pyelispotanalysis
Detecting spots (IFN-Gamma positive cells) from an Elispot assay image using Deep learning
Science Score: 67.0%
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Repository
Detecting spots (IFN-Gamma positive cells) from an Elispot assay image using Deep learning
Basic Info
- Host: GitHub
- Owner: ajinkya-kulkarni
- License: gpl-3.0
- Language: Python
- Default Branch: main
- Homepage: https://elispotanalysis.streamlit.app/
- Size: 1.2 MB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 1
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Metadata Files
README.md
PyElispotAnalysis
Detecting spots (IFN-Gamma positive cells) from an Elispot assay image
App Overview
A web application developed using Streamlit is available at https://elispot-analysis.streamlit.app/
Overview
PyElispotAnalysis is a Streamlit web application that provides a user-friendly interface for the automated analysis of Elispot assay images. Developed with a focus on ease of use and accuracy, it allows for the rapid quantification of spots within an assay image, facilitating the assessment of immune responses.
Features
Image Upload: Users can upload Elispot assay images in various formats, including .tif, .tiff, .png, .jpg, and .jpeg. Interactive Sliders: The app features interactive sliders for fine-tuning analysis parameters such as local window size, sensitivity for spot detection, and minimum and maximum spot area. Automated Spot Detection: Utilizes adaptive thresholding and morphological operations to identify and count spots. Visualization: Offers side-by-side visualization of the original and processed images with detected spots highlighted. Histograms: Generates histograms for the distribution of spot sizes to provide insights into the range and density of the immune response. Detailed Reporting: Produces a downloadable report with quantitative data on each detected spot, including area and diameter. Responsive Design: Crafted to work on various devices with an intuitive layout that adapts to different screen sizes.
Workflow
Image Processing: Upon image upload, the app processes the image, converting it to grayscale and resizing it to suit the user interface. Parameter Adjustment: Users can adjust analysis parameters using sliders to optimize spot detection according to their specific assay characteristics. Spot Detection: The app applies adaptive thresholding and morphological operations to detect spots, which are then filtered based on size criteria set by the user. Result Visualization: Detected spots are circled on the processed image, and the user can compare this with the original image using an interactive slider. Data Analysis: The app generates a histogram of spot sizes and a detailed report, including a table with metrics for each spot. Report Download: Users can download the processed image and a CSV file containing the detailed spot analysis.
Support and Contribution
Information on how to reach out for support, report bugs, or contribute to the project. Encourage contributions such as bug fixes, feature requests, and suggestions for improvement.
Owner
- Name: Ajinkya Kulkarni
- Login: ajinkya-kulkarni
- Kind: user
- Location: Göttingen
- Company: Max Planck Institute for Multidisciplinary Sciences
- Website: https://orcid.org/0000-0003-1423-3676
- Twitter: kulkajinkya
- Repositories: 5
- Profile: https://github.com/ajinkya-kulkarni
Image Data Scientist @mpi_nat working in Translational Oncology
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Kulkarni" given-names: "Ajinkya" orcid: "https://orcid.org/0000-0003-1423-3676" title: "PyElispotAnalysis" version: 1.0 doi: 10.5281/zenodo.10630700 date-released: 2024-02-07 url: "https://github.com/ajinkya-kulkarni/PyElispotAnalysis"
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Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Ajinkya Kulkarni | k****a@g****m | 13 |
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