iTRADE: image-based TRAnsfer learning for Drug Effects

<code>iTRADE: <strong>i</strong>mage-based <strong>TRA</strong>nsfer learning for <strong>D</strong>rug <strong>E</strong>ffects</code> This dataset supports <em>Berker et al. (2022) IEEE Trans Med Imaging</em>, https://doi.org/10.1109/TMI.2022.3205554. It comprises microscopy images, layout information and metabolic readouts for a total of 18 acquisitions: 1 control experiment (CE) and 17 drug screens (DS). <pre><code>. ├── Images ├── Layouts ├── MeanOfStack ├── Metabolic └── README.md 4 directories, 1 file </code></pre> <strong><code>Images</code>: microscopy images</strong> In total, the dataset contains 1 × 420 + 17 × 1848 = 31836 TIFF images (2123194264 bytes) in 18 folders. <pre><code>Images ├── BT-40_V2_DS1 ├── BT-40_V3_DS1 ├── BT-40_V3_DS2 ├── HD-MB03_V1_DS1 ├── HD-MB03_V1_DS2 ├── HD-MB03_V2_DS1 ├── HD-MB03_V2_DS2 ├── INF_R_1021_relapse1_V1_DS2 ├── INF_R_1025_primary_V2_DS1 ├── INF_R_1123_primary_V1_DS1 ├── INF_R_153_CE ├── INF_R_153_V2_DS1 ├── INF_R_153_V3_DS1 ├── NCI-H3122_V2_DS1 ├── SJ-GBM2_V2_DS1 ├── SMS-KCNR_V1_DS1 ├── SMS-KCNR_V2_DS1 └── SMS-KCNR_V2_DS2 18 directories, 0 files </code></pre> <em>Control experiment</em> The <code>INF_R_153_CE</code> folder contains images for a control experiment using the INF<em>R</em>153 cell line subjected to DMSO in 7 × 14 = 98 wells and staurosporine (STS) in 8 × 14 = 112 wells on a single 384-well plate (ignoring border wells). Each well is represented by 2 images (maximum intensity projection, <code>proj</code>, and mid-z image, <code>midz</code>), respectively. In total, this folder contains (7 + 8) × 14 × 2 = 420 images (26 megabytes). <pre><code>Images/INF_R_153_CE ├── [ 60K] INF_R_153_CE_P1_B05_midz_224x224.tif ├── [ 58K] INF_R_153_CE_P1_B05_proj_224x224.tif ┆ ... ├── [ 62K] INF_R_153_CE_P1_O23_midz_224x224.tif └── [ 59K] INF_R_153_CE_P1_O23_proj_224x224.tif 0 directories, 420 files </code></pre> <em>Drug screens</em> Other folders, such as <code>BT-40_V2_DS1</code>, are named for screen identifiers, which consist of a sample identifier (name of cell line, <code>INF_R_153|BT-40|HD-MB03|NCI-H3122|SJ-GBM2|SMS-KCNR</code>, or <em>INFORM</em> pseudonym of primary patient-derived sample, <code>INF_R_[0-9]{3,}_(primary|relapse[0-9])</code>) followed by <code>_V[0-9]_DS[0-9]</code> indicating the biological (<code>V</code>) and the technical (<code>DS</code>) replicate. For the three patient-derived samples included (<code>INF_R_[0-9]{4}</code>), <code>V1</code> signifies fresh viable tissue shipped immediately after biopsy while <code>V2</code> indicates a cell culture shipped after establishment. Each drug-screen folder contains images for a single drug screen consisting of 3 plates per screen, 14 × 22 = 308 wells per plate (namely, a 384-well plate ignoring all border wells), and 2 images per well. In total, each folder contains 3 × 14 × 22 × 2 = 1848 images (112 to 146 megabytes). <pre><code>Images/BT-40_V2_DS1 ├── [ 62K] BT-40_V2_DS1_P1_B02_midz_224x224.tif ├── [ 58K] BT-40_V2_DS1_P1_B02_proj_224x224.tif ┆ ... ├── [ 60K] BT-40_V2_DS1_P3_O23_midz_224x224.tif └── [ 54K] BT-40_V2_DS1_P3_O23_proj_224x224.tif 0 directories, 1848 files Images/BT-40_V3_DS1 ├── [ 64K] BT-40_V3_DS1_P1_B02_midz_224x224.tif ├── [ 61K] BT-40_V3_DS1_P1_B02_proj_224x224.tif ┆ ... ├── [ 63K] BT-40_V3_DS1_P3_O23_midz_224x224.tif └── [ 62K] BT-40_V3_DS1_P3_O23_proj_224x224.tif 0 directories, 1848 files ... </code></pre> <em>Image files</em> Image file names of the form <code>$ScreenID_P[123]_[B-O][0-9]{2}_(midz|proj)_[0-9]+x[0-9]+.tif</code> include the plate number (<code>1</code>, <code>2</code>, <code>3</code>), the well coordinates (<code>B02</code> to <code>O23</code>), the image type (<code>midz|proj</code>) and the size of the square images (<code>224x224</code>). Images are stored in Tagged Image File Format (TIFF), using a 16-bit integer in little-endian encoding for each pixel value. Files have been read from original TIFF image files and downscaled using the <code>Keras-Preprocessing</code> (v1.1.2) <code>load_img</code> function, and resaved (from plain image arrays without any metadata) using the <code>opencv-python</code> (v4.5.5.62) <code>imwrite</code> function using Adobe Deflate as a compression algorithm. <strong><code>Layouts</code>: layout information</strong> <pre><code>Layouts ├── Drugs.csv ├── Layout_CE.csv └── Layout_DS.csv 0 directories, 3 files </code></pre> Two text files, <code>Layout_CE.csv</code> and <code>Layout_DS.csv</code>, describe the layout of control experiments (<code>CE</code>) and drug screens (<code>DS</code>), respectively. Note that <code>Layout_DS.csv</code> has been generated from the imaging layout file published with the iTReX web app (available at https://itrex.kitz-heidelberg.de/), which can be downloaded from GitHub or iTReX. See <code>itrade.util.layouts.convert_itrex_ds_layout()</code> for details. A third text file, <code>Drugs.csv</code>, maps drug names as used in the iTReX-based <code>Layout_DS.csv</code> to drug names, abbreviations and drug (sub-)classes used throughout the manuscript. This file is used only by <code>plots.R</code>. All layout information is stored in long-table format. Text files are stored as Comma-Separated Values (CSV) with UTF-8 character encoding and Unix-style (<code>LF</code>) line endings. <strong><code>MeanOfStack</code>: mean-of-stack measurements</strong> For each drug screen represented by a folder named after the screen identifier, mean-of-stack computations produced from the full-resolution (<code>2048x2048</code> pixels) are stored in matrix format using one text file per plate, named <code>SID_P[123]_$Barcode.txt</code>, e.g., <code>BT-40_V2_DS1_P1_H104-03N1A98.txt</code>. Text files are stored as Tab-Separated Values (TSV) with UTF-8 character encoding and Unix-style (<code>LF</code>) line endings. This folder comprises a total of 17 × 3 = 51 text files. <pre><code>MeanOfStack ├── BT-40_V2_DS1 │ ├── BT-40_V2_DS1_P1_H104-03N1A98.txt │ ├── BT-40_V2_DS1_P2_H104-03N2A98.txt │ └── BT-40_V2_DS1_P3_H104-03N3A98.txt ├── BT-40_V3_DS1 │ ├── BT-40_V3_DS1_P1_H104-03N1D07.txt │ ├── BT-40_V3_DS1_P2_H104-03N2D07.txt │ └── BT-40_V3_DS1_P3_H104-03N3D07.txt ┆ ... └── SMS-KCNR_V2_DS2 ├── SMS-KCNR_V2_DS2_P1_H104-03N1D02.txt ├── SMS-KCNR_V2_DS2_P2_H104-03N2D02.txt └── SMS-KCNR_V2_DS2_P3_H104-03N3D02.txt 17 directories, 51 files </code></pre> <strong><code>Metabolic</code>: metabolic readouts</strong> Similar to mean-of-stack computations, metabolic readouts are included for each drug screen. Folder and file names and file formats are identical to the <code>MeanOfStack</code> folder. <pre><code>Metabolic ├── BT-40_V2_DS1 │ ├── BT-40_V2_DS1_P1_H104-03N1A98.txt │ ├── BT-40_V2_DS1_P2_H104-03N2A98.txt │ └── BT-40_V2_DS1_P3_H104-03N3A98.txt ├── BT-40_V3_DS1 │ ├── BT-40_V3_DS1_P1_H104-03N1D07.txt │ ├── BT-40_V3_DS1_P2_H104-03N2D07.txt │ └── BT-40_V3_DS1_P3_H104-03N3D07.txt ┆ ... └── SMS-KCNR_V2_DS2 ├── SMS-KCNR_V2_DS2_P1_H104-03N1D02.txt ├── SMS-KCNR_V2_DS2_P2_H104-03N2D02.txt └── SMS-KCNR_V2_DS2_P3_H104-03N3D02.txt 17 directories, 51 files </code></pre>**iTRADE：基于图像的迁移学习用于药物效应研究** 本数据集支撑Berker等人2022年发表于《IEEE Transactions on Medical Imaging》的研究工作，相关DOI为https://doi.org/10.1109/TMI.2022.3205554。 该数据集涵盖显微图像、孔板布局信息与代谢读数，共计18组采集数据，包含1组对照实验（CE）与17组药物筛选（DS）。其目录结构如下： <pre><code>. ├── Images ├── Layouts ├── MeanOfStack ├── Metabolic └── README.md 4 directories, 1 file </code></pre> **Images：显微图像** 本数据集总计包含31836张TIFF图像（总大小2123194264字节），存储于18个文件夹中，具体数量为1×420 + 17×1848 = 31836。该目录的子文件夹结构如下： <pre><code>Images ├── BT-40_V2_DS1 ├── BT-40_V3_DS1 ├── BT-40_V3_DS2 ├── HD-MB03_V1_DS1 ├── HD-MB03_V1_DS2 ├── HD-MB03_V2_DS1 ├── HD-MB03_V2_DS2 ├── INF_R_1021_relapse1_V1_DS2 ├── INF_R_1025_primary_V2_DS1 ├── INF_R_1123_primary_V1_DS1 ├── INF_R_153_CE ├── INF_R_153_V2_DS1 ├── INF_R_153_V3_DS1 ├── NCI-H3122_V2_DS1 ├── SJ-GBM2_V2_DS1 ├── SMS-KCNR_V1_DS1 ├── SMS-KCNR_V2_DS1 └── SMS-KCNR_V2_DS2 18 directories, 0 files </code></pre> **对照实验** `INF_R_153_CE`文件夹包含使用INF_R_153细胞系的对照实验图像。该实验在一块384孔板（忽略边缘孔）中设置了7×14=98个二甲基亚砜（DMSO）处理孔与8×14=112个星形孢菌素（STS）处理孔。每个孔对应2张图像：最大强度投影图（`proj`）与中层z平面图像（`midz`）。该文件夹总计包含(7 + 8)×14×2 = 420张图像，总大小约26MB。该文件夹内的文件结构如下： <pre><code>Images/INF_R_153_CE ├── [ 60K] INF_R_153_CE_P1_B05_midz_224x224.tif ├── [ 58K] INF_R_153_CE_P1_B05_proj_224x224.tif ┆ ... ├── [ 62K] INF_R_153_CE_P1_O23_midz_224x224.tif └── [ 59K] INF_R_153_CE_P1_O23_proj_224x224.tif 0 directories, 420 files </code></pre> **药物筛选** 其余文件夹（如`BT-40_V2_DS1`）以筛选标识符命名，其命名规则为：样本标识符（细胞系名称：`INF_R_153|BT-40|HD-MB03|NCI-H3122|SJ-GBM2|SMS-KCNR`，或患者来源原代样本的INFORM假名：`INF_R_[0-9]{3,}_(primary|relapse[0-9])`）后接`_V[0-9]_DS[0-9]`，其中`V`代表生物学重复，`DS`代表技术重复。