https://github.com/broadinstitute/profiling-resistance-mechanisms
Predicting pharmacodynamic responses to cancer drugs using cell morphology
https://github.com/broadinstitute/profiling-resistance-mechanisms
Science Score: 57.0%
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Low similarity (10.8%) to scientific vocabulary
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Repository
Predicting pharmacodynamic responses to cancer drugs using cell morphology
Basic Info
- Host: GitHub
- Owner: broadinstitute
- License: bsd-3-clause
- Language: Jupyter Notebook
- Default Branch: master
- Size: 1.57 GB
Statistics
- Stars: 7
- Watchers: 8
- Forks: 5
- Open Issues: 27
- Releases: 2
Topics
Metadata Files
README.md
Discovering Morphological Markers of Drug Resistance
In this repository we analyze Cell Painting data generated from multiple cell line clones that were resistant or sensitive to bortezomib.
Citation
Kelley ME, Berman AY, Stirling DR, Cimini BA, Han Y, Singh S, Carpenter AE, Kapoor TM, Way GP. High-content microscopy reveals a morphological signature of bortezomib resistance. (2023) eLife; 12:e91362. DOI: https://doi.org/10.7554/eLife.91362.
Data collection and processing
We cultured a colon cancer cell line (HCT116), treated with a proteosome inhibitor (Bortezomib), and selected two resistant clones. We applied Cell Painting to these cell lines (in triplicate) under four conditions (DMSO, 0.7nm, 7nm, and 70nm Bortezomib).
The Cell Painting assay captures several cellular morphology features (described in more detail here). Our hypothesis was that morphological features could distinguish wildtype from resistant clones.
We processed the cell painting data using CellProfiler. We use CellProfiler to test quality control, segment images to extract nuclei, and measure features captured by cell painting.
This repository contains all image analysis pipelines and image-based profiling pipelines (see 0.generate-profiles).
Pilot analyses
This repository ingests the processed Cell Painting data and performs several downstream analyses.
Using the triplicate measurements, and two batches, we perform the following pilot analyses:
- Obtain similarity matrices for each batch independently and combined; perform hierarchical clustering; visualize heatmaps.
- Apply UMAP to the batched data to observe large differences across variables
- Apply t-tests to determine cell morphology differences between conditions:
- We test for differences between resistant clones at two doses of Bortezomib (
0.7nmand7nm) - We also test for differences between wildtype and resistant clones at a low dose of Bortezomib (
0.7nm)
- We test for differences between resistant clones at two doses of Bortezomib (
UMAP Batch Analysis

T-test to Determine Morphological Differences

Reproducibility
We use conda to manage package versions. After installing conda, obtain all required packages:
```bash conda env create --force --file environment.yml
Activate environment
conda activate resistance-mechansisms ```
Clone the github repository. First, generate and enable SSH Keys if you haven't already.
```bash
Then clone and enter repo
git clone git@github.com:broadinstitute/profiling-resistance-mechanisms cd profiling-resistance-mechanisms ```
All analyses are presented in analysis.sh.
To reproduce, perform the following:
bash
./analysis.sh
Bug Reporting
Please file an issue with any questions or bug reports.
Internal documents
Owner
- Name: Broad Institute
- Login: broadinstitute
- Kind: organization
- Location: Cambridge, MA
- Website: http://www.broadinstitute.org/
- Twitter: broadinstitute
- Repositories: 1,083
- Profile: https://github.com/broadinstitute
Broad Institute of MIT and Harvard
GitHub Events
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- Delete event: 1
- Push event: 1
- Pull request event: 1
Last Year
- Delete event: 1
- Push event: 1
- Pull request event: 1
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 78
- Total pull requests: 108
- Average time to close issues: 3 months
- Average time to close pull requests: 8 days
- Total issue authors: 2
- Total pull request authors: 2
- Average comments per issue: 1.79
- Average comments per pull request: 0.43
- Merged pull requests: 106
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- gwaybio (40)
- shntnu (3)
Pull Request Authors
- gwaybio (56)
- shntnu (1)
- DavidStirling (1)