Metadata-Version: 2.1
Name: stTransfer
Version: 1.0.4
Summary: Transfer learning for spatial transcriptomics data and single-cell RNA-seq data.
Home-page: https://github.com/zepoch/stTransfer.git
Author: zhoutao
Author-email: zhotoa@foxmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: aiohttp >=3.9.3

# stTransfer

[![python >= 3.10](https://img.shields.io/badge/python-3.10-brightgreen)](https://www.python.org/) 

### Installation      
```python
pip install stTransfer
```

```python
import stTransfer as st
st.dnn_workflow(data_path = '/data/input/single.h5ad',
                ann_key = 'celltype', # celltype in adata.obs
                marker_genes=None, # marker genes list
                batch_size=4096, # train batch size
                epochs=200, # train epochs
                gpu="0", # gpu id
                model_name="dnn.bgi", # model name
                model_path="/data/model", # model path
                filter_mt=False, # filter mitochondrial genes or not
                cell_min_counts=300, # min counts per cell
                gene_min_cells=10, # min cells per gene
                cell_max_counts=98.) # max counts per cell

st_adata = st.load_data(data_path = '/data/input/st_adata.h5ad', # obsm.['spatial'] is required
                        filter_mt=True, 
                        min_cells=10, 
                        min_counts=300, 
                        max_percent=98.0) # load data

st_adata_with_pslabel = st.transfer_from_sc_data(adata = st_adata, # adata with obsm.['spatial']
                                                 dnn_path = '/data/model/dnn.bgi', # dnn model path
                                                 gpu="0")

distribution_fine_tune(adata, 
                       pca_dim=200, 
                       k_graph=30, 
                       edge_weight=True, 
                       epochs=200, 
                       w_cls=20, 
                       w_dae=1., 
                       w_gae=1.,
                       gpu="0", 
                       save_path="/data//output") # output path
```
