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evel_cscc.py
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evel_cscc.py
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import anndata
import torch
import torch.nn.functional as F
from scipy.stats import pearsonr
from tqdm import tqdm
from model import mclSTExp_Attention
from dataset import SKIN
from torch.utils.data import DataLoader
import os
import numpy as np
from train import generate_args
def build_loaders_inference():
datasets = []
for i in range(12):
dataset = SKIN(train=False, fold=i)
print(dataset.id2name[0])
datasets.append(dataset)
dataset = torch.utils.data.ConcatDataset(datasets)
test_loader = DataLoader(dataset, batch_size=32, shuffle=False, num_workers=0)
print("Finished building loaders")
return test_loader
def get_embeddings(model_path, model):
test_loader = build_loaders_inference()
state_dict = torch.load(model_path)
new_state_dict = {}
for key in state_dict.keys():
new_key = key.replace('module.', '') # remove the prefix 'module.'
new_key = new_key.replace('well', 'spot') # for compatibility with prior naming
new_state_dict[new_key] = state_dict[key]
model.load_state_dict(new_state_dict)
model.eval()
model = model.to('cuda')
print("Finished loading model")
test_image_embeddings = []
spot_embeddings = []
with torch.no_grad():
for batch in tqdm(test_loader):
image_features = model.image_encoder(batch["image"].cuda())
image_embeddings = model.image_projection(image_features)
test_image_embeddings.append(image_embeddings)
spot_feature = batch["expression"].cuda()
x = batch["position"][:, 0].long().cuda()
y = batch["position"][:, 1].long().cuda()
centers_x = model.x_embed(x)
centers_y = model.y_embed(y)
spot_feature = spot_feature + centers_x + centers_y
# coordinates = batch["position"].float().cuda()
# scale = max(coordinates[:, 0].max() - coordinates[:, 0].min(),
# coordinates[:, 1].max() - coordinates[:, 1].min())
# coordinates[:, 0] = (coordinates[:, 0] - coordinates[:, 0].min()) / scale
# coordinates[:, 1] = (coordinates[:, 1] - coordinates[:, 1].min()) / scale
# pe = model.pe_enc(coordinates)
# pe = model.act(pe)
# pe = model.layer_norm(pe)
# spot_feature = spot_feature + pe
spot_features = spot_feature.unsqueeze(dim=0)
spot_embedding = model.spot_encoder(spot_features)
spot_embedding = model.spot_projection(spot_embedding).squeeze(dim=0)
spot_embeddings.append(spot_embedding)
return torch.cat(test_image_embeddings), torch.cat(spot_embeddings)
def find_matches(spot_embeddings, query_embeddings, top_k=1):
# find the closest matches
spot_embeddings = torch.tensor(spot_embeddings)
query_embeddings = torch.tensor(query_embeddings)
query_embeddings = F.normalize(query_embeddings, p=2, dim=-1)
spot_embeddings = F.normalize(spot_embeddings, p=2, dim=-1)
dot_similarity = query_embeddings @ spot_embeddings.T
print(dot_similarity.shape)
values, indices = torch.topk(dot_similarity.squeeze(0), k=top_k)
return values.cpu().numpy(), indices.cpu().numpy()
def save_embeddings(model_path, save_path, datasize):
args = generate_args()
model = mclSTExp_Attention(encoder_name=args.encoder_name,
spot_dim=args.dim,
temperature=args.temperature,
image_dim=args.image_embedding_dim,
projection_dim=args.projection_dim,
heads_num=args.heads_num,
heads_dim=args.heads_dim,
head_layers=args.heads_layers,
dropout=args.dropout)
img_embeddings_all, spot_embeddings_all = get_embeddings(model_path, model)
img_embeddings_all = img_embeddings_all.cpu().numpy()
spot_embeddings_all = spot_embeddings_all.cpu().numpy()
# print("img_embeddings_all.shape", img_embeddings_all.shape)
# print("spot_embeddings_all.shape", spot_embeddings_all.shape)
if not os.path.exists(save_path):
os.makedirs(save_path)
for i in range(len(datasize)):
index_start = sum(datasize[:i])
index_end = sum(datasize[:i + 1])
image_embeddings = img_embeddings_all[index_start:index_end]
spot_embeddings = spot_embeddings_all[index_start:index_end]
print("image_embeddings.shape", image_embeddings.shape)
print("spot_embeddings.shape", spot_embeddings.shape)
np.save(save_path + "img_embeddings_" + str(i + 1) + ".npy", image_embeddings.T)
np.save(save_path + "spot_embeddings_" + str(i + 1) + ".npy", spot_embeddings.T)
SAVE_EMBEDDINGS = False
patients = ['P2', 'P5', 'P9', 'P10']
reps = ['rep1', 'rep2', 'rep3']
names = []
for i in patients:
for j in reps:
names.append(i + '_ST_' + j)
datasize = [666, 646, 638, 590, 521, 521, 1145, 1071, 1182, 608, 621, 462]
if SAVE_EMBEDDINGS:
for fold in range(12):
save_embeddings(model_path=f"model_result/cscc/{names[fold]}/best_{fold}.pt",
save_path=f"./model_result/cscc_result/embeddings_{fold}/",
