run_slide.py

import pandas as pd
import numpy as np
import scanpy as sc
import matplotlib.pyplot as plt
import os
import sys
from sklearn.metrics.cluster import adjusted_rand_score
import SR_DGN
os.environ['R_HOME'] = '/home/dell/anaconda3/envs/stpython/lib/R'

adata = sc.read('Data/Mouse_hippocampus_Slide-seqV2/filtered_feature_bc_matrix_200115_08.h5ad')
adata.var_names_make_unique()

used_barcode = pd.read_csv('Data/Mouse_hippocampus_Slide-seqV2/used_barcodes.csv', sep=',', header=0, index_col=0)
used_barcode = used_barcode["barcodes"]
adata = adata[used_barcode,]

sc.pp.highly_variable_genes(adata, flavor="seurat_v3", n_top_genes=3000)
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.scale(adata, zero_center=False, max_value=10)

SR_DGN.Cal_Spatial_Net(adata, rad_cutoff=40)
SR_DGN.Stats_Spatial_Net(adata)
adata = SR_DGN.train(adata,device="cpu")

adata = SR_DGN.mclust_R(adata, used_obsm='SR-DGN', num_cluster=10)