{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 数据处理:SMILES信息在Gene里,CP里面没有,所以首先在这里实现映射和对齐" ] }, { "cell_type": "code", "execution_count": 2, "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": "markdown", "metadata": {}, "source": [ "# 先下载数据,按照 aws , 指令如下:这里面有多组数据,都是多模态的(同时包含基因和形态),但是好几组数据问题比较大,我们先用BBBC036\n", "\n", "aws s3 sync s3://cellpainting-gallery/cpg0003-rosetta/broad/workspace/preprocessed_data/ ./cpg0003 --exact-timestamps --no-sign-request" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "打开其中的BC036,看文件名就能大致推断这些是 L1000(基因表达)实验数据的不同处理版本。拆一下:\n", "\n", "# replicate_level_l1k.csv.gz\n", "\n", "replicate level 表示这是原始的重复水平(每个实验重复、每个孔的独立测量)。\n", "\n", "没有额外后缀,通常代表的是比较“基础”的数值(可能是直接从测序/芯片信号经过标准化后的结果)。\n", "\n", "# replicate_level_l1k_pclfc.csv.gz\n", "\n", "pclfc 应该是 plate-centered log fold change 的缩写。\n", "\n", "意味着:对同一板(plate)的数据做了中心化(plate centering),再计算 log fold change,相当于消除板间差异的影响。\n", "\n", "# replicate_level_l1k_pczscore.csv.gz\n", "\n", "pczscore = plate-centered z-score。\n", "\n", "在每个板内,先做中心化,再按标准差缩放,得到标准正态分布的 z-score 值。常用于跨板比较,因为板效应(batch effect)被部分去掉。\n", "\n", "# replicate_level_l1k_vczscore.csv.gz\n", "\n", "vczscore = vehicle-centered z-score。\n", "\n", "是以 对照组(vehicle control) 为基准,做 z-score 标准化。这样更直接衡量药物相对对照的扰动幅度。\n", "\n", "# treatment_level_l1k.csv.gz\n", "\n", "treatment level 表示已经把 replicate-level 聚合到 处理水平(treatment level)。\n", "\n", "一个药物 × 剂量 × 时间点的所有重复实验已经合并成一个代表性 profile(比如均值或中位数)。\n", "\n", "通常用于下游分析(聚类、表型比较),因为它去掉了重复的噪声。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(21122, 627) (6929, 988)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_3471785/3133962603.py:8: DtypeWarning: Columns (981,982,983) have mixed types. Specify dtype option on import or set low_memory=False.\n", " GE_CSV = pd.read_csv(GE_path)\n" ] } ], "source": [ "root = '/home/bob/boom/VCBench/data/MVC/CDRPBIO-BBBC036-Bray/'\n", "\n", "# 只用里面的细胞形态数据\n", "CP_path = root + 'CellPainting/replicate_level_cp_normalized_variable_selected.csv.gz'\n", "GE_path = root + 'L1000/replicate_level_l1k.csv.gz'\n", "\n", "CP_CSV = pd.read_csv(CP_path)\n", "GE_CSV = pd.read_csv(GE_path)\n", "print(CP_CSV.shape, GE_CSV.shape)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexpert_type
0trt17594
1control3528
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" ], "text/plain": [ " index pert_type\n", "0 trt 17594\n", "1 control 3528" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CP_CSV['pert_type'].value_counts().reset_index()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2239" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(CP_CSV['Metadata_pert_id'].unique())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 读取基因是为了把里面的 SMIELS 迁移过来" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "映射字典大小: 1917\n", "匹配成功: 15012/21122 (71.07%)\n" ] } ], "source": [ "# 创建BROAD_CPD_ID到CPD_SMILES的映射字典\n", "smiles_mapping = dict(zip(GE_CSV['BROAD_CPD_ID'], GE_CSV['CPD_SMILES']))\n", "\n", "# 检查映射是否唯一(键值对数量应等于唯一ID数量)\n", "print(f\"映射字典大小: {len(smiles_mapping)}\")\n", "\n", "# 使用映射为CP_CSV添加SMILES列\n", "CP_CSV['SMILES'] = CP_CSV['Metadata_pert_id'].map(smiles_mapping)\n", "\n", "# 检查匹配情况\n", "matched = CP_CSV['SMILES'].notna().sum()\n", "total = len(CP_CSV)\n", "print(f\"匹配成功: {matched}/{total} ({matched/total:.2%})\")\n", "\n", "# 保存结果\n", "# CP_CSV.to_csv('path/to/updated_cp_data.csv', index=False) " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(18540, 628)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 提取匹配成功或者pert_type为'control'的记录(或关系)\n", "\n", "CP_CSV_matched = CP_CSV[\n", " (CP_CSV['SMILES'].notna()) | # 匹配成功的记录\n", " (CP_CSV['pert_type'] == 'control') # 或pert_type为control的记录\n", "].copy()\n", "CP_CSV_matched.shape" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Metadata_PlateMetadata_WellMetadata_Assay_Plate_BarcodeMetadata_Plate_Map_NameMetadata_well_positionMetadata_ASSAY_WELL_ROLEMetadata_broad_sampleMetadata_mmoles_per_literMetadata_solventMetadata_pert_id...Nuclei_Texture_Variance_RNA_10_0Metadata_pert_inameMetadata_pert_iname2Metadata_moaMetadata_targetMetadata_mmoles_per_liter2Metadata_Sample_Dosepert_typecontrol_typeSMILES
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124277a0224277H-BIOA-004-3a02treatedBRD-K18316707-001-01-95.000000DMSOBRD-K18316707...0.174417O-1918BRD-K18316707cannabinoid receptor antagonistNaN10.00BRD-K18316707-001-01-9_10.0trtNaNCOc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C
224277a0324277H-BIOA-004-3a03treatedBRD-K18438502-001-02-65.000000DMSOBRD-K18438502...0.115051NaNNaNNaNNaN10.00BRD-K18438502-001-02-6_10.0trtNaNCOc1cc(O)cc(\\C=C\\c2ccccc2)c1
324277a0424277H-BIOA-004-3a04treatedBRD-K18550767-001-02-85.000000DMSOBRD-K18550767...0.322316bergeninbergenininterleukin inhibitorIL1B, TNF10.00BRD-K18550767-001-02-8_10.0trtNaNCOc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]...
424277a0524277H-BIOA-004-3a05treatedBRD-K18574842-323-03-32.195487DMSOBRD-K18574842...0.437593nafcillinnafcillinbacterial cell wall synthesis inhibitorCYP1A2, CYP3A4, SLC22A64.39BRD-K18574842-323-03-3_4.39trtNaNCCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@...
