Tox-CNN

Tox_CNN is a python-based tool for in vitro screening of drug-induced toxicity.

Description

Tox_CNN makes use of Convolutional Neural Netwoks (CNN) to predict toxicity state of cells under different treatments from microscopy images of simple fluorescently labeled nuclei.

Software requirements

The tool was implemented as a script in Python and it was tested only in Windows 7 and 10:

• Anaconda x64 with Python 3.5
• Visual Studio Community 2013 with update 5
• MinGW-w64 5.4.0 realease
• Python packages needed and the corresponding versions can be found in "requirements.txt" file

Additionally, GPU processing is supported to speed up computations:

• CUDA 8.0
• CuDNN 8.0

Installation

Install Keras, Theano and Dependencies following instructions in the link.

Parameters

To check the list of input parameters to run the tool, together with their descriptions:

$ python Tox_CNN.py --help

The main input (-i) should be a table containing at least 2 columns containing:

• Paths to single-cell images (--path_column)
• Label (HEALTHY or TOXICITY_AFFECTED) for each cell [only in training mode] (--label_column)

Modes

• Test: Prediction of toxicity state (HEALTHY or TOXICITY_AFFECTED) of cells using a pretrained model.
• Train: Create a model for prediction of toxicity state (HEALTHY or TOXICITY_AFFECTED) of cells.
• Transfer Learning: Fine tune a pretrained model to be adapted for prediction of toxicity state of cells in a different scenario.

Example

An example is provided to test the tool. It corresponds to a part of a toxicity-screening plate containing fluorescent images of cells labeled with DAPI under three diferent conditions

• Untreated cells
• DMSO
• Cells treated with 5FLUO drug at different concentrations

Files provided:

• Tables: One row/cell including path to corresponding single-cell image (PATH_Crop_Nuc), HEALTHY or TOXICITY_AFFECTED label (CLASS) and template information comming from the plate structure (well, row, column, field, cell ID, cell line, treatment, concentration and train/test set):
  • Example_1drug_TEST.csv - Table for test including information about all cells.
  • Example_1drug_TRAINING.csv - Table for training including information about a subset of cells.
• Images:
  • IMAGES/CROPS - Zip file containing all single-cell images refered in tables. [Unzip before running the tests]
• Pretrained model:
  • MODELS/Tox_CNN_mixed_model.hdf5 - Mixed model pretrained for toxicity prediction in HL1 cells stained with DAPI.

Running the example

Test:

$ python Tox_CNN.py -i "Example/Example_1drug_TEST.csv" -o "Example/TEST_Example_Tox_CNN" --image_size 50 50 --in_memory --model_fname "Example/MODELS/Tox_CNN_mixed_model.hdf5"

Train:

$ python Tox_CNN.py -i "Example/Example_1drug_TRAINING.csv" -o "Example/MODEL_Example_Tox_CNN" --image_size 50 50 --in_memory --epochs 120 -v

Transfer learning:

$ python Tox_CNN.py -i "Example/Example_1drug_TRAINING.csv" -o "Example/TRANSFERMODEL_Example_Tox_CNN" --image_size 50 50 --in_memory --epochs 25 --model_fname "Example/MODELS/Tox_CNN_mixed_model.hdf5" --net_tune -v