• Step 1: Download cell images
  • Batch and Plate metadata
    • 2020_11_04_CPJUMP1
    • 2020_11_18_CPJUMP1_TimepointDay1
    • 2020_11_19_TimepointDay4
    • 2020_12_02_CPJUMP1_2WeeksTimePoint
    • 2020_12_07_CPJUMP1_4WeeksTimePoint
    • 2020_12_08_CPJUMP1_Bleaching
  • Image metadata
  • Plate map and Perturbation Metadata
• Step 2: Extract features using CellProfiler and DeepProfiler
• Step 3: Process the profiles using pycytominer
• Step 4: Run the analysis script
• Data Organization
• Maintenance plan
• Compute resources
• License

Table of contents generated with markdown-toc

Features from the cell images were extracted using CellProfiler and the single cell profiles were aggregated, annotated, normalized and feature selected using pycytominer. Features were also extracted using DeepProfiler which were annotated and spherized. The resulting profiles were analyzed using the notebooks in this repo. Steps for reproducing the data in this repository are outlined below.

Cell images are available on a S3 bucket. The images can be downloaded using the command

aws s3 cp \
  --recursive \
  s3://cellpainting-gallery/jump-pilots/source_4/images/ . 

You can test out download for a single file using:

suffix=images/2020_11_04_CPJUMP1/images/BR00117010__2020-11-08T18_18_00-Measurement1/Images/r01c01f01p01-ch1sk1fk1fl1.tiff

aws s3 cp \
  s3://cellpainting-gallery/jump-pilot/source_4/${suffix} \
  .

See this wiki for sample Cell Painting images and the meaning of (CellProfiler-derived) Cell Painting features.

There are six batches of data - 2020_11_04_CPJUMP1, 2020_11_18_CPJUMP1_TimepointDay1, 2020_11_19_TimepointDay4, 2020_12_02_CPJUMP1_2WeeksTimePoint, 2020_12_07_CPJUMP1_4WeeksTimePoint and 2020_12_08_CPJUMP1_Bleaching.

51 384-well plates of cells from two cell lines treated with different types of perturbations at different time points.

Eight ORF plates imaged one day after staining.

Eight ORF plates imaged four days after staining.

Eight ORF plates imaged 14 days after staining.

Eight ORF plates imaged 28 days after staining.

Four compound plates imaged and additional six times (A, B, C, D, E and F).

The folder for each 384-well plate typically contains images from nine sites for each well (for some wells 7,8 or 16 sites were imaged). The (x,y) coordinates of sites are available in the Metadata_PositionX and Metadata_PositionY columns of the load_data.csv.gz files in the load_data_csv folder. There are eight images per site (five from the fluorescent channels and three brightfield images). The names of the image files follow the naming convention - rXXcXXfXXp01-chXXsk1fk1fl1.tiff where

• rXX is the row number of the well that was imaged. rXX ranges from r01 to r16.
• cXX is the column number of the well that was imaged. cXX ranges from c01 to c24.
• fXX corresponds to the site that was imaged. fXX ranges from f01 to f16.
• chXX corresponds to the fluorescent channels imaged. chXX ranges from ch01 to ch08.
  • ch01 - Alexa 647
  • ch02 - Alexa 568
  • ch03 - Alexa 488 long
  • ch04 - Alexa 488
  • ch05 - Hoechst 33342
  • ch06-8 - three brighfield z planes.

Cell bounding boxes and segmentation masks have not been provided.

Plate map and Metadata are available in the metadata/ folder and also from https://github.com/jump-cellpainting/JUMP-Target.

Use the CellProfiler pipelines in pipelines/2020_11_04_CPJUMP1 and follow the instructions in the profiling handbook up until chapter 5.3 to generate the well-level aggregated CellProfiler profiles from the cell images.

Follow the README.md to extract features from a pretrained neural network using DeepProfiler

Pycytominer adds metadata from metadata/moa to the well-level aggregated profiles, normalizes the profiles to the whole plate and to the negative controls, separately and filters out invariant and redundant features.

To reproduce the profiles, clone this repo, download the files and activate the conda environment, after installing Miniconda, with the commands

git clone https://github.com/jump-cellpainting/neurips-cpjump1
cd neurips-cpjump1
git lfs pull
git submodule update --init --recursive
conda env create --force --file environment.yml
conda activate profiling

Then run the pycytominer workflow with the command

./run.sh

This creates the profiles in the profiles/ folder for all the plates in each batch. The folder for each plate contains the following files

The benchmark scripts compute Percent Replicating which is a measure of signature strength, Percent Matching across modalities which is a measure of how well the chemical and genetic perturbation profiles match. These metrics are calculated using the Feature selected normalized to negative control profiles (well-level profiles).

In the case of features extracted using DeepProfiler, the annotated features are spherized and Percent Replicating is calculated on these profiles.

To run the benchmark script activate the conda environment in benchmark/

conda env create --force --file benchmark/environment.yml
conda activate benchmark

Then run the jupyter notebooks (benchmark/0.percent_matching.ipynb and benchmark/1.percent_matching_across_modalities.ipynb) to create the figures in benchmark/figues/ and the tables in benchmark/README.md.

The following is the description of contents of the relevant folders in this repo.

• benchmark - contains the notebooks for reproducing the benchmark scores and figures
• config_files - contains the config files required for processing the profiles with pycytominer
• datasplit - contains the recommended data splits
• deep_profiles - contains the config files, functions and instructions for extracting DeepProfiler features
• example_images - contains single-site, all channel images from ten example wells
• load_data_csv - contains file location and other image metadata for each plate in all batches
• metadata - contains the perturbation metadata and plate maps
• pipelines - contains the CellProfiler pipelines for cell segmentation and feature extraction
• profiles - contains both CellProfiler and DeepProfiler extracted features for all batches
• profiling-recipe - contains the scripts that for running the pycytominer pipeline for processing profiles
• visualization - contains notebooks for generating plate map and clinical phase status visualization figures
• environment.yml - conda environment for running pycytominer pipeline
• run.sh - runs the pycytominer pipeline for processing profiles
• maintenace_plan.md - contains our maintenance plan for this dataset

We have provided our maintenance plan in maintenance_plan.md.

For segmentation and feature extraction, each plate of images took on average 30 minutes to process, using a fleet of 200 m4.xlarge spot instances (800 vCPUs), which cost approximately $10 per plate. Aggregation into mean profiles takes 12-18 hours, though can be parallelized onto a single large machine, at the total cost of <$1 per plate. For profile processing with pycytominer, each plate took under two minutes, using a local machine (Intel Core i9 with 16 GB memory)

DeepProfiler took around 8 hours to extract features from ~280.000 images in a p3.2xlarge with a single Tesla V100-SXM2 GPU. Note that cells location were previously precomputed with the CellProfiler segmentation pipeline.

We use a dual license in this repository. We license the source code as BSD 3-Clause, and license the data, results, and figures as CC0 1.0.