{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 数据聚合：「输入药物 SMILES，输出基因表达 + 细胞形态」的多模态虚拟细胞模型，配对不齐，训练集不均衡。\n", "\n", "## 策略有3种：\n", "\n", "### 1 是按 SMILES 聚合，对每个 SMILES 在每个模态上 取平均或中位数（或更复杂的 embedding 聚合）。\n", "\n", "### 2 是多实例学习（Multiple Instance Learning, MIL）：思路：把一个 SMILES 的所有 replicate 当作一个 bag，模型学习 bag-level 对齐。\n", "\n", "### 3 是不对齐 replicate，随机采样：思路：训练时，每个 batch 从两个模态中随机采样该 SMILES 的一个 replicate，逐步逼近两个分布。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import os\n", "import h5py\n", "\n", "def load_from_HDF(fname):\n", " \"\"\"Load data from a HDF5 file to a dictionary.\"\"\"\n", " data = dict()\n", " with h5py.File(fname, 'r') as f:\n", " for key in f:\n", " data[key] = np.asarray(f[key])\n", " if isinstance(data[key][0], np.bytes_):\n", " data[key] = data[key].astype(str)\n", " return data\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "CP_path = './Processed_Paired_CP_KeepRep.h5'\n", "GE_path = './Processed_Paired_GE_KeepRep.h5'\n", "\n", "CP_CSV = load_from_HDF(CP_path)\n", "GE_CSV = load_from_HDF(GE_path)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'canonical_smiles': array(['CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC',\n", " 'C=C(C)[C@@H]1CCC(C)=C[C@H]1c1c(OC)cc(C)cc1OC',\n", " 'COc1cc(O)cc(/C=C/c2ccccc2)c1', ...,\n", " 'C[C@@H]1CN([C@@H](C)CO)C(=O)CCCn2cc(nn2)CO[C@H]1CN(C)C(=O)c1ccncc1',\n", " 'C[C@H](CO)N1C[C@H](C)[C@@H](CN(C)C(=O)c2ccncc2)OCc2cn(nn2)CCCC1=O',\n", " 'C[C@@H]1CN([C@@H](C)CO)C(=O)CCCn2nncc2CO[C@H]1CN(C)Cc1ccc2c(c1)OCCN2C'],\n", " dtype='此处了解详细信息。\n", "\u001b[1;31m有关更多详细信息，请查看 Jupyter log。" ] } ], "source": [ "out_path = './Paired_CP_GE_Dataset.h5'\n", "\n", "agg_data = load_from_HDF(out_path)\n", "agg_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "共有化合物: 20341\n", "总配对样本数: 242192\n", "数据集形状:\n", " control_CP: (242192, 775)\n", " target_CP: (242192, 775)\n", " control_GE: (242192, 977)\n", " target_GE: (242192, 977)\n", "✅ 配对数据集已保存到: ./Paired_CP_GE_Dataset.h5\n" ] } ], "source": [ "# import h5py\n", "# import numpy as np\n", "# import pandas as pd\n", "# from collections import defaultdict\n", "\n", "# def create_paired_dataset(CP_data, GE_data, out_path):\n", "# \"\"\"\n", "# 创建样本级配对的多模态数据集\n", "# 保持原始样本的完整性，只配对真正对应的样本\n", "# \"\"\"\n", " \n", "# # 构建SMILES到索引列表的映射\n", "# cp_smiles_to_indices = defaultdict(list)\n", "# ge_smiles_to_indices = defaultdict(list)\n", " \n", "# for idx, smiles in enumerate(CP_data['canonical_smiles']):\n", "# cp_smiles_to_indices[smiles].append(idx)\n", " \n", "# for idx, smiles in enumerate(GE_data['canonical_smiles']):\n", "# ge_smiles_to_indices[smiles].append(idx)\n", " \n", "# # 找到共有的SMILES\n", "# common_smiles = set(cp_smiles_to_indices.keys()) & set(ge_smiles_to_indices.keys())\n", "# print(f\"共有化合物: {len(common_smiles)}\")\n", " \n", "# # 收集所有有效配对\n", "# paired_samples = []\n", " \n", "# for smiles in common_smiles:\n", "# cp_indices = cp_smiles_to_indices[smiles]\n", "# ge_indices = ge_smiles_to_indices[smiles]\n", " \n", "# # 为每个SMILES创建所有可能的CP-GE配对\n", "# for cp_idx in cp_indices:\n", "# for ge_idx in ge_indices:\n", "# paired_samples.append({\n", "# 'canonical_smiles': smiles,\n", "# 'cp_index': cp_idx,\n", "# 'ge_index': ge_idx\n", "# })\n", " \n", "# print(f\"总配对样本数: {len(paired_samples)}\")\n", " \n", "# # 创建最终的字典形数据集\n", "# dataset_dict = {\n", "# 'canonical_smiles': [],\n", "# 'control_CP': [],\n", "# 'target_CP': [], \n", "# 'control_GE': [],\n", "# 'target_GE': []\n", "# }\n", " \n", "# for sample in paired_samples:\n", "# dataset_dict['canonical_smiles'].append(sample['canonical_smiles'])\n", "# dataset_dict['control_CP'].append(CP_data['control'][sample['cp_index']])\n", "# dataset_dict['target_CP'].append(CP_data['target'][sample['cp_index']])\n", "# dataset_dict['control_GE'].append(GE_data['control'][sample['ge_index']]) \n", "# dataset_dict['target_GE'].append(GE_data['target'][sample['ge_index']])\n", " \n", "# # 