scvi-tools

النتيجة المتوقعة:

```python
import scvi
import scanpy as sc

# Preprocess data
adata = sc.read_h5ad("my_single_cell_data.h5ad")
sc.pp.filter_genes(adata, min_counts=3)

# Register data with batch information
scvi.model.SCVI.setup_anndata(
adata,
layer="counts",
batch_key="batch"
)

# Train model
model = scvi.model.SCVI(adata)
model.train()

# Get batch-corrected latent representation
latent = model.get_latent_representation()
adata.obsm["X_scVI"] = latent

# Downstream analysis
sc.pp.neighbors(adata, use_rep="X_scVI")
sc.tl.umap(adata)
sc.tl.leiden(adata)
```

النتيجة المتوقعة:

```python
# Differential expression between two groups
de_results = model.differential_expression(
groupby="leiden",
group1="0", # Cluster 0
group2="1", # Cluster 1
mode="change",
delta=0.25 # Minimum effect size
)

# View top differentially expressed genes
print(de_results.head(20))

# Filter for significant genes
significant_genes = de_results[
(de_results['is_de_fdr_0.05']) &
(de_results['bayes_factor'] > 1)
]
print(f"Found {len(significant_genes)} differentially expressed genes")
```