datasize=datasize, dim=171, fold=fold)
spot_expressions = [np.load(f"./data/preprocessed_expression_matrices/cscc_data/{samp}/preprocessed_matrix.npy")
for samp in names]
patients = ['P2', 'P5', 'P9', 'P10']
reps = ['rep1', 'rep2', 'rep3']
names = []
for i in patients:
for j in reps:
names.append(i + '_ST_' + j)
def get_R(data1, data2, dim=1, func=pearsonr):
adata1 = data1.X
adata2 = data2.X
r1, p1 = [], []
for g in range(data1.shape[dim]):
if dim == 1:
r, pv = func(adata1[:, g], adata2[:, g])
elif dim == 0:
r, pv = func(adata1[g, :], adata2[g, :])
r1.append(r)
p1.append(pv)
r1 = np.array(r1)
p1 = np.array(p1)
return r1, p1
heg_pcc_list = []
hvg_pcc_list = []
mse_list = []
mae_list = []
for fold in range(12):
save_path = f"./embedding_result/cscc_result/embeddings_{fold}/"
spot_embeddings = [np.load(save_path + f"spot_embeddings_{i + 1}.npy") for i in range(12)]
image_embeddings = np.load(save_path + f"img_embeddings_{fold + 1}.npy")
# query
image_query = image_embeddings
expression_gt = spot_expressions[fold]
spot_embeddings = spot_embeddings[:fold] + spot_embeddings[fold + 1:]
spot_expressions_rest = spot_expressions[:fold] + spot_expressions[fold + 1:]
spot_key = np.concatenate(spot_embeddings, axis=1)
expression_key = np.concatenate(spot_expressions_rest, axis=1)
method = "weighted"
save_path = f"./cscc_pred/preds_{fold}/"
if image_query.shape[1] != 256:
image_query = image_query.T
print("image query shape: ", image_query.shape)
if expression_gt.shape[0] != image_query.shape[0]:
expression_gt = expression_gt.T
print("expression_gt shape: ", expression_gt.shape)
if spot_key.shape[1] != 256:
spot_key = spot_key.T
print("spot_key shape: ", spot_key.shape)
if expression_key.shape[0] != spot_key.shape[0]:
expression_key = expression_key.T
print("expression_key shape: ", expression_key.shape)
value, indices = find_matches(spot_key, image_query, top_k=600)
matched_spot_embeddings_pred = np.zeros((indices.shape[0], spot_key.shape[1]))
matched_spot_expression_pred = np.zeros((indices.shape[0], expression_key.shape[1]))
for i in range(indices.shape[0]):
# weights = value[i] / np.sum(value[i])
#
# s = sum(weights)
# matched_spot_embeddings_pred[i, :] = np.average(spot_key[indices[i, :], :], axis=0, weights=weights)
# matched_spot_expression_pred[i, :] = np.average(expression_key[indices[i, :], :], axis=0, weights=weights)
from sklearn.metrics.pairwise import cosine_similarity
# a = 1 - cosine_similarity(spot_key[indices[i, :], :], image_query[i, :].reshape(1, -1))
dis = np.linalg.norm(spot_key[indices[i, :], :] - image_query[i, :], axis=1)
reciprocal_of_square_dis = np.reciprocal(dis ** 2)
weights = reciprocal_of_square_dis / np.sum(reciprocal_of_square_dis)
# weights = weights.flatten()
matched_spot_embeddings_pred[i, :] = np.average(spot_key[indices[i, :], :], axis=0, weights=weights)
matched_spot_expression_pred[i, :] = np.average(expression_key[indices[i, :], :], axis=0,
weights=weights)
#
# # print("matched spot embeddings pred shape: ", matched_spot_embeddings_pred.shape)
# # print("matched spot expression pred shape: ", matched_spot_expression_pred.shape)
# np.save(save_path + "matched_spot_expression_pred.npy", matched_spot_expression_pred.T)
true = expression_gt
pred = matched_spot_expression_pred
# print(pred.shape)
# print(true.shape)
# print(np.max(pred))
# print(np.max(true))
# print(np.min(pred))
# print(np.min(true))
mse = mean_squared_error(true, pred)
mse_list.append(mse)
print("Mean Squared Error (MSE): ", mse)
mae = mean_absolute_error(true, pred)
mae_list.append(mae)
print("Mean Absolute Error (MAE): ", mae)
gene_list_path = "D:\dataset\Her2st\data/skin_hvg_cut_1000.npy"
gene_list = list(np.load(gene_list_path, allow_pickle=True))
adata_ture = anndata.AnnData(true)
adata_pred = anndata.AnnData(pred)
adata_pred.var_names = gene_list
adata_ture.var_names = gene_list
gene_mean_expression = np.mean(adata_ture.X, axis=0)
top_50_genes_indices = np.argsort(gene_mean_expression)[::-1][:50]
top_50_genes_names = adata_ture.var_names[top_50_genes_indices]
top_50_genes_expression = adata_ture[:, top_50_genes_names]
top_50_genes_pred = adata_pred[:, top_50_genes_names]
heg_pcc, heg_p = get_R(top_50_genes_pred, top_50_genes_expression)
hvg_pcc, hvg_p = get_R(adata_pred, adata_ture)
hvg_pcc = hvg_pcc[~np.isnan(hvg_pcc)]
heg_pcc_list.append(np.mean(heg_pcc))
hvg_pcc_list.append(np.mean(hvg_pcc))
from sklearn.metrics import mean_squared_error, mean_absolute_error
print(f"avg heg pcc: {np.mean(heg_pcc_list):.4f}")
print(f"avg hvg pcc: {np.mean(hvg_pcc_list):.4f}")
print(f"Mean Squared Error (MSE): {np.mean(mse_list):.4f}")
print(f"Mean Absolute Error (MAE): {np.mean(mae_list):.4f}")