# Human hepatocellular carcinoma

The HCC ST data can be download {bdg-link-primary}`here <http://lifeome.net/supp/livercancer-st/data.htm>`.


## Import package

```python
import numpy as np
import pandas as pd
import scanpy as sc

from sklearn import mixture
from STMiner.SPFinder import SPFinder
from STMiner.Algorithm.distance import compare_gmm_distance
```

## Load data

```python
data = sc.read_10x_h5("I://HCC-5A/filtered_feature_bc_matrix.h5")  # Replace with your h5 file path
position=pd.read_csv("I://HCC-5A/spatial/tissue_positions_list.csv", header=None, index_col=0) # Replace with your tissue_positions_list.csv file path
position.columns = ['in_tissue','x','y','px','py']
data.obs = pd.merge(data.obs, position, left_index=True, right_index=True)
sc.pp.filter_genes(data, min_cells=50)
hcc = SPFinder(data) # Load anndata to STMiner
```

## Get patterns of interested gene set

STMiner allows to input the genes or gene sets of interest and calculated the distance between all genes and the given gene/genes.

```python
imm_genes =  ['CCL2','CCL3','CCL4','CCL5','CCL8','CCL18','CCL19','CCL21','CXCL9','CXCL10','CXCL11','CXCL13']
hcc.get_pattern_of_given_genes(gene_list=imm_genes)
```

## Cmpare all genes with interested gene set

```python
hcc.fit_pattern(n_comp=20) # Fit patterns of all genes
df = compare_gmm_distance(hcc.custom_pattern, hcc.patterns) # Compare the distance between all genes and the given gene set
```