Transfer learning for spatial transcriptomics data and single-cell RNA-seq data.
Project description
stTransfer
Installation
pip install stTransfer
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")
st.distribution_fine_tune(st_adata_with_pslabel,
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
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