Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks
SND-ID: 2024-175. Version: 1. DOI: https://doi.org/10.48723/srtg-ss33
Associated documentation
Citation
Creator/Principal investigator(s)
Osheen Sharma
- Karolinska Institutet, Department of Oncology-Pathology
Brinton Seashore-Ludlow
- Karolinska Institutet, Department of Oncology and Pathology
Research principal
Karolinska Institutet
- Department of Oncology and Pathology
Description
This dataset provides detailed imaging data from various co-culture assays of ovarian cancer and fibroblast cell lines, treated with a wide range of drugs. The structured organization and comprehensive naming conventions allow for easy navigation and analysis of the data. The images are treated with 528 drugs from FIMM Oncology Library to study the drug effect on cancer cell morphology in presence of fibroblasts.
The dataset comprises images from 2D coculture high-content screening data in .tiff format. The images were acquired using the Opera Phenix at a 10x magnification. It includes a total of 245,760 raw images (including 4 field of views), each with a resolution of 1080x1080 pixels. For initial analysis, the images were read directly into CellProfiler, a software platform designed for high-throughput image analysis. To facilitate neural network processing, each image was converted into a NumPy array using Python 3 and the Python Imaging Library (PIL).
The data set is available for download in five separate ZIP archives, Kuramochi_BjhTERT.zip (93.68 GB), Kuramochi_WI38.zip (93.50 GB), M
The dataset comprises images from 2D coculture high-content screening data in .tiff format. The images were acquired using the Opera Phenix at a 10x magnification. It includes a total of 245,760 raw images (including 4 field of views), each with a resolution of 1080x1080 pixels. For initial analysis, the images were read directly into CellProfiler, a software platform designed for high-throughput image analysis. To facilitate neural network processing, each image was converted into a NumPy array using Python 3 and the Python Imaging Library (PIL).
The data set is available for download in five separate ZIP archives, Kuramochi_BjhTERT.zip (93.68 GB), Kuramochi_WI38.zip (93.50 GB), MH_BjhTERT.zip (62.22 GB), OvCar3_BjhTERT.zip (84.98 GB), OvCar8_WI38.zip (91.89 GB).
For a description on the file structure, see associated documentation file Dataset_Description.pdf. Show less..
Data contains personal data
No
Language
Unit of analysis
Population
This dataset consists of images of ovarian cancer cell lines and fibroblasts cell lines grown together (2D coculture) in a 384 well microtiter plate (in vitro). In our analysis each single cell is an object. These cocultures are used to study how cancer and fibroblasts cells interact with each other and upon drug purturbation and how the morphology of cancer cells changes.
Time Method
Study design
Experimental study
Sampling procedure
Data format / data structure
Responsible department/unit
Department of Oncology and Pathology
Contributor(s)
Greta Gudoitytė - Karolinska Institutet, Department of Oncology and Pathology
Research area
Bioinformatics (computational biology) (Standard för svensk indelning av forskningsämnen 2011)
Computer vision and robotics (autonomous systems) (Standard för svensk indelning av forskningsämnen 2011)
Cell biology (Standard för svensk indelning av forskningsämnen 2011)
Bioinformatics and systems biology (Standard för svensk indelning av forskningsämnen 2011)
Cancer and oncology (Standard för svensk indelning av forskningsämnen 2011)