转换为numpy数组\n", "# final_dataset = {}\n", "# final_dataset['canonical_smiles'] = np.array(dataset_dict['canonical_smiles'], dtype='S')\n", "# final_dataset['control_CP'] = np.array(dataset_dict['control_CP'], dtype=np.float32)\n", "# final_dataset['target_CP'] = np.array(dataset_dict['target_CP'], dtype=np.float32)\n", "# final_dataset['control_GE'] = np.array(dataset_dict['control_GE'], dtype=np.float32)\n", "# final_dataset['target_GE'] = np.array(dataset_dict['target_GE'], dtype=np.float32)\n", " \n", "# print(f\"数据集形状:\")\n", "# print(f\" control_CP: {final_dataset['control_CP'].shape}\")\n", "# print(f\" target_CP: {final_dataset['target_CP'].shape}\")\n", "# print(f\" control_GE: {final_dataset['control_GE'].shape}\")\n", "# print(f\" target_GE: {final_dataset['target_GE'].shape}\")\n", " \n", "# # 保存为HDF5\n", "# with h5py.File(out_path, 'w') as f:\n", "# for key, value in final_dataset.items():\n", "# f.create_dataset(key, data=value)\n", " \n", "# print(f\"✅ 配对数据集已保存到: {out_path}\")\n", "# return final_dataset\n", "\n", "# # 使用方案\n", "# out_path = './Paired_CP_GE_Dataset.h5'\n", "# paired_dataset = create_paired_dataset(CP_CSV, GE_CSV, out_path)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'canonical_smiles': array(['C[C@@H]1CCCCO[C@@H](CN(C)C(=O)c2ccccc2)[C@@H](C)CN([C@H](C)CO)C(=O)c2cc(N(C)C)ccc2O1',\n", " 'C[C@@H]1CCCCO[C@@H](CN(C)C(=O)c2ccccc2)[C@@H](C)CN([C@H](C)CO)C(=O)c2cc(N(C)C)ccc2O1',\n", " 'C[C@@H]1CCCCO[C@@H](CN(C)C(=O)c2ccccc2)[C@@H](C)CN([C@H](C)CO)C(=O)c2cc(N(C)C)ccc2O1',\n", " ...,\n", " 'C/C=C/c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1.C/C=C\\\\c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1',\n", " 'C/C=C/c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1.C/C=C\\\\c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1',\n", " 'C/C=C/c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1.C/C=C\\\\c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1'],\n", " dtype=' 0 or inf_count > 0:\n", " print(f\"警告: {name} 包含 {nan_count} NaN 和 {inf_count} Inf\")\n", " data_array = np.nan_to_num(data_array, nan=0.0, posinf=1.0, neginf=-1.0)\n", " \n", " # 数据标准化\n", " mean = data_array.mean()\n", " std = data_array.std()\n", " \n", " print(f\"{name}: 均值={mean:.4f}, 标准差={std:.4f}, 形状={data_array.shape}\")\n", " \n", " return data_array\n", " \n", " final_dataset['control_CP'] = clean_and_validate_data(dataset_dict['control_CP'], 'control_CP')\n", " final_dataset['target_CP'] = clean_and_validate_data(dataset_dict['target_CP'], 'target_CP')\n", " final_dataset['control_GE'] = clean_and_validate_data(dataset_dict['control_GE'], 'control_GE')\n", " final_dataset['target_GE'] = clean_and_validate_data(dataset_dict['target_GE'], 'target_GE')\n", " \n", " # 保存为HDF5\n", " with h5py.File(out_path, 'w') as f:\n", " for key, value in final_dataset.items():\n", " f.create_dataset(key, data=value)\n", " \n", " print(f\"✅ 稳健配对数据集已保存到: {out_path}\")\n", " return final_dataset\n", "\n", "\n", "out_path = './Paired_CP_GE_Dataset.h5'\n", "paired_dataset = create_robust_paired_dataset(CP_CSV, GE_CSV, out_path)\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "out_path = './Paired_CP_GE_Dataset.h5'\n", "\n", "agg_data = load_from_HDF(out_path)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((169287, 775), (169287, 775), (169287, 977), (169287, 977))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agg_data['control_CP'].shape, agg_data['target_CP'].shape, agg_data['control_GE'].shape, agg_data['target_GE'].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "boom", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 2 }