cytoself

cytoself is a self-supervised platform that we developed for learning features of protein subcellular localization from microscopy images. This model is described in detail in our recent preprint [2]. The representations derived from cytoself encapsulate highly specific features that can derive functional insights for proteins on the sole basis of their localization.

Applying cytoself to images of endogenously labeled proteins from the recently released OpenCell database creates a highly resolved protein localization atlas [1].

[1] Cho, Nathan H., et al. "OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization." bioRxiv (2021). https://www.biorxiv.org/content/10.1101/2021.03.29.437595v1
[2] Kobayashi, Hirofumi, et al. "Self-Supervised Deep-Learning Encodes High-Resolution Features of Protein Subcellular Localization." bioRxiv (2021). https://www.biorxiv.org/content/10.1101/2021.03.29.437595v1

How cytoself works

cytoself uses images and its identity information as a label to learn the localization patterns of proteins. We used cell images where single protein is labeled and the ID of labeled protein as identity information.

What's in this repository

This repository offers three main components: DataManager, cytoself.models, and Analytics.

DataManager is a simple module to handle train, validate and test data. You may want to modify it to adapt to your own data structure. This module is in cytoself.data_loader.data_manager.

cytoself.models contains modules for three different variants of the cytoself model: a model without split-quantization, a model without the pretext task, and the 'full' model (refer to our preprint for details about these variants). There is a submodule for each model variant that provides methods for constructing, compiling, and training the models (which are built using tensorflow).

Analytics is a simple module to perform analytic processes such as dimension reduction and plotting. You may want to modify it too to perform your own analysis. This module is in cytoself.analysis.analytics.

Installation

Recommended: create a new environment and install cytoself on the environment from pypi

conda create -y -n cytoself python=3.7
conda activate cytoself
pip install cytoself

(Option) Install TensorFlow GPU

If your computer is equipped with GPUs that support Tensorflow 1.15, you can install Tensorflow-gpu to utilize GPUs. Install the following packages before cytoself, or uninstall the existing CPU versions and reinstall the GPU versions again with conda.

conda install -y h5py=2.10.0 tensorflow-gpu=1.15

For the developers

You can also install cytoself from this GitHub repository.

git clone https://github.com/royerlab/cytoself.git
pip install .

Troubleshooting

In case of getting errors in the installation, run the following code inside the cytoself folder to manually install the dependencies.

pip install -r requirements.txt

As a reference for a complete dependency, a snapshot of a working environment can be found in environment.yml

Example script

A minimal example script is in example/simple_training.py.

Test if this package runs in your computer with command

python examples/simple_example.py

Computation resources

It is highly recommended to use GPU to run cytoself. A full model with image shape (100, 100, 2) and batch size 64 can take ~9GB of GPU memory.

Tested Environment

Google Colab (CPU/GPU/TPU)

macOS 11.6.1, RAM 32GB (CPU)

Windows10 Pro 64bit, RAM 32GB (CPU)

Ubuntu 18.04.6 LTS, RTX 2080Ti, CUDA 11.2 (CPU/GPU)