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5 rows × 628 columns

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" ], "text/plain": [ " Metadata_Plate Metadata_Well Metadata_Assay_Plate_Barcode \\\n", "0 24277 a01 24277 \n", "1 24277 a02 24277 \n", "2 24277 a03 24277 \n", "3 24277 a04 24277 \n", "4 24277 a05 24277 \n", "\n", " Metadata_Plate_Map_Name Metadata_well_position Metadata_ASSAY_WELL_ROLE \\\n", "0 H-BIOA-004-3 a01 treated \n", "1 H-BIOA-004-3 a02 treated \n", "2 H-BIOA-004-3 a03 treated \n", "3 H-BIOA-004-3 a04 treated \n", "4 H-BIOA-004-3 a05 treated \n", "\n", " Metadata_broad_sample Metadata_mmoles_per_liter Metadata_solvent \\\n", "0 BRD-K18250272-003-03-7 3.022516 DMSO \n", "1 BRD-K18316707-001-01-9 5.000000 DMSO \n", "2 BRD-K18438502-001-02-6 5.000000 DMSO \n", "3 BRD-K18550767-001-02-8 5.000000 DMSO \n", "4 BRD-K18574842-323-03-3 2.195487 DMSO \n", "\n", " Metadata_pert_id ... Nuclei_Texture_Variance_RNA_10_0 Metadata_pert_iname \\\n", "0 BRD-K18250272 ... 0.066800 propoxycaine \n", "1 BRD-K18316707 ... 0.174417 O-1918 \n", "2 BRD-K18438502 ... 0.115051 NaN \n", "3 BRD-K18550767 ... 0.322316 bergenin \n", "4 BRD-K18574842 ... 0.437593 nafcillin \n", "\n", " Metadata_pert_iname2 Metadata_moa \\\n", "0 propoxycaine local anesthetic \n", "1 BRD-K18316707 cannabinoid receptor antagonist \n", "2 NaN NaN \n", "3 bergenin interleukin inhibitor \n", "4 nafcillin bacterial cell wall synthesis inhibitor \n", "\n", " Metadata_target Metadata_mmoles_per_liter2 \\\n", "0 NaN 6.05 \n", "1 NaN 10.00 \n", "2 NaN 10.00 \n", "3 IL1B, TNF 10.00 \n", "4 CYP1A2, CYP3A4, SLC22A6 4.39 \n", "\n", " Metadata_Sample_Dose pert_type control_type \\\n", "0 BRD-K18250272-003-03-7_6.05 trt NaN \n", "1 BRD-K18316707-001-01-9_10.0 trt NaN \n", "2 BRD-K18438502-001-02-6_10.0 trt NaN \n", "3 BRD-K18550767-001-02-8_10.0 trt NaN \n", "4 BRD-K18574842-323-03-3_4.39 trt NaN \n", "\n", " SMILES \n", "0 CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC \n", "1 COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C \n", "2 COc1cc(O)cc(\\C=C\\c2ccccc2)c1 \n", "3 COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]... \n", "4 CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@... \n", "\n", "[5 rows x 628 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CP_CSV_matched.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " index pert_type\n", "0 trt 15012\n", "1 control 3528" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CP_CSV_matched['pert_type'].value_counts().reset_index()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Metadata_PlateMetadata_WellMetadata_Assay_Plate_BarcodeMetadata_Plate_Map_NameMetadata_well_positionMetadata_ASSAY_WELL_ROLEMetadata_broad_sampleMetadata_mmoles_per_literMetadata_solventMetadata_pert_id...Nuclei_Texture_Variance_RNA_10_0Metadata_pert_inameMetadata_pert_iname2Metadata_moaMetadata_targetMetadata_mmoles_per_liter2Metadata_Sample_Dosepert_typecontrol_typeSMILES
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324277a0424277H-BIOA-004-3a04treatedBRD-K18550767-001-02-85.000000DMSOBRD-K18550767...0.322316bergeninbergenininterleukin inhibitorIL1B, TNF10.00BRD-K18550767-001-02-8_10.0trtNaNCOc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]...
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18540 rows × 628 columns

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" ], "text/plain": [ " Metadata_Plate Metadata_Well Metadata_Assay_Plate_Barcode \\\n", "0 24277 a01 24277 \n", "1 24277 a02 24277 \n", "2 24277 a03 24277 \n", "3 24277 a04 24277 \n", "4 24277 a05 24277 \n", "... ... ... ... \n", "21063 26247 n14 26247 \n", "21084 26247 o11 26247 \n", "21085 26247 o12 26247 \n", "21108 26247 p11 26247 \n", "21109 26247 p12 26247 \n", "\n", " Metadata_Plate_Map_Name Metadata_well_position Metadata_ASSAY_WELL_ROLE \\\n", "0 H-BIOA-004-3 a01 treated \n", "1 H-BIOA-004-3 a02 treated \n", "2 H-BIOA-004-3 a03 treated \n", "3 H-BIOA-004-3 a04 treated \n", "4 H-BIOA-004-3 a05 treated \n", "... ... ... ... \n", "21063 H-BIOA-007-3 n14 mock \n", "21084 H-BIOA-007-3 o11 mock \n", "21085 H-BIOA-007-3 o12 mock \n", "21108 H-BIOA-007-3 p11 mock \n", "21109 H-BIOA-007-3 p12 mock \n", "\n", " Metadata_broad_sample Metadata_mmoles_per_liter Metadata_solvent \\\n", "0 BRD-K18250272-003-03-7 3.022516 DMSO \n", "1 BRD-K18316707-001-01-9 5.000000 DMSO \n", "2 BRD-K18438502-001-02-6 5.000000 DMSO \n", "3 BRD-K18550767-001-02-8 5.000000 DMSO \n", "4 BRD-K18574842-323-03-3 2.195487 DMSO \n", "... ... ... ... \n", "21063 DMSO 0.000000 DMSO \n", "21084 DMSO 0.000000 DMSO \n", "21085 DMSO 0.000000 DMSO \n", "21108 DMSO 0.000000 DMSO \n", "21109 DMSO 0.000000 DMSO \n", "\n", " Metadata_pert_id ... Nuclei_Texture_Variance_RNA_10_0 \\\n", "0 BRD-K18250272 ... 0.066800 \n", "1 BRD-K18316707 ... 0.174417 \n", "2 BRD-K18438502 ... 