MERSCOPE
[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]:
adata_dict = sodb.load_dataset('Dataset13_MS_raw')
adata_raw = list(adata_dict.values())[0]
load experiment[Dataset13] in dataset[Dataset13_MS_raw]
[6]:
adata_raw
[6]:
AnnData object with n_obs × n_vars = 734696 × 483
obs: 'fov', 'volume', 'min_x', 'max_x', 'min_y', 'max_y', 'slice_id', 'batch', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_300_genes', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'n_counts', 'ct'
var: 'mt', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
uns: 'ct_colors', 'hvg', 'leiden', 'log1p', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
obsm: 'X_pca', 'X_umap', 'blank_genes', 'spatial'
varm: 'PCs'
obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances'
[7]:
adata = BINARY.clean_adata(adata_raw, save_obs=['slice_id'])
adata
[7]:
AnnData object with n_obs × n_vars = 734696 × 483
obs: 'slice_id'
obsm: 'spatial'
[8]:
import numpy as np
np.unique(adata.obs['slice_id'])
[8]:
array(['R1S1', 'R1S2', 'R1S3', 'R2S1', 'R2S2', 'R2S3', 'R3S1', 'R3S2',
'R3S3'], dtype=object)
[10]:
experiment_name = 'R3S2'
[11]:
adata = adata[adata.obs['slice_id']== experiment_name ]
adata
[11]:
View of AnnData object with n_obs × n_vars = 85958 × 483
obs: 'slice_id'
obsm: 'spatial'
[12]:
adata.var_names_make_unique()
[13]:
adata = BINARY.Count2Binary(adata)
[14]:
BINARY.Construct_Spatial_Graph(adata, use_method='KNN', cutoff=15)
------Constructing spatial graph...------
The graph contains 1289370 edges, 85958 cells.
15.0000 neighbors per cell on average.
[15]:
adata = BINARY.train_BINARY(adata, pos_weight = 10, device= 'cuda:0')
Size of Input: (85958, 483)
100%|██████████| 1000/1000 [01:43<00:00, 9.62it/s]
[16]:
sc.pp.neighbors(adata, use_rep='BINARY')
sc.tl.umap(adata)
[21]:
sc.tl.leiden(adata, resolution= 1)
[22]:
ax = sc.pl.embedding(adata, basis='spatial', color= 'leiden', show= False)
ax.axis('equal')
[22]:
(51.18393756430595, 10113.721657756902, -141.07202616399155, 7537.619496310782)
