CalciumGAN: A Python repository from bryanlimy

Synthesising Realistic Calcium Imaging Data of Neuronal Populations Using GAN

Implementation of CalciumGAN, used to obtain the results detailed in CalciumGAN: A Generative Adversarial Network Model for Synthesising Realistic Calcium Imaging Data of Neuronal Populations.

Citing this work

@article{li2020calciumgan,
  title={CalciumGAN: A Generative Adversarial Network Model for Synthesising Realistic Calcium Imaging Data of Neuronal Populations},
  author={Li, Bryan M and Amvrosiadis, Theoklitos and Rochefort, Nathalie and Onken, Arno},
  journal={arXiv preprint arXiv:2009.02707},
  year={2020}
}

1. Installation

1.1 Requirements

It is recommended to install the codebase in a virtual environment, such as conda.

1.2 Quick install

Create a new conda environment in Python 3.6
```
conda create -n calciumgan python=3.6
```
Activate calciumgan virtual environment
```
conda activate calciumgan
```
Install all dependencies and packages with setup.sh script, works on both Linus and macOS.
```
sh setup.sh
```

1.3 Manual setup

Install the following packages:

TensorFlow
j-friedrich/OASIS
Neo
Elephant
packages in requirements.txt
code from dg_python are also being used for the dichotomized Gaussian model

2. Dataset

Navigate to dataset
```
cd dataset
```

2.1 Recorded Calcium Imaging Data

Place all raw calcium imaging data under dataset/raw_data

Apply OASIS to infer spike train

python spike_train_inference.py --input_dir raw_data

Generate TFRecords from a specific pickle file --input, normalize the data, preform segmentation and store the TFrecords in output_dir. Use --help to see all available arguments.
```
python generate_tfrecords.py --input raw_data/signals.pkl --output_dir tfrecords/sl2048 --sequence_length 2048 --normalize
```

2.2 Dichotomized Gaussian Artificial Data

Generate artificial spike trains and calcium-like signals from the Dichomotized Gaussian distribution with the mean and covariance of data in --input, save the the output pickle file to --output. TFRecords in --output_dir. Use --help to see all available arguments.
```
python generate_dg_data.py --input raw_data/signals.pkl --output dg.pkl
```
Generate TFRecords from a specific pickle file --input, normalize the data, preform segmentation and store the TFrecords in output_dir. Use --help to see all available arguments.
```
python generate_tfrecords.py --input dg.pkl --output_dir tfrecords/sl2048_dg --sequence_length 2048 --normalize
```

3. Train model

To train CalciumGAN on the recorded calcium imaging data with the default hyper-parameters for 400 epochs. Checkpoints, generated data, model training information are stored in --output_dir.

python main.py --input_dir dataset/tfrecords/sl2048 --output_dir runs/001 --epochs 400 --batch_size 128 --model calciumgan --algoirthm wgan-gp --noise_dim 32 --num_units 64 --kernel_size 24 --strides 2 --m 10 --layer_norm --mixed_precision --save_generated last

Use --help to check all available arguments. Mixed precision compute, TensorBoard profiling, hyper-parameter search are some of the features built into this codebase.
The training command applies to both recorded data and dischotomized Gaussian artificial data.

4. Spike analysis

4.1 Recorded Calcium Imaging Data

Deconvolve the calcium signals to spike trains from generated data in --output_dir, then compute various spikes statistics. Use --help to check all available arguments.
```
python compute_metrics.py --output_dir runs/001
```
All the plots can be found in runs/001/metrics/plots

4.2 Dichotomized Gaussian Artificial Data

Deconvolve the calcium signals to spike trains from generated data in --output_dir, then compute various spikes statistics. Use --help to check all available arguments.
```
python compute_dg_metrics.py --output_dir runs/002
```
PLots of mean and covariance can be found in diagrams/

5. Visualization and Profiling

Run tensorboard
```
tensorboard --logdir runs/001
```
We have implemented profiling with TensorFlow Profiler support. You can enable profiling with --profile flag when training the model with main.py.

6. Hyper-parameters Search

We have incorporated the Hyperparameter Turning with Keras feature. Modify the hyper-parameters you would like to test in seasrch.py and run
```
python search.py --input_dir dataset/tfrecords/sl2048 --output_dir runs/hparams_search --epochs 400 --mixed_precision
```

bryanlimy/CalciumGAN