0.115051 \n", "3 BRD-K18550767 ... 0.322316 \n", "4 BRD-K18574842 ... 0.437593 \n", "... ... ... ... \n", "21063 DMSO ... 0.009180 \n", "21084 DMSO ... -0.203010 \n", "21085 DMSO ... -0.241133 \n", "21108 DMSO ... -0.109876 \n", "21109 DMSO ... -0.116873 \n", "\n", " Metadata_pert_iname Metadata_pert_iname2 \\\n", "0 propoxycaine propoxycaine \n", "1 O-1918 BRD-K18316707 \n", "2 NaN NaN \n", "3 bergenin bergenin \n", "4 nafcillin nafcillin \n", "... ... ... \n", "21063 NaN NaN \n", "21084 NaN NaN \n", "21085 NaN NaN \n", "21108 NaN NaN \n", "21109 NaN NaN \n", "\n", " Metadata_moa Metadata_target \\\n", "0 local anesthetic NaN \n", "1 cannabinoid receptor antagonist NaN \n", "2 NaN NaN \n", "3 interleukin inhibitor IL1B, TNF \n", "4 bacterial cell wall synthesis inhibitor CYP1A2, CYP3A4, SLC22A6 \n", "... ... ... \n", "21063 NaN NaN \n", "21084 NaN NaN \n", "21085 NaN NaN \n", "21108 NaN NaN \n", "21109 NaN NaN \n", "\n", " Metadata_mmoles_per_liter2 Metadata_Sample_Dose pert_type \\\n", "0 6.05 BRD-K18250272-003-03-7_6.05 trt \n", "1 10.00 BRD-K18316707-001-01-9_10.0 trt \n", "2 10.00 BRD-K18438502-001-02-6_10.0 trt \n", "3 10.00 BRD-K18550767-001-02-8_10.0 trt \n", "4 4.39 BRD-K18574842-323-03-3_4.39 trt \n", "... ... ... ... \n", "21063 0.00 negcon control \n", "21084 0.00 negcon control \n", "21085 0.00 negcon control \n", "21108 0.00 negcon control \n", "21109 0.00 negcon control \n", "\n", " control_type SMILES \n", "0 NaN CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC \n", "1 NaN COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C \n", "2 NaN COc1cc(O)cc(\\C=C\\c2ccccc2)c1 \n", "3 NaN COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]... \n", "4 NaN CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@... \n", "... ... ... \n", "21063 negcon NaN \n", "21084 negcon NaN \n", "21085 negcon NaN \n", "21108 negcon NaN \n", "21109 negcon NaN \n", "\n", "[18540 rows x 628 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CP_CSV_matched" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 删除无关元素,保留核心" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Metadata_PlateMetadata_pert_mfc_idMetadata_pert_wellMetadata_pert_id_vendorMetadata_cell_idMetadata_broad_sample_typeMetadata_pert_vehicleMetadata_pert_typeCells_AreaShape_Center_XCells_AreaShape_Compactness...Nuclei_Texture_Variance_RNA_10_0Metadata_pert_inameMetadata_pert_iname2Metadata_moaMetadata_targetMetadata_mmoles_per_liter2Metadata_Sample_Dosepert_typecontrol_typeSMILES
024277BRD-K18250272-003-03-7a01NaNU2OStrtDMSOtrt-0.0703460.256710...0.066800propoxycainepropoxycainelocal anestheticNaN6.05BRD-K18250272-003-03-7_6.05trtNaNCCCOc1cc(N)ccc1C(=O)OCCN(CC)CC
124277BRD-K18316707-001-01-9a02NaNU2OStrtDMSOtrt0.0413800.050962...0.174417O-1918BRD-K18316707cannabinoid receptor antagonistNaN10.00BRD-K18316707-001-01-9_10.0trtNaNCOc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C
224277BRD-K18438502-001-02-6a03NaNU2OStrtDMSOtrt-0.130346-0.098185...0.115051NaNNaNNaNNaN10.00BRD-K18438502-001-02-6_10.0trtNaNCOc1cc(O)cc(\\C=C\\c2ccccc2)c1
324277BRD-K18550767-001-02-8a04NaNU2OStrtDMSOtrt-0.3165550.410722...0.322316bergeninbergenininterleukin inhibitorIL1B, TNF10.00BRD-K18550767-001-02-8_10.0trtNaNCOc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]...
424277BRD-K18574842-323-03-3a05NaNU2OStrtDMSOtrt-0.0475870.248559...0.437593nafcillinnafcillinbacterial cell wall synthesis inhibitorCYP1A2, CYP3A4, SLC22A64.39BRD-K18574842-323-03-3_4.39trtNaNCCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@...
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5 rows × 619 columns

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" ], "text/plain": [ " Metadata_Plate Metadata_pert_mfc_id Metadata_pert_well \\\n", "0 24277 BRD-K18250272-003-03-7 a01 \n", "1 24277 BRD-K18316707-001-01-9 a02 \n", "2 24277 BRD-K18438502-001-02-6 a03 \n", "3 24277 BRD-K18550767-001-02-8 a04 \n", "4 24277 BRD-K18574842-323-03-3 a05 \n", "\n", " Metadata_pert_id_vendor Metadata_cell_id Metadata_broad_sample_type \\\n", "0 NaN U2OS trt \n", "1 NaN U2OS trt \n", "2 NaN U2OS trt \n", "3 NaN U2OS trt \n", "4 NaN U2OS trt \n", "\n", " Metadata_pert_vehicle Metadata_pert_type Cells_AreaShape_Center_X \\\n", "0 DMSO trt -0.070346 \n", "1 DMSO trt 0.041380 \n", "2 DMSO trt -0.130346 \n", "3 DMSO trt -0.316555 \n", "4 DMSO trt -0.047587 \n", "\n", " Cells_AreaShape_Compactness ... Nuclei_Texture_Variance_RNA_10_0 \\\n", "0 0.256710 ... 0.066800 \n", "1 0.050962 ... 0.174417 \n", "2 -0.098185 ... 0.115051 \n", "3 0.410722 ... 0.322316 \n", "4 0.248559 ... 