Slide-seq
[1]:
import warnings
warnings.filterwarnings("ignore")
[2]:
import pandas as pd
import numpy as np
import scanpy as sc
import matplotlib.pyplot as plt
[3]:
import BINARY
[4]:
import pysodb
sodb = pysodb.SODB()
[5]:
# Define names of the dataset_name and experiment_name
dataset_name = 'stickels2020highly'
experiment_name = 'stickels2021highly_Slide-seqV2_Mouse_Hippocampus_Puck_200115_08'
# Load a specific experiment
# It takes two arguments: the name of the dataset and the name of the experiment to load.
# Two arguments are available at https://gene.ai.tencent.com/SpatialOmics/.
adata_raw = sodb.load_experiment(dataset_name,experiment_name)
load experiment[stickels2021highly_Slide-seqV2_Mouse_Hippocampus_Puck_200115_08] in dataset[stickels2020highly]
[6]:
adata_raw
[6]:
AnnData object with n_obs × n_vars = 53208 × 23264
obs: 'leiden'
var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
uns: 'hvg', 'leiden', 'leiden_colors', 'log1p', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
obsm: 'X_pca', 'X_umap', 'spatial'
varm: 'PCs'
obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances'
[7]:
adata = BINARY.clean_adata(adata_raw)
adata
[7]:
AnnData object with n_obs × n_vars = 53208 × 23264
obsm: 'spatial'
[8]:
adata.var_names_make_unique()
[9]:
adata = BINARY.Count2Binary(adata)
[11]:
BINARY.Construct_Spatial_Graph(adata,
use_method='KNN',
cutoff=23)
------Constructing spatial graph...------
The graph contains 1223784 edges, 53208 cells.
23.0000 neighbors per cell on average.
[12]:
adata = BINARY.train_BINARY(adata, pos_weight= 10, device= 'cuda:0')
Size of Input: (53208, 3000)
100%|██████████| 1000/1000 [02:33<00:00, 6.53it/s]
[13]:
sc.pp.neighbors(adata, use_rep='BINARY')
sc.tl.umap(adata)
[14]:
optimal_resolution = BINARY.find_optimal_resolution(adata, desired_clusters=10, algorithm="leiden", add_key=None, start=0, end=5, step=1, max_iter=50)
The optimal resolution is: 0.224609375
Stored in adata.obs[leiden]: leiden
[15]:
sc.tl.leiden(adata, resolution= optimal_resolution)
[21]:
ax = sc.pl.embedding(adata, basis='spatial', color='leiden', size = 2, show=False)
ax.axis('equal')
[21]:
(-119.124, 5960.444, -125.922, 6031.482)