对于3例患者来源样本（`INF_R_[0-9]{4}`），`V1`表示活检后立即运输的新鲜活组织，`V2`表示建系后运输的细胞培养物。每个药物筛选文件夹对应单次药物筛选实验，每轮筛选包含3块板，每块板有14×22=308个孔（即忽略边缘孔的384孔板），每个孔对应2张图像。因此每个文件夹总计包含3×14×22×2 = 1848张图像，大小为112~146MB。以`BT-40_V2_DS1`文件夹为例，其内部文件结构如下： <pre><code>Images/BT-40_V2_DS1 ├── [ 62K] BT-40_V2_DS1_P1_B02_midz_224x224.tif ├── [ 58K] BT-40_V2_DS1_P1_B02_proj_224x224.tif ┆ ... ├── [ 60K] BT-40_V2_DS1_P3_O23_midz_224x224.tif └── [ 54K] BT-40_V2_DS1_P3_O23_proj_224x224.tif 0 directories, 1848 files Images/BT-40_V3_DS1 ├── [ 64K] BT-40_V3_DS1_P1_B02_midz_224x224.tif ├── [ 61K] BT-40_V3_DS1_P1_B02_proj_224x224.tif ┆ ... ├── [ 63K] BT-40_V3_DS1_P3_O23_midz_224x224.tif └── [ 62K] BT-40_V3_DS1_P3_O23_proj_224x224.tif 0 directories, 1848 files ... </code></pre> **图像文件** 图像文件名遵循`$ScreenID_P[123]_[B-O][0-9]{2}_(midz|proj)_[0-9]+x[0-9]+.tif`格式，其中包含板编号（1、2、3）、孔坐标（B02至O23）、图像类型（`midz`或`proj`）以及正方形图像的分辨率（如224x224）。图像以标记图像文件格式（TIFF，Tagged Image File Format）存储，每个像素值采用16位小端序整数编码。所有图像均源自原始TIFF文件，使用`Keras-Preprocessing`（v1.1.2）的`load_img`函数进行下采样，再通过`opencv-python`（v4.5.5.62）的`imwrite`函数重新保存（仅保留纯图像数组，不包含元数据），压缩算法采用Adobe Deflate。 **Layouts：孔板布局信息** 该目录的结构如下： <pre><code>Layouts ├── Drugs.csv ├── Layout_CE.csv └── Layout_DS.csv 0 directories, 3 files </code></pre> 两个文本文件`Layout_CE.csv`与`Layout_DS.csv`分别描述对照实验（CE）与药物筛选（DS）的孔板布局。需注意，`Layout_DS.csv`源自iTReX网页应用（可从https://itrex.kitz-heidelberg.de/访问）配套发布的成像布局文件，该文件可从GitHub或iTReX平台下载，详细转换方法可参考`itrade.util.layouts.convert_itrex_ds_layout()`。第三个文本文件`Drugs.csv`用于将iTReX格式的`Layout_DS.csv`中的药物名称映射为论文中使用的药物全名、缩写以及药物（亚）类别，该文件仅由`plots.R`脚本调用。所有布局信息均以长表格式存储，文本文件采用UTF-8字符编码与Unix风格（LF）换行符的逗号分隔值（CSV）格式。 **MeanOfStack：堆叠均值测量结果** 针对每个以筛选标识符命名的药物筛选实验，从全分辨率（2048x2048像素）图像计算得到的堆叠均值结果将以矩阵格式存储，每块板对应一个文本文件，命名格式为`SID_P[123]_$Barcode.txt`，例如`BT-40_V2_DS1_P1_H104-03N1A98.txt`。文本文件采用UTF-8字符编码与Unix风格（LF）换行符的制表符分隔值（TSV）格式存储。该文件夹总计包含17×3 = 51个文本文件，其目录结构示例如下： <pre><code>MeanOfStack ├── BT-40_V2_DS1 │ ├── BT-40_V2_DS1_P1_H104-03N1A98.txt │ ├── BT-40_V2_DS1_P2_H104-03N2A98.txt │ └── BT-40_V2_DS1_P3_H104-03N3A98.txt ├── BT-40_V3_DS1 │ ├── BT-40_V3_DS1_P1_H104-03N1D07.txt │ ├── BT-40_V3_DS1_P2_H104-03N2D07.txt │ └── BT-40_V3_DS1_P3_H104-03N3D07.txt ┆ ... └── SMS-KCNR_V2_DS2 ├── SMS-KCNR_V2_DS2_P1_H104-03N1D02.txt ├── SMS-KCNR_V2_DS2_P2_H104-03N2D02.txt └── SMS-KCNR_V2_DS2_P3_H104-03N3D02.txt 17 directories, 51 files </code></pre> **Metabolic：代谢读数** 与堆叠均值计算结果类似，本数据集也为每个药物筛选实验提供了代谢读数。文件夹与文件名的命名规则、文件格式均与`MeanOfStack`文件夹完全一致，其目录结构示例如下： <pre><code>Metabolic ├── BT-40_V2_DS1 │ ├── BT-40_V2_DS1_P1_H104-03N1A98.txt │ ├── BT-40_V2_DS1_P2_H104-03N2A98.txt │ └── BT-40_V2_DS1_P3_H104-03N3A98.txt ├── BT-40_V3_DS1 │ ├── BT-40_V3_DS1_P1_H104-03N1D07.txt │ ├── BT-40_V3_DS1_P2_H104-03N2D07.txt │ └── BT-40_V3_DS1_P3_H104-03N3D07.txt ┆ ... └── SMS-KCNR_V2_DS2 ├── SMS-KCNR_V2_DS2_P1_H104-03N1D02.txt ├── SMS-KCNR_V2_DS2_P2_H104-03N2D02.txt └── SMS-KCNR_V2_DS2_P3_H104-03N3D02.txt 17 directories, 51 files </code></pre>