0.437593 \n", "\n", " Metadata_pert_iname Metadata_pert_iname2 \\\n", "0 propoxycaine propoxycaine \n", "1 O-1918 BRD-K18316707 \n", "2 NaN NaN \n", "3 bergenin bergenin \n", "4 nafcillin nafcillin \n", "\n", " Metadata_moa Metadata_target \\\n", "0 local anesthetic NaN \n", "1 cannabinoid receptor antagonist NaN \n", "2 NaN NaN \n", "3 interleukin inhibitor IL1B, TNF \n", "4 bacterial cell wall synthesis inhibitor CYP1A2, CYP3A4, SLC22A6 \n", "\n", " Metadata_mmoles_per_liter2 Metadata_Sample_Dose pert_type \\\n", "0 6.05 BRD-K18250272-003-03-7_6.05 trt \n", "1 10.00 BRD-K18316707-001-01-9_10.0 trt \n", "2 10.00 BRD-K18438502-001-02-6_10.0 trt \n", "3 10.00 BRD-K18550767-001-02-8_10.0 trt \n", "4 4.39 BRD-K18574842-323-03-3_4.39 trt \n", "\n", " control_type SMILES \n", "0 NaN CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC \n", "1 NaN COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C \n", "2 NaN COc1cc(O)cc(\\C=C\\c2ccccc2)c1 \n", "3 NaN COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]... \n", "4 NaN CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@... \n", "\n", "[5 rows x 619 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CP_CSV_matched_filter = CP_CSV_matched.drop(columns=['Metadata_Well', 'Metadata_Assay_Plate_Barcode', 'Metadata_Plate_Map_Name', 'Metadata_well_position',\\\n", " 'Metadata_ASSAY_WELL_ROLE', 'Metadata_broad_sample', 'Metadata_mmoles_per_liter', 'Metadata_solvent', 'Metadata_pert_id'])\n", "CP_CSV_matched_filter.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Metadata_PlateMetadata_pert_mfc_idMetadata_pert_wellMetadata_pert_id_vendorMetadata_cell_idMetadata_broad_sample_typeMetadata_pert_vehicleMetadata_pert_typeCells_AreaShape_Center_XCells_AreaShape_Compactness...Nuclei_Texture_SumVariance_ER_10_0Nuclei_Texture_SumVariance_RNA_10_0Nuclei_Texture_Variance_AGP_10_0Nuclei_Texture_Variance_ER_10_0Nuclei_Texture_Variance_Mito_10_0Nuclei_Texture_Variance_RNA_10_0Metadata_mmoles_per_liter2Metadata_Sample_Dosecontrol_typeSMILES
024277BRD-K18250272-003-03-7a01NaNU2OStrtDMSOtrt-0.0703460.256710...0.006318-0.024573-0.362861-0.199743-0.3298320.0668006.05BRD-K18250272-003-03-7_6.05NaNCCCOc1cc(N)ccc1C(=O)OCCN(CC)CC
124277BRD-K18316707-001-01-9a02NaNU2OStrtDMSOtrt0.0413800.050962...-0.089380-0.041392-0.220610-0.154711-0.1160720.17441710.00BRD-K18316707-001-01-9_10.0NaNCOc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C
224277BRD-K18438502-001-02-6a03NaNU2OStrtDMSOtrt-0.130346-0.098185...0.020954-0.0896330.075818-0.021001-0.0286610.11505110.00BRD-K18438502-001-02-6_10.0NaNCOc1cc(O)cc(\\C=C\\c2ccccc2)c1
324277BRD-K18550767-001-02-8a04NaNU2OStrtDMSOtrt-0.3165550.410722...0.0311780.103611-0.070876-0.044751-0.0095910.32231610.00BRD-K18550767-001-02-8_10.0NaNCOc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]...
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5 rows × 614 columns

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" ], "text/plain": [ " Metadata_Plate Metadata_pert_mfc_id Metadata_pert_well \\\n", "0 24277 BRD-K18250272-003-03-7 a01 \n", "1 24277 BRD-K18316707-001-01-9 a02 \n", "2 24277 BRD-K18438502-001-02-6 a03 \n", "3 24277 BRD-K18550767-001-02-8 a04 \n", "4 24277 BRD-K18574842-323-03-3 a05 \n", "\n", " Metadata_pert_id_vendor Metadata_cell_id Metadata_broad_sample_type \\\n", "0 NaN U2OS trt \n", "1 NaN U2OS trt \n", "2 NaN U2OS trt \n", "3 NaN U2OS trt \n", "4 NaN U2OS trt \n", "\n", " Metadata_pert_vehicle Metadata_pert_type Cells_AreaShape_Center_X \\\n", "0 DMSO trt -0.070346 \n", "1 DMSO trt 0.041380 \n", "2 DMSO trt -0.130346 \n", "3 DMSO trt -0.316555 \n", "4 DMSO trt -0.047587 \n", "\n", " Cells_AreaShape_Compactness ... Nuclei_Texture_SumVariance_ER_10_0 \\\n", "0 0.256710 ... 0.006318 \n", "1 0.050962 ... -0.089380 \n", "2 -0.098185 ... 0.020954 \n", "3 0.410722 ... 0.031178 \n", "4 0.248559 ... 0.339794 \n", "\n", " Nuclei_Texture_SumVariance_RNA_10_0 Nuclei_Texture_Variance_AGP_10_0 \\\n", "0 -0.024573 -0.362861 \n", "1 -0.041392 -0.220610 \n", "2 -0.089633 0.075818 \n", "3 0.103611 -0.070876 \n", "4 0.238471 -0.157253 \n", "\n", " Nuclei_Texture_Variance_ER_10_0 Nuclei_Texture_Variance_Mito_10_0 \\\n", "0 -0.199743 -0.329832 \n", "1 -0.154711 -0.116072 \n", "2 -0.021001 -0.028661 \n", "3 -0.044751 -0.009591 \n", "4 -0.002357 -0.057382 \n", "\n", " Nuclei_Texture_Variance_RNA_10_0 Metadata_mmoles_per_liter2 \\\n", "0 0.066800 6.05 \n", "1 0.174417 10.00 \n", "2 0.115051 10.00 \n", "3 0.322316 10.00 \n", "4 0.437593 4.39 \n", "\n", " Metadata_Sample_Dose control_type \\\n", "0 BRD-K18250272-003-03-7_6.05 NaN \n", "1 BRD-K18316707-001-01-9_10.0 NaN \n", "2 BRD-K18438502-001-02-6_10.0 NaN \n", "3 BRD-K18550767-001-02-8_10.0 NaN \n", "4 BRD-K18574842-323-03-3_4.39 NaN \n", "\n", " SMILES \n", "0 CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC \n", "1 COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C \n", "2 COc1cc(O)cc(\\C=C\\c2ccccc2)c1 \n", "3 COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]... \n", "4 CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@... \n", "\n", "[5 rows x 614 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CP_CSV_matched_filter = CP_CSV_matched_filter.drop(columns=['Metadata_pert_iname',\\\n", " 'Metadata_pert_iname2', 'Metadata_moa', 'Metadata_target', 'pert_type'])\n", "CP_CSV_matched_filter.