp.R sample

Summary of 12 slices

• Rscript DLPFC.ARI_boxplot.R

Stero-seq data

Stero-seq data in SEDR project is included in data folder.