@@ -161,43 +161,46 @@ Seurat* -- Needs to be implemented
### Method Comparsion with Redeconve & Seurat (Human Breast)
**Note**: We used Human Breast dataset. The default hyperparameters is $ratio=0.8$, $k=1000$, $m=100$, $n_{genes}$ is the overlap with marker genes. After scGPT generation, there are **14981** intersected genes between the spatial and single cell expression data with**166** marker genes.
**Note**: We used Human Breast dataset. The default hyperparameters is $ratio=0.8$, $k=1000$, $m=100$, $n_{genes}$ is the overlap with marker genes. Notice that there exist genes with 0's accorss all 1000 selected cells (in Redeconve method). Therefore Tangram requires to filter those zero-expression gene out and resulting an overlapped**165** marker genes and **14655** overlapped genes in total.
| Methods | Median | Mean |
|----------------|-----------|-------|
| Ours (1000) | 0.738423 | 0.690996 |
| Tangram (1000) | 0.497457 | 0.473931 |
| Redeconve | 0.458208 | 0.453385 |
| Seurat | \ | \|
| Ours (Full) | 0.929573 | 0.869638 |
| Tangram (Full) | 0.540444 | 0.528668 |
| Method | Median | Mean |
|-------------------|----------|----------|
| Ours (1000) | 0.738423 | 0.690996 |
| Tangram (1000) | 0.497457 | 0.473931 |
| Redeconve | 0.458208 | 0.453385 |
| Seurat | \ | \ |
| Ours (Full) | 0.929573 | 0.869638 |
| Tangram (Full) | 0.540444 | 0.528668 |
### Method Comparsion with Redeconve & Seurat (Human Lymph Node)
**Note**: We used Human Lymph Node dataset. The default hyperparameters is $ratio=0.8$, $k=730$, $m=100$, $n_{genes}$ is the overlap with marker genes.
**Note**: We used Human Lymph Node dataset. The default hyperparameters is $ratio=0.8$, $k=730$, $m=100$, $n_{genes}$ is the overlap with marker genes. After scGPT generation and filtering, there are **13515** intersected genes between the spatial and single cell expression data with **114** overlapped marker genes.
| Method | Median | Mean |
|-------------------|----------|----------|
| Ours (1000) | 0.845477 | 0.768616 |
| Tangram (1000) | 0.533852 | 0.541026 |
| Redeconve | 0.549875 | 0.546411 |
| Seurat | \ | \ |
| Ours (Full) | 0.929626 | 0.904360 |
| Tangram (Full) | 0.590144 | 0.601816 |
| Methods | Median | Mean |
|------------|---------|---------|
| Ours (1000) | 0.814423 | 0.749012 |
| Tangram (1000) | 0.535075 | 0.535446 |
| Redeconve | 0.548433 | 0.545180 |
| Seurat | \ | \ |
| Ours (Full) | 0.931290 | 0.906363 |
| Tangram (Full) | 0.558952 | 0.569556 |
### Method Comparsion with Redeconve & Seurat (PDAC)
**Note**: We used PDAC dataset. The default hyperparameters is $ratio=0.8$, $k=107$, $m=18$, $n_{genes}$ is the overlap with marker genes.
**Note**: We used PDAC dataset. The default hyperparameters is $ratio=0.8$, $k=107$, $m=18$, $n_{genes}$ is the overlap with marker genes. After scGPT generation and filtering, there are **11960** intersected genes between the spatial and single cell expression data with **127** overlapped marker genes.