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Metadata_PlateCells_AreaShape_Center_XCells_AreaShape_CompactnessCells_AreaShape_ExtentCells_AreaShape_FormFactorCells_AreaShape_OrientationCells_AreaShape_PerimeterCells_AreaShape_SolidityCells_AreaShape_Zernike_1_1Cells_AreaShape_Zernike_2_0...Nuclei_Texture_SumEntropy_Mito_3_0Nuclei_Texture_SumEntropy_RNA_5_0Nuclei_Texture_SumVariance_ER_10_0Nuclei_Texture_SumVariance_RNA_10_0Nuclei_Texture_Variance_AGP_10_0Nuclei_Texture_Variance_ER_10_0Nuclei_Texture_Variance_Mito_10_0Nuclei_Texture_Variance_RNA_10_0Metadata_mmoles_per_liter2SMILES
024277-0.0703460.256710-0.114990-0.237090-0.0379280.5259500.0727950.008488-0.228257...-0.0104270.1189970.006318-0.024573-0.362861-0.199743-0.3298320.0668006.05CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC
1242770.0413800.0509620.051053-0.0337330.0647110.2988180.145941-0.0708260.050313...-0.0585600.065822-0.089380-0.041392-0.220610-0.154711-0.1160720.17441710.00COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C
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5 rows × 605 columns

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" ], "text/plain": [ " Metadata_Plate Cells_AreaShape_Center_X Cells_AreaShape_Compactness \\\n", "0 24277 -0.070346 0.256710 \n", "1 24277 0.041380 0.050962 \n", "2 24277 -0.130346 -0.098185 \n", "3 24277 -0.316555 0.410722 \n", "4 24277 -0.047587 0.248559 \n", "\n", " Cells_AreaShape_Extent Cells_AreaShape_FormFactor \\\n", "0 -0.114990 -0.237090 \n", "1 0.051053 -0.033733 \n", "2 0.044212 0.164668 \n", "3 -0.236208 -0.055084 \n", "4 -0.020058 0.085772 \n", "\n", " Cells_AreaShape_Orientation Cells_AreaShape_Perimeter \\\n", "0 -0.037928 0.525950 \n", "1 0.064711 0.298818 \n", "2 -0.130904 0.001828 \n", "3 -0.099876 0.503035 \n", "4 0.096394 0.545575 \n", "\n", " Cells_AreaShape_Solidity Cells_AreaShape_Zernike_1_1 \\\n", "0 0.072795 0.008488 \n", "1 0.145941 -0.070826 \n", "2 0.094736 0.119333 \n", "3 0.154239 -0.172400 \n", "4 0.214587 -0.086168 \n", "\n", " Cells_AreaShape_Zernike_2_0 ... Nuclei_Texture_SumEntropy_Mito_3_0 \\\n", "0 -0.228257 ... -0.010427 \n", "1 0.050313 ... -0.058560 \n", "2 -0.033444 ... 0.029891 \n", "3 -0.215598 ... 0.015408 \n", "4 -0.090049 ... 0.118873 \n", "\n", " Nuclei_Texture_SumEntropy_RNA_5_0 Nuclei_Texture_SumVariance_ER_10_0 \\\n", "0 0.118997 0.006318 \n", "1 0.065822 -0.089380 \n", "2 0.079777 0.020954 \n", "3 0.339100 0.031178 \n", "4 0.415623 0.339794 \n", "\n", " Nuclei_Texture_SumVariance_RNA_10_0 Nuclei_Texture_Variance_AGP_10_0 \\\n", "0 -0.024573 -0.362861 \n", "1 -0.041392 -0.220610 \n", "2 -0.089633 0.075818 \n", "3 0.103611 -0.070876 \n", "4 0.238471 -0.157253 \n", "\n", " Nuclei_Texture_Variance_ER_10_0 Nuclei_Texture_Variance_Mito_10_0 \\\n", "0 -0.199743 -0.329832 \n", "1 -0.154711 -0.116072 \n", "2 -0.021001 -0.028661 \n", "3 -0.044751 -0.009591 \n", "4 -0.002357 -0.057382 \n", "\n", " Nuclei_Texture_Variance_RNA_10_0 Metadata_mmoles_per_liter2 \\\n", "0 0.066800 6.05 \n", "1 0.174417 10.00 \n", "2 0.115051 10.00 \n", "3 0.322316 10.00 \n", "4 0.437593 4.39 \n", "\n", " SMILES \n", "0 CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC \n", "1 COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C \n", "2 COc1cc(O)cc(\\C=C\\c2ccccc2)c1 \n", "3 COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]... \n", "4 CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@... \n", "\n", "[5 rows x 605 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CP_CSV_matched_filter = CP_CSV_matched_filter.drop(columns=['Metadata_pert_mfc_id','Metadata_pert_type', 'control_type', 'Metadata_Sample_Dose', \\\n", " 'Metadata_pert_well', 'Metadata_pert_id_vendor', 'Metadata_cell_id', 'Metadata_broad_sample_type', 'Metadata_pert_vehicle'])\n", "CP_CSV_matched_filter.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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110.00COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C242770.0413800.0509620.051053-0.0337330.0647110.2988180.145941...-0.0424740.254293-0.0585600.065822-0.089380-0.041392-0.220610-0.154711-0.1160720.174417
210.00COc1cc(O)cc(\\C=C\\c2ccccc2)c124277-0.130346-0.0981850.0442120.164668-0.1309040.0018280.094736...0.0979920.2303310.0298910.0797770.020954-0.0896330.075818-0.021001-0.0286610.115051
