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The scEPS software package implements the following functionalities:
- Estimate scEPS statistics at each individual cell neighborhood
- Cluster cell neighborhoods into approximately independent blocks
- Aggregate scEPS statistics for groups of cell neighborhoods
- Calculate the correlation between scEPS statistics and gene expression
We provided detaile documentations for each of these functionalities on the relevant wiki page (see right hand side). Below we provide a general overview of a scEPS workflow.
Step 0a. Obtain gene-level MAGMA association statistics, using misc/run_magma.sh
Step 0b. Preprocess single-cell RNA-seq data, using misc/preprocess_scdata.py
Step 1. Estimate scEPS sceps.py
Step 2. Cluster cell neighborhoods into approximately independent blocks for statistical block bootstrap, using sceps_cluster_neighborhood.py
Step 3. Aggregate scEPS statistics at individual cell neighborhoods into cell types, using sceps_aggregate.py
Step 4. (optional) Calculate the correlation between scEPS statistics and gene expression, using sceps_corr.py
We recommend using the following directory structure for a typical scEPS analysis.
DISEASE/
├── step_0a/
│ ├── out/
│ └── run_magma.sh
├── step_0b/
│ ├── out/
│ └── run_preprocess.sh
├── step1/
│ ├── out/
│ └── run_step1.sh
├── step2/
│ ├── out/
│ └── run_step2.sh
├── step3/
│ ├── out/
│ └── step3.sh
├─── step4/
│ ├── step4_out/
│ └── run_step4.sh
If the user chooses to perform separate scEPS analysis for each cell type in the single cell data during step 1, the user may organize the analysis as follows:
DISEASE/
├── step_0a/
│ ├── out/
│ └── run_magma.sh
├── step_0b/
│ ├── out/
│ └── run_preprocess.sh
├── step1/
│ ├── out_celltype1/
│ ├── out_celltype2/
│ ├── out_celltype3/
│ ├── run_step1_celltype1.sh
│ ├── run_step1_celltype2.sh
│ └── run_step1_celltype3.sh
├── step2/
│ ├── out/
│ └── run_step2.sh
├── step3/
│ ├── out/
│ └── step3.sh
├─── step4/
│ ├── step4_out/
│ └── run_step4.sh