Sync

.gitattributes

@@ -1 +1,3 @@
 *.h5ad filter=lfs diff=lfs merge=lfs -text
+*.glb filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text

embryo-backend/Data/FakeEmbryo.py

@@ -6,7 +6,7 @@ import os
 np.random.seed(42)
 
 # 参数配置
-stages = [("CS7", 500), ("CS7.5", 750), ("CS8", 1000), ("CS9", 1500)]
+stages = [("CS7", 500), ("CS8", 1000), ("CS9", 1500)]
 genes = ["SOX2", "NANOG", "T", "POU5F1", "OTX2", "ZIC2", "FOXA2", "LEFTY1"]
 layers = {
     "Ectoderm": 1.0,     # 外层

embryo-backend/Data/GetGeneName.py

@@ -0,0 +1,30 @@
+import gzip
+import re
+import os
+
+# ==== 参数 ====
+gtf_file = "gencode.v48.annotation.gtf.gz"   # 你的 GTF 文件
+output_file = "human_protein_coding_genes.txt"
+script_dir = os.path.dirname(os.path.realpath(__file__))
+genes = set()
+
+# ==== 解析 GTF ====
+with gzip.open(f"{script_dir}/{gtf_file}", "rt") as f:
+    for line in f:
+        if line.startswith("#"):
+            continue
+        parts = line.split("\t")
+        if len(parts) > 2 and parts[2] == "gene":
+            # 仅保留 gene_type=protein_coding 的行
+            if 'gene_type "protein_coding"' in line or 'gene_biotype "protein_coding"' in line:
+                m = re.search(r'gene_name "([^"]+)"', line)
+                if m:
+                    genes.add(m.group(1))
+
+# ==== 保存到文件 ====
+genes_sorted = sorted(genes)
+with open(f"{script_dir}/{output_file}", "w") as f:
+    for g in genes_sorted:
+        f.write(g + "\n")
+
+print(f"✅ 已提取 {len(genes_sorted)} 个蛋白编码基因 → {output_file}")