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" ], "text/plain": [ " Metadata_mmoles_per_liter2 \\\n", "0 6.05 \n", "1 10.00 \n", "2 10.00 \n", "3 10.00 \n", "4 4.39 \n", "\n", " SMILES Metadata_Plate \\\n", "0 CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC 24277 \n", "1 COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C 24277 \n", "2 COc1cc(O)cc(\\C=C\\c2ccccc2)c1 24277 \n", "3 COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]... 24277 \n", "4 CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@... 24277 \n", "\n", " Cells_AreaShape_Center_X Cells_AreaShape_Compactness \\\n", "0 -0.070346 0.256710 \n", "1 0.041380 0.050962 \n", "2 -0.130346 -0.098185 \n", "3 -0.316555 0.410722 \n", "4 -0.047587 0.248559 \n", "\n", " Cells_AreaShape_Extent Cells_AreaShape_FormFactor \\\n", "0 -0.114990 -0.237090 \n", "1 0.051053 -0.033733 \n", "2 0.044212 0.164668 \n", "3 -0.236208 -0.055084 \n", "4 -0.020058 0.085772 \n", "\n", " Cells_AreaShape_Orientation Cells_AreaShape_Perimeter \\\n", "0 -0.037928 0.525950 \n", "1 0.064711 0.298818 \n", "2 -0.130904 0.001828 \n", "3 -0.099876 0.503035 \n", "4 0.096394 0.545575 \n", "\n", " Cells_AreaShape_Solidity ... Nuclei_Texture_SumEntropy_DNA_10_0 \\\n", "0 0.072795 ... 0.099029 \n", "1 0.145941 ... -0.042474 \n", "2 0.094736 ... 0.097992 \n", "3 0.154239 ... 0.389063 \n", "4 0.214587 ... 0.610344 \n", "\n", " Nuclei_Texture_SumEntropy_ER_10_0 Nuclei_Texture_SumEntropy_Mito_3_0 \\\n", "0 0.271039 -0.010427 \n", "1 0.254293 -0.058560 \n", "2 0.230331 0.029891 \n", "3 0.208347 0.015408 \n", "4 0.426826 0.118873 \n", "\n", " Nuclei_Texture_SumEntropy_RNA_5_0 Nuclei_Texture_SumVariance_ER_10_0 \\\n", "0 0.118997 0.006318 \n", "1 0.065822 -0.089380 \n", "2 0.079777 0.020954 \n", "3 0.339100 0.031178 \n", "4 0.415623 0.339794 \n", "\n", " Nuclei_Texture_SumVariance_RNA_10_0 Nuclei_Texture_Variance_AGP_10_0 \\\n", "0 -0.024573 -0.362861 \n", "1 -0.041392 -0.220610 \n", "2 -0.089633 0.075818 \n", "3 0.103611 -0.070876 \n", "4 0.238471 -0.157253 \n", "\n", " Nuclei_Texture_Variance_ER_10_0 Nuclei_Texture_Variance_Mito_10_0 \\\n", "0 -0.199743 -0.329832 \n", "1 -0.154711 -0.116072 \n", "2 -0.021001 -0.028661 \n", "3 -0.044751 -0.009591 \n", "4 -0.002357 -0.057382 \n", "\n", " Nuclei_Texture_Variance_RNA_10_0 \n", "0 0.066800 \n", "1 0.174417 \n", "2 0.115051 \n", "3 0.322316 \n", "4 0.437593 \n", "\n", "[5 rows x 605 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 把最后2列移动到前面2列\n", "\n", "cols = CP_CSV_matched_filter.columns.tolist()\n", "cols = cols[-2:] + cols[:-2]\n", "CP_CSV_matched_filter = CP_CSV_matched_filter[cols]\n", "CP_CSV_matched_filter.head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(18540, 605)\n" ] }, { "data": { "text/html": [ "
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110.00COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C242770.0413800.0509620.051053-0.0337330.0647110.2988180.145941...-0.0424740.254293-0.0585600.065822-0.089380-0.041392-0.220610-0.154711-0.1160720.174417
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5 rows × 605 columns

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" ], "text/plain": [ " dose SMILES Metadata_Plate \\\n", "0 6.05 CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC 24277 \n", "1 10.00 COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C 24277 \n", "2 10.00 COc1cc(O)cc(\\C=C\\c2ccccc2)c1 24277 \n", "3 10.00 COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]... 24277 \n", "4 4.39 CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@... 24277 \n", "\n", " Cells_AreaShape_Center_X Cells_AreaShape_Compactness \\\n", "0 -0.070346 0.256710 \n", "1 0.041380 0.050962 \n", "2 -0.130346 -0.098185 \n", "3 -0.316555 0.410722 \n", "4 -0.047587 0.248559 \n", "\n", " Cells_AreaShape_Extent Cells_AreaShape_FormFactor \\\n", "0 -0.114990 -0.237090 \n", "1 0.051053 -0.033733 \n", "2 0.044212 0.164668 \n", "3 -0.236208 -0.055084 \n", "4 -0.020058 0.085772 \n", "\n", " Cells_AreaShape_Orientation Cells_AreaShape_Perimeter \\\n", "0 -0.037928 0.525950 \n", "1 0.064711 0.298818 \n", "2 -0.130904 0.001828 \n", "3 -0.099876 0.503035 \n", "4 0.096394 0.545575 \n", "\n", " Cells_AreaShape_Solidity ... Nuclei_Texture_SumEntropy_DNA_10_0 \\\n", "0 0.072795 ... 0.099029 \n", "1 0.145941 ... -0.042474 \n", "2 0.094736 ... 0.097992 \n", "3 0.154239 ... 0.389063 \n", "4 0.214587 ... 0.610344 \n", "\n", " Nuclei_Texture_SumEntropy_ER_10_0 Nuclei_Texture_SumEntropy_Mito_3_0 \\\n", "0 0.271039 -0.010427 \n", "1 0.254293 -0.058560 \n", "2 0.230331 0.029891 \n", "3 0.208347 0.015408 \n", "4 0.426826 0.118873 \n", "\n", " Nuclei_Texture_SumEntropy_RNA_5_0 Nuclei_Texture_SumVariance_ER_10_0 \\\n", "0 0.118997 0.006318 \n", "1 0.065822 -0.089380 \n", "2 0.079777 0.020954 \n", "3 0.339100 0.031178 \n", "4 0.415623 0.339794 \n", "\n", " Nuclei_Texture_SumVariance_RNA_10_0 Nuclei_Texture_Variance_AGP_10_0 \\\n", "0 -0.024573 -0.362861 \n", "1 -0.041392 -0.220610 \n", "2 -0.089633 0.075818 \n", "3 0.103611 -0.070876 \n", "4 0.238471 -0.157253 \n", "\n", " Nuclei_Texture_Variance_ER_10_0 Nuclei_Texture_Variance_Mito_10_0 \\\n", "0 -0.199743 -0.329832 \n", "1 -0.154711 -0.116072 \n", "2 -0.021001 -0.028661 \n", "3 -0.044751 -0.009591 \n", "4 -0.002357 -0.057382 \n", "\n", " Nuclei_Texture_Variance_RNA_10_0 \n", "0 0.066800 \n", "1 0.174417 \n", "2 0.115051 \n", "3 0.322316 \n", "4 0.437593 \n", "\n", "[5 rows x 605 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 改名:Metadata_mmoles_per_liter2列改为does\n", "CP_CSV_matched_filter = CP_CSV_matched_filter.rename(columns={'Metadata_mmoles_per_liter2': 'dose', 'Metadata_pert_iname2': 'pert_iname'})\n", "\n", "print(CP_CSV_matched_filter.shape)\n", "CP_CSV_matched_filter.head()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "CP_CSV_matched_filter.to_csv('./CP_data.csv', index=False) \n", "\n", "# CP_CSV_matched_filter.to_parquet('./CP_data.parquet', index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 下面就不用看了,是基因表达部分的" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 处理基因表达" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0-0.118505-0.293445-0.2940880.292745-0.1458960.973580.247915-0.159028-1.2490490.357185...50.0PAC001_U2OS_6H_X1_B1_UNI4445LBRD-K07762753aminopurvalanol ABIOCC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...BRD-K07762753_50.0BRD-K07762753-001-03-6_50.0trtNaN
10.043264-0.264945-0.0006330.0009350.177604-0.504720.2143150.1442710.106551-0.051834...12.7PAC001_U2OS_6H_X1_B1_UNI4445LBRD-K09991945GSK-3 inhibitor IIBIOIc1cccc(CSc2nnc(o2)-c2ccncc2)c1BRD-K09991945_12.7BRD-K09991945-001-02-0_12.7trtNaN
2-0.0708050.1957550.004606-0.040855-0.067346-0.19872-0.0531950.167971-0.215449-0.273714...50.0PAC001_U2OS_6H_X1_B1_UNI4445LBRD-K46678324RHO-kinase inhibitor IIBIOClc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1BRD-K46678324_50.0BRD-K46678324-001-03-7_50.0trtNaN
30.027165-0.1495450.173113-0.100695-0.309296-0.20782-0.3239850.294872-0.371449-0.099914...16.2PAC001_U2OS_6H_X1_B1_UNI4445LBRD-K67860401GSK-3beta inhibitor VIIIBIOCOc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1BRD-K67860401_16.2BRD-K67860401-001-02-3_16.2trtNaN
40.3032940.254455-0.055418-0.0536350.000455-0.109080.0314250.0444820.2233510.163186...12.5PAC001_U2OS_6H_X1_B1_UNI4445LBRD-K52620403STO 609BIOOC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23BRD-K52620403_12.5BRD-K52620403-001-01-8_12.5trtNaN
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5 rows × 988 columns

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" ], "text/plain": [ " 221227_x_at 212345_s_at 218597_s_at 217140_s_at 209253_at 214404_x_at \\\n", "0 -0.118505 -0.293445 -0.294088 0.292745 -0.145896 0.97358 \n", "1 0.043264 -0.264945 -0.000633 0.000935 0.177604 -0.50472 \n", "2 -0.070805 0.195755 0.004606 -0.040855 -0.067346 -0.19872 \n", "3 0.027165 -0.149545 0.173113 -0.100695 -0.309296 -0.20782 \n", "4 0.303294 0.254455 -0.055418 -0.053635 0.000455 -0.10908 \n", "\n", " 219888_at 201225_s_at 202535_at 219499_at ... pert_dose \\\n", "0 0.247915 -0.159028 -1.249049 0.357185 ... 50.0 \n", "1 0.214315 0.144271 0.106551 -0.051834 ... 12.7 \n", "2 -0.053195 0.167971 -0.215449 -0.273714 ... 50.0 \n", "3 -0.323985 0.294872 -0.371449 -0.099914 ... 16.2 \n", "4 0.031425 0.044482 0.223351 0.163186 ... 12.5 \n", "\n", " det_plate BROAD_CPD_ID CPD_NAME \\\n", "0 PAC001_U2OS_6H_X1_B1_UNI4445L BRD-K07762753 aminopurvalanol A \n", "1 PAC001_U2OS_6H_X1_B1_UNI4445L BRD-K09991945 GSK-3 inhibitor II \n", "2 PAC001_U2OS_6H_X1_B1_UNI4445L BRD-K46678324 RHO-kinase inhibitor II \n", "3 PAC001_U2OS_6H_X1_B1_UNI4445L BRD-K67860401 GSK-3beta inhibitor VIII \n", "4 PAC001_U2OS_6H_X1_B1_UNI4445L BRD-K52620403 STO 609 \n", "\n", " CPD_TYPE CPD_SMILES \\\n", "0 BIO CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C... \n", "1 BIO Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1 \n", "2 BIO Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1 \n", "3 BIO COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1 \n", "4 BIO OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23 \n", "\n", " pert_id_dose pert_sample_dose pert_type control_type \n", "0 BRD-K07762753_50.0 BRD-K07762753-001-03-6_50.0 trt NaN \n", "1 BRD-K09991945_12.7 BRD-K09991945-001-02-0_12.7 trt NaN \n", "2 BRD-K46678324_50.0 BRD-K46678324-001-03-7_50.0 trt NaN \n", "3 BRD-K67860401_16.2 BRD-K67860401-001-02-3_16.2 trt NaN \n", "4 BRD-K52620403_12.5 BRD-K52620403-001-01-8_12.5 trt NaN \n", "\n", "[5 rows x 988 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "GE_CSV.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
221227_x_at212345_s_at218597_s_at217140_s_at209253_at214404_x_at219888_at201225_s_at202535_at219499_at...211071_s_at203341_at202801_at206414_s_at204978_at205379_at203897_atBROAD_CPD_IDCPD_NAMECPD_SMILES
0-0.118505-0.293445-0.2940880.292745-0.1458960.973580.247915-0.159028-1.2490490.357185...-0.97559-1.7406050.3403820.4987780.3521520.535910.52484BRD-K07762753aminopurvalanol ACC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...
