Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/broadinstitute/luad-cell-painting

Repository for analysis of CellPainting data in the LUAD dataset

https://github.com/broadinstitute/luad-cell-painting

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    1 of 3 committers (33.3%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.0%) to scientific vocabulary

Keywords from Contributors

cytominer
Last synced: 10 months ago · JSON representation

Repository

Repository for analysis of CellPainting data in the LUAD dataset

Basic Info
Statistics
  • Stars: 7
  • Watchers: 8
  • Forks: 4
  • Open Issues: 0
  • Releases: 1
Created over 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

LUAD analysis using DeepProfiler

This repository contains the source code to run the cmVIP analysis in the LUAD dataset. Our internal repository is here.

Profiling

1. Install requirements

This folder is a DeepProfiler project. Experiments reported in the paper used the c91b9d8 commit.

To install the dependencies, including the DeepProfiler version we used, run: bash $ pip install -r requirements.txt

2. Download the data

Be aware this script will override any previous data. To download the data run:

bash $ utils/download_all.sh

3. Prepare the data.

  1. Run extract_locations.py script to generate location files.

  2. Use DeepProfiler to prepare the dataset:

bash $ python3 -m deepprofiler --root=./ --config luad.json --gpu 0 prepare

--gpu option sets the GPU id to use.

4. Extract features.

Use DeepProfiler to extract features:

bash $ python3 -m deepprofiler --gpu 0 --exp efn_pretrained --root ./ --config luad.json profile

5. Create well profiles.

To create the well-based profiles run:

bash $ python3 utils/create_profiles.py

It will write a pd.DataFrame in parquet with profiles.

VIP analysis

The analysis is split in three notebooks: - 1-Expression-VIP.ipynb: Run the baseline analysis using L1000 profiling. - 2-Cell-Morphology-VIP.ipynb: Run the Cell Morphology VIP method. - 3-Aggregation-plots.ipynb: Create the plots summarizing results.

Notes about the dataset

From the paper:

An additional 88 constructs are included in the dataset, representing TP53 alleles that inadvertently had double mutations. A comprehensive description of the process for selecting the constructs that were analyzed is presented in Supplementary Figure 2.

We have filtered out these constructs in the Filter quality control status section of the 2-Cell-Morphology-VIP.ipynb notebook.

Owner

  • Name: Broad Institute
  • Login: broadinstitute
  • Kind: organization
  • Location: Cambridge, MA

Broad Institute of MIT and Harvard

GitHub Events

Total
  • Issue comment event: 1
  • Push event: 1
  • Pull request event: 1
Last Year
  • Issue comment event: 1
  • Push event: 1
  • Pull request event: 1

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 15
  • Total Committers: 3
  • Avg Commits per committer: 5.0
  • Development Distribution Score (DDS): 0.2
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
John Arevalo j****o@g****m 12
Juan C j****o@g****m 2
Shantanu Singh s****h@b****g 1
Committer Domains (Top 20 + Academic)
broadinstitute.org: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total 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
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
View more stats
Top Authors
Issue Authors
  • shntnu (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Dependencies

requirements.txt pypi
  • ipywidgets ==7.6.
  • kaleido ==0.2.
  • notebook ==6.4.
  • openpyxl ==3.0.
  • pandas ==1.3.
  • plotly ==5.3.
  • pyarrow ==6.0.
  • scipy ==1.7.
  • statsmodels ==0.13.
  • tqdm ==4.62.
  • umap-learn ==0.5.