DSM-tools is a Python module that converts neuron morphology into sequences by binary tree traversals and implements deep learning models for encoding the sequences and predicting cell types.
This project was started by Feng Xiong (xiongfengNJ) in 2020, SEU-ALLEN, Nanjing.
The Python package was authorised to be developed by Zuo-Han Zhao (zzhmark) in 2022.
- Python (>=3.9)
- tensorflow (>=2.10.0)
- scikit-learn (>=1.1.3)
- SimpleITK (2.2.0)
- gensim (>=4.2.0)
- matplotlib (>=3.6.2)
- pandas (>=1.5.1)
- importlib_resources (>=5.10.0)
pip install DSM-tools
pip install git+https://github.com/xiongfengNJ/DSM-tools
Here's some simple usage that get you a quick start.
from dsmtools.preprocessing import NeuronSequenceDataset
# dataset generation tool, with computation settings
ds = NeuronSequenceDataset(swc_file_paths, jobs=10)
# processing
ds.read_parallel()
ds.qc_parallel(qc_len_thr=10)
ds.make_sequence_parallel()
# save the result (OrderedDict) as pickle
ds.pickle_result('output.pickle')
By default, it gives you a set of features for each neuron by the order of preorder traversals.
from dsmtools.modeling import DSMDataConverter, DSMHierarchicalAttentionNetwork
# further convert the dataframes to dataset fed to tensorflow
converter = DSMDataConverter(ds)
han_x = converter.convert_for_han()
# models trained with our data ready for use
han = DSMHierarchicalAttentionNetwork.load_1282_seu()
le = DSMHierarchicalAttentionNetwork.label_encoder_1282_seu()
# decode the one-hot matrix back to labels
le.inverse_transform(np.argmax(han.predict(han_x), axis=1))
The prediction for autoencoder is similar.
from dsmtools.modeling import DSMAutoencoder
ae_x = converter.convert_for_ae()
# build model
ae = DSMAutoencoder()
ae.compile(feature_dim=6, seq_len=2000)
# training
ae.fit(train_x, test_x, model_save_path='ae_checkpoint.h5', epochs=300, batch_size=32)
ae.plot_learning_curve('ae_learning.png')
The training for HAN is similar.
Please see the examples directory for details.
You can inherit classes like NeuronSequenceDataset
to change the data processing behaviours, which should be quite easy.
The tree manipulating class NeuronTree
offers you the freedom of exploring your own definition of subtree nodes to generate traversal sequences.
DSM-tools API Documentation: https://xiongfengnj.github.io/DSM-tools
powered by pdoc, served in GitHub Pages.
Package source: https://github.com/xiongfengNJ/DSM-tools
Experiment code: https://github.com/xiongfengNJ/neuron2seq
Experiment data: http://101.43.104.173:8500
Online service: http://101.43.104.173:8501
SEU-ALLEN Joint Center,
Institute for Brain and Intelligence,
Southeast University
Lab homepage: https://braintell.org
Our paper is still under review, please check the link for preprint. For now, you can cite it as:
Hanchuan Peng, Feng Xiong, Peng Xie et al. DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction, 29 June 2022, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-1627621/v1]