10.043264-0.264945-0.0006330.0009350.177604-0.504720.2143150.1442710.106551-0.051834...0.25051-0.025625-0.024428-0.2251220.533052-0.19329-0.25436BRD-K09991945GSK-3 inhibitor IIIc1cccc(CSc2nnc(o2)-c2ccncc2)c1
2-0.0708050.1957550.004606-0.040855-0.067346-0.19872-0.0531950.167971-0.215449-0.273714...0.056810.1306950.005919-0.408722-0.057177-0.256990.31004BRD-K46678324RHO-kinase inhibitor IIClc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1
30.027165-0.1495450.173113-0.100695-0.309296-0.20782-0.3239850.294872-0.371449-0.099914...-0.161290.365195-0.040848-0.2511220.0407320.17091-0.05144BRD-K67860401GSK-3beta inhibitor VIIICOc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1
40.3032940.254455-0.055418-0.0536350.000455-0.109080.0314250.0444820.2233510.163186...0.007840.093595-0.3034180.474878-0.022947-0.285890.06423BRD-K52620403STO 609OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23
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5 rows × 980 columns

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" ], "text/plain": [ " 221227_x_at 212345_s_at 218597_s_at 217140_s_at 209253_at 214404_x_at \\\n", "0 -0.118505 -0.293445 -0.294088 0.292745 -0.145896 0.97358 \n", "1 0.043264 -0.264945 -0.000633 0.000935 0.177604 -0.50472 \n", "2 -0.070805 0.195755 0.004606 -0.040855 -0.067346 -0.19872 \n", "3 0.027165 -0.149545 0.173113 -0.100695 -0.309296 -0.20782 \n", "4 0.303294 0.254455 -0.055418 -0.053635 0.000455 -0.10908 \n", "\n", " 219888_at 201225_s_at 202535_at 219499_at ... 211071_s_at 203341_at \\\n", "0 0.247915 -0.159028 -1.249049 0.357185 ... -0.97559 -1.740605 \n", "1 0.214315 0.144271 0.106551 -0.051834 ... 0.25051 -0.025625 \n", "2 -0.053195 0.167971 -0.215449 -0.273714 ... 0.05681 0.130695 \n", "3 -0.323985 0.294872 -0.371449 -0.099914 ... -0.16129 0.365195 \n", "4 0.031425 0.044482 0.223351 0.163186 ... 0.00784 0.093595 \n", "\n", " 202801_at 206414_s_at 204978_at 205379_at 203897_at BROAD_CPD_ID \\\n", "0 0.340382 0.498778 0.352152 0.53591 0.52484 BRD-K07762753 \n", "1 -0.024428 -0.225122 0.533052 -0.19329 -0.25436 BRD-K09991945 \n", "2 0.005919 -0.408722 -0.057177 -0.25699 0.31004 BRD-K46678324 \n", "3 -0.040848 -0.251122 0.040732 0.17091 -0.05144 BRD-K67860401 \n", "4 -0.303418 0.474878 -0.022947 -0.28589 0.06423 BRD-K52620403 \n", "\n", " CPD_NAME CPD_SMILES \n", "0 aminopurvalanol A CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C... \n", "1 GSK-3 inhibitor II Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1 \n", "2 RHO-kinase inhibitor II Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1 \n", "3 GSK-3beta inhibitor VIII COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1 \n", "4 STO 609 OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23 \n", "\n", "[5 rows x 980 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "GE_CSV_filter = GE_CSV.drop(columns=['CPD_TYPE','det_plate', 'pert_id', 'pert_dose'])\n", "GE_CSV_filter.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BROAD_CPD_IDCPD_NAMECPD_SMILES221227_x_at212345_s_at218597_s_at217140_s_at209253_at214404_x_at219888_at...218397_at202996_at204608_at211071_s_at203341_at202801_at206414_s_at204978_at205379_at203897_at
0BRD-K07762753aminopurvalanol ACC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...-0.118505-0.293445-0.2940880.292745-0.1458960.973580.247915...-0.5299050.8049920.430269-0.97559-1.7406050.3403820.4987780.3521520.535910.52484
1BRD-K09991945GSK-3 inhibitor IIIc1cccc(CSc2nnc(o2)-c2ccncc2)c10.043264-0.264945-0.0006330.0009350.177604-0.504720.214315...0.228895-0.3813080.5569690.25051-0.025625-0.024428-0.2251220.533052-0.19329-0.25436
2BRD-K46678324RHO-kinase inhibitor IIClc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1-0.0708050.1957550.004606-0.040855-0.067346-0.19872-0.053195...-0.031795-0.051728-0.0177410.056810.1306950.005919-0.408722-0.057177-0.256990.31004
3BRD-K67860401GSK-3beta inhibitor VIIICOc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc10.027165-0.1495450.173113-0.100695-0.309296-0.20782-0.323985...-0.013787-0.1776080.219869-0.161290.365195-0.040848-0.2511220.0407320.17091-0.05144
4BRD-K52620403STO 609OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c230.3032940.254455-0.055418-0.0536350.000455-0.109080.031425...-0.052585-0.1961080.2454690.007840.093595-0.3034180.474878-0.022947-0.285890.06423
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5 rows × 980 columns

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" ], "text/plain": [ " BROAD_CPD_ID CPD_NAME \\\n", "0 BRD-K07762753 aminopurvalanol A \n", "1 BRD-K09991945 GSK-3 inhibitor II \n", "2 BRD-K46678324 RHO-kinase inhibitor II \n", "3 BRD-K67860401 GSK-3beta inhibitor VIII \n", "4 BRD-K52620403 STO 609 \n", "\n", " CPD_SMILES 221227_x_at \\\n", "0 CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C... -0.118505 \n", "1 Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1 0.043264 \n", "2 Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1 -0.070805 \n", "3 COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1 0.027165 \n", "4 OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23 0.303294 \n", "\n", " 212345_s_at 218597_s_at 217140_s_at 209253_at 214404_x_at 219888_at \\\n", "0 -0.293445 -0.294088 0.292745 -0.145896 0.97358 0.247915 \n", "1 -0.264945 -0.000633 0.000935 0.177604 -0.50472 0.214315 \n", "2 0.195755 0.004606 -0.040855 -0.067346 -0.19872 -0.053195 \n", "3 -0.149545 0.173113 -0.100695 -0.309296 -0.20782 -0.323985 \n", "4 0.254455 -0.055418 -0.053635 0.000455 -0.10908 0.031425 \n", "\n", " ... 218397_at 202996_at 204608_at 211071_s_at 203341_at 202801_at \\\n", "0 ... -0.529905 0.804992 0.430269 -0.97559 -1.740605 0.340382 \n", "1 ... 0.228895 -0.381308 0.556969 0.25051 -0.025625 -0.024428 \n", "2 ... -0.031795 -0.051728 -0.017741 0.05681 0.130695 0.005919 \n", "3 ... -0.013787 -0.177608 0.219869 -0.16129 0.365195 -0.040848 \n", "4 ... -0.052585 -0.196108 0.245469 0.00784 0.093595 -0.303418 \n", "\n", " 206414_s_at 204978_at 205379_at 203897_at \n", "0 0.498778 0.352152 0.53591 0.52484 \n", "1 -0.225122 0.533052 -0.19329 -0.25436 \n", "2 -0.408722 -0.057177 -0.25699 0.31004 \n", "3 -0.251122 0.040732 0.17091 -0.05144 \n", "4 0.474878 -0.022947 -0.28589 0.06423 \n", "\n", "[5 rows x 980 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 把最后3列换成前面3列\n", "\n", "cols = GE_CSV_filter.columns.tolist()\n", "cols = cols[-3:] + cols[:-3]\n", "GE_CSV_filter = GE_CSV_filter[cols]\n", "GE_CSV_filter.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "GE_CSV_filter.to_csv('./GE_data.csv', index=False) " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6929, 980)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "GE_CSV_filter.shape" ] } ], "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 }