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DeepLoc

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This repository contains the code, and access to the pretrained models and datasets for the paper: "Automated analysis of high-content microscopy data with deep learning"

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# DeepLoc
This repository contains the code, pretrained models, and datasets for the paper:
"Automated analysis of high-content microscopy data with deep learning"
Kraus, O.Z., Grys, B.T., Ba, J., Chong, Y., Frey, B.J., Boone, C., & Andrews, B.J.
Molecular Systems Biology 13.4 (2017): 924.
http://msb.embopress.org/content/13/4/924

The commands below assume starting from the 'DeepLoc' directory.

REQUIREMENTS
------------

for training and evaluation scripts:

Python 2.7 64-bit: http://www.python.org/getit/

CUDA 8.0+ SDK (for GPU support): https://developer.nvidia.com/cuda-downloads
    
cuDNN 5.1 (for GPU support): https://developer.nvidia.com/cudnn
   
Tensorflow v1.0+: https://www.tensorflow.org/install
     
   * developed on tensorflow version 0.10 (https://www.tensorflow.org/versions/r0.10/get_started/os_setup#virtualenv_installation)
   * updated to run on version 1.0.1

You can use the following command to install the dependencies (other than tensorflow):

pip install -r requirements.txt

or if running the Anaconda Python distribution use to start an environment with the dependencies (recommended):

conda env create -f environment.yml
    
GETTING THE FULL DATA SETS
--------------------------
The datasets and pretrained models are too large to store in the repository. Please download them using instructions below.

To download and unzip the datasets and pretrained model, please run:

bash download_datasets.sh

Otherwise, the datasets are available at:
  http://spidey.ccbr.utoronto.ca/~okraus/DeepLoc_full_datasets.zip

and the pretrained model is available at:
  http://spidey.ccbr.utoronto.ca/~okraus/pretrained_DeepLoc.zip

include the datasets in the 'datasets' subdirectory.

TRAINING DeepLoc on Chong et al., 2015 DATA (CELL, doi:10.1016/j.cell.2015.04.051)
----------------------------------------------------------------------------------

To train DeepLoc on the Chong et al. dataset run:

python DeepLoc_train.py --logdir path/to/log-directory
    
   * the argument passed to --logdir indicates where to save the resulting models and model predictions (default is './logs')
   * download the datasets as described above and store them in './datasets'
   * by default, models are saved every 500 iterations, and a test batch is evaluated every 50 iterations

To evaluate the performance of different DeepLoc checkpoints run:

python DeepLoc_eval.py --logdir path/to/log-directory

* the argument to --logdir should be the same path used for training
   * adds a python cPickle file called 'test_acc_deploy_results.pkl' including 
      training and test performance (accuracy and test values) for the full datasets
   * default output stored in './logs'

VISUALIZING DeepLoc GRAPH AND TRAINING PERFORMANCE
--------------------------------------------------

The DeepLoc model and training performance can be visualized using Tensorboard
(https://www.tensorflow.org/how_tos/summaries_and_tensorboard/)

To initialize a tensorboard session, run:

tensorboard --logdir=path/to/log-directory

once TensorBoard is running, navigate your web browser to localhost:6006 to view the TensorBoard

DEPLOYING DeepLoc TO SAMPLE IMAGE FROM ENTIRE SCREEN
----------------------------------------------------

DeepLoc can be deployed to an entire automated microscopy screen using the demo in:

python DeepLoc_eval_sample_image.py
    
   * assumes model saved in './pretrained_DeepLoc/pretrained_models/model.ckpt-5000'
   * default output stored as csv file in './sample_image'

VISUALIZING DeepLoc CLASSES AND FEATURES
----------------------------------------

Patterns that maximally activate DeepLoc output classes can be visualized using:

python DeepLoc_visualize_classes.py
    
   * assumes model saved in './pretrained_DeepLoc/pretrained_models/model.ckpt-5000'
   * default output stored in './output_figures/generated_cells.png'

TRANSFERING DeepLoc TO wt2017 and SWAT_RFP DATASETS
---------------------------------------------------

DeepLoc can be loaded and fine-tuned on the wt2017 dataset using:

python DeepLoc_transfer_wt2017.py
    
   * assumes model saved in './pretrained_DeepLoc/pretrained_models/model.ckpt-5000'
   * default output stored in './logs/transfer_wt2017

and fine-tuned on the SWAT_RFP dataset using:

python DeepLoc_transfer_SWAT_RFP.py
    
   * assumes model saved in './pretrained_DeepLoc/pretrained_models/model.ckpt-5000'
   * default output stored in './logs/transfer_SWAT_RFP

DeepLoc

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