embryo-backend/Data/glbTrans.py

@@ -0,0 +1,173 @@
+import os
+import trimesh
+import pandas as pd
+import numpy as np
+import anndata as ad
+from collections import defaultdict
+
+# ==== 参数 ====
+script_dir = os.path.dirname(os.path.realpath(__file__))
+glb_files = [f"CS{i}.glb" for i in range(11,24,1)]
+N_total = 30000
+use_surface_sampling = False
+n_genes = 1000
+n_markers_per_label = 10
+
+# ==== 读取真实基因名 ====
+gene_list_file = os.path.join(script_dir, "human_protein_coding_genes.txt")
+assert os.path.exists(gene_list_file), f"❌ 缺少基因名文件 {gene_list_file}"
+all_gene_names = pd.read_csv(gene_list_file, header=None)[0].tolist()
+gene_names = all_gene_names[:n_genes]
+
+# ==== 真实 marker 基因 ====
+true_markers = {
+    "Ectoderm": ["SOX2", "PAX6", "NES", "TUBB3", "OTX2"],
+    "Mesoderm": ["TBXT", "MESP1", "HAND1", "GATA4", "PDGFRA"],
+    "Endoderm": ["SOX17", "FOXA2", "GATA6", "CXCR4", "HNF1B"],
+    "Notochord": ["SHH", "NOG", "CHRD", "FOXA2", "Brachyury"],
+    "NeuralTube": ["OLIG2", "NKX6-1", "HOXB4", "PAX3", "SNAI2"]
+}
+
+# ==== 采样函数 ====
+def sample_mesh(mesh, label, n_samples):
+    if use_surface_sampling:
+        points, face_idx = trimesh.sample.sample_surface(mesh, n_samples)
+        if hasattr(mesh.visual, "vertex_colors") and len(mesh.visual.vertex_colors) == len(mesh.vertices):
+            face_colors = mesh.visual.vertex_colors[mesh.faces]
+            colors = face_colors.mean(axis=1)[face_idx, :3]
+        else:
+            mat_color = np.array([200, 200, 200])
+            colors = np.tile(mat_color, (n_samples, 1))
+    else:
+        verts = mesh.vertices
+        if len(verts) > n_samples:
+            idx = np.random.choice(len(verts), n_samples, replace=False)
+            verts = verts[idx]
+        else:
+            idx = np.arange(len(verts))
+        if hasattr(mesh.visual, "vertex_colors") and len(mesh.visual.vertex_colors) == len(mesh.vertices):
+            colors = mesh.visual.vertex_colors[idx, :3]
+        else:
+            mat_color = np.array([200, 200, 200])
+            colors = np.tile(mat_color, (len(verts), 1))
+        points = verts
+    labels = np.array([label] * len(points))
+    return points, colors, labels
+
+# ==== Step 1: 计算全局最大边长 ====
+all_bounds = []
+for glb_name in glb_files:
+    glb_path = os.path.join(script_dir, glb_name)
+    if not os.path.exists(glb_path):
+        continue
+    scene = trimesh.load(glb_path)
+    if isinstance(scene, trimesh.Scene):
+        for geom in scene.geometry.values():
+            all_bounds.append(geom.bounds)  # (min,max)
+    else:
+        all_bounds.append(scene.bounds)
+
+all_bounds = np.array(all_bounds)
+global_min = np.min(all_bounds[:,0,:], axis=0)
+global_max = np.max(all_bounds[:,1,:], axis=0)
+global_size = np.max(global_max - global_min)
+print(f"🌍 统一缩放基准: global_size={global_size}")
+
+# ==== Step 2: 处理单个 GLB ====
+def process_glb(glb_path, sample_name):
+    scene = trimesh.load(glb_path)
+    all_points, all_colors, all_labels = [], [], []
+
+    if isinstance(scene, trimesh.Scene):
+        total_points = sum(len(g.vertices) for g in scene.geometry.values())
+        for name, geom in scene.geometry.items():
+            ratio = len(geom.vertices) / total_points
+            n_samples = max(1, int(N_total * ratio))
+            p, c, l = sample_mesh(geom, name, n_samples)
+            all_points.append(p)
+            all_colors.append(c)
+            all_labels.append(l)
+    else:
+        p, c, l = sample_mesh(scene, "mesh", N_total)
+        all_points.append(p)
+        all_colors.append(c)
+        all_labels.append(l)
+
+    points = np.vstack(all_points)
+    colors = np.vstack(all_colors)
+    labels = np.concatenate(all_labels)
+
+    if len(points) > N_total:
+        idx = np.random.choice(len(points), N_total, replace=False)
+        points, colors, labels = points[idx], colors[idx], labels[idx]
+
+    # ✅ 平移到中心点
+    center = points.mean(axis=0)
+    points_centered = points - center
+
+    # ✅ 统一缩放（保持原始比例）
+    points_scaled = points_centered / global_size
+
+    # ✅ 如需修正坐标系方向，可启用以下行（示例：交换Y和Z）
+    # points_scaled = points_scaled[:, [0, 2, 1]]
+    # points_scaled[:, 1] *= -1
+
+    df = pd.DataFrame(points_scaled, columns=["x","y","z"])
+    df["r"], df["g"], df["b"] = colors[:,0], colors[:,1], colors[:,2]
+    df["label"] = labels
+
+    csv_file = os.path.join(script_dir, f"{sample_name}_point_cloud_30000_centered_scaled.csv")
+    df.to_csv(csv_file, index=False)
+    print(f"✅ 已导出 {csv_file} (中心化 & 统一大小, 保持比例)")
+    return df
+
+# ==== Step 3: 生成 h5ad ====
+def create_h5ad(df, sample_name):
+    points = df[['x','y','z']].to_numpy()
+    cell_types = df['label'].to_numpy()
+    unique_labels = sorted(set(cell_types))
+
+    marker_genes = {}
+    all_marker_set = set()
+    for ct in unique_labels:
+        valid_markers = [g for g in true_markers.get(ct, []) if g in gene_names]
+        while len(valid_markers) < n_markers_per_label:
+            g = np.random.choice(gene_names)
+            if g not in valid_markers:
+                valid_markers.append(g)
+        marker_genes[ct] = valid_markers
+        all_marker_set.update(valid_markers)
+
+    np.random.seed(42)
+    expr_matrix = np.random.poisson(lam=1.0, size=(len(df), len(gene_names))).astype(np.float32)
+    for ct in unique_labels:
+        cell_idx = np.where(cell_types == ct)[0]
+        marker_idx = [gene_names.index(g) for g in marker_genes[ct]]
+        expr_matrix[np.ix_(cell_idx, marker_idx)] += np.random.poisson(lam=5.0, size=(len(cell_idx), len(marker_idx)))
+
+    obs = pd.DataFrame(index=[f"{sample_name}_cell{i}" for i in range(len(df))])
+    obs["cell_type"] = cell_types
+    var = pd.DataFrame(index=gene_names)
+    var["is_marker"] = ["yes" if g in all_marker_set else "no" for g in gene_names]
+
+    adata = ad.AnnData(X=expr_matrix, obs=obs, var=var)
+    adata.obsm["spatial"] = points
+
+    output_h5ad = os.path.join(script_dir, f"{sample_name}.h5ad")
+    adata.write(output_h5ad)
+
+    with open(os.path.join(script_dir, f"{sample_name}_marker_genes.txt"), "w") as f:
+        for ct, genes in marker_genes.items():
+            f.write(f"{ct} : {', '.join(genes)}\n")
+
+    print(f"✅ 已生成 {output_h5ad} ({len(df)} cells × {len(gene_names)} genes)")
+
+# ==== Step 4: 批量运行 ====
+for glb_name in glb_files:
+    glb_path = os.path.join(script_dir, glb_name)
+    if os.path.exists(glb_path):
+        sample_name = os.path.splitext(glb_name)[0]
+        df = process_glb(glb_path, sample_name)
+        create_h5ad(df, sample_name)
+    else:
+        print(f"⚠️ 未找到 {glb_name}，跳过")