MitoCheck Data

Data

Access

All data are publicly available.

Confocal Microscopy

Data	Level	Location	Notes
Images	1	Image Data Resource (IDR)	Accession idr0013(screenA)

Repository Structure:

This repository is structured as follows:

Order	Module	Description
0.locate_data	Locate mitosis movies	Find locations (plate, well, frame) for training and control movies
1.idr_streams	Extract features from mitosis movies	Use idrstream to extract features from training and control movies
2.format_training_data	Format training data	Compile metadata, phenotypic class, and feature data for Mitocheck-labeled movies
3.normalize_data	Normalize data	Use UMAP to suggest batch effects are not dominant signal and normalize with data using negative controls as normalization population
4.analyze_data	Analyze data	Analyze normalized data

Other necessary folders/files:

Folder/File	Description
mitocheck_metadata	IDR curated metadata, trainingset file and features dataset necessary for locating Mitocheck-labeled training data
utils	Python files with functions used throughout repository
mitocheck_data_env.yml	Environment file with packages necessary to process mitocheck data

Training Data

As part of the Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes, Mitocheck created a training dataset with cell phenotypic classes and their locations. This dataset was provided by J.K. Hériché and is located in mitocheck_metadata. This dataset contains the following files:

• trainingset.dat : Mitocheck-assigned object IDs and phenotypic class for cells from a specified frame, well, and plate.
• features/ : Mitocheck-assigned object IDs and bounding boxes for cells from a specified frame, well, and plate.

We use trainingset.dat to locate the frame, well, and plate of labeled cells in 0.locate_data. After extracting the features from these labeled frames with idrstream, we associate the bounding boxes of cells from features/ with their idrstream-derived coordinates to assign cells their phenotypic class (as labeled by Mitocheck).

Control Data

We extract single-cell features from positive and negative controls, which are useful for normalizing all Mitocheck data and suggesting that batch effects are not a dominant signal.

We use IDR-curated mitocheck metadata to locate the well and plate of each control movie. Because idrstream can only extract features from a single frame, we choose a random frame from the middle 33% of the movie. Mitocheck mitosis movies are about 93 frames long, so a random frame between frames 31 and 62 are chosen to extract features from. Because we cannot exactly align the movies in time, we opt to randomly sample from the middle of the movies.

Dataset Types

We extract all single-cell features with and without illumination correction. We refer to these dataset types in the code as dataset_type, where ic corresponds to the dataset with illumination correction and no_ic corresponds to the dataset without illumination correction.

Setup

Perform the following steps to set up the mitocheck_data environment necessary for processing data in this repository.

Step 1: Create Mitocheck Environment

# Run this command to create the conda environment for mitocheck data processing

conda env create -f mitocheck_data_env.yml

Step 2: Activate Mitocheck Environment

# Run this command to activate the conda environment for mitocheck data processing

conda activate mitocheck_data