PASTE alignment tutorial

import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
import scanpy as sc
import paste as pst
import os.path as osp
import glob
from utils.function_utils import *

import ot
ot.backend.get_backend_list()

import torch
torch.cuda.is_available()

iters = 1
save_dir_gt = './paste_result'

Mouse Hypothalamus data integration (pair-wise)

"""mhypo"""
section_ids_list = [['-0.04', '-0.09'], ['-0.09', '-0.14'], ['-0.14', '-0.19'], ['-0.19', '-0.24']]
run_times = []
for section_ids in section_ids_list:
    dataset = section_ids[0] + '_' + section_ids[1]
    output = '.'
    slice1 = load_mHypothalamus(section_id=section_ids[0])
    slice2 = load_mHypothalamus(section_id=section_ids[1])

    # run paste pairwise alignment
    pi0 = pst.match_spots_using_spatial_heuristic(slice1.obsm['spatial'],slice2.obsm['spatial'],use_ot=True)
    pi12 = pst.pairwise_align(slice1, slice2, G_init=pi0, norm=True, backend = ot.backend.TorchBackend(), use_gpu=True)

    # save alignment matrix
    if not os.path.exists(os.path.join(save_dir_gt, dataset)):
        os.makedirs(os.path.join(save_dir_gt, dataset))
    np.save(osp.join(save_dir_gt, dataset, 'iter'+str(0)+'embedding.npy'), pi12)

DLPFC data integration (pair-wise)

section_ids_list = [['151507', '151508'], ['151508', '151509'], ['151509', '151510']]
run_times = []

for section_ids in section_ids_list:
    dataset = section_ids[0] + '_' + section_ids[1]
    slice1 = load_DLPFC(section_id=section_ids[0])
    slice2 = load_DLPFC(section_id=section_ids[1])


    # run paste pairwise alignment
    pi0 = pst.match_spots_using_spatial_heuristic(slice1.obsm['spatial'],slice2.obsm['spatial'],use_ot=True)
    pi12 = pst.pairwise_align(slice1, slice2, G_init=pi0, norm=True, backend = ot.backend.TorchBackend(), use_gpu=True)

    # save alignment matrix
    if not os.path.exists(os.path.join(save_dir_gt, dataset)):
        os.makedirs(os.path.join(save_dir_gt, dataset))
    np.save(osp.join(save_dir_gt, dataset, 'iter'+str(0)+'embedding.npy'), pi12)