SemI-SUpervised generative Autoencoder for single cell data
Project description
Semi-supervised Single-cell modeling:
Free software: MIT license
Documentation: https://github.com/trungnt13/sisua/tree/master/docs.
Reference:
Trung Ngo Trong, Roger Kramer, Juha Mehtonen, Gerardo González, Ville Hautamäki, Merja Heinäniemi. “SISUA: SemI-SUpervised Generative Autoencoder for Single Cell Data”, ICML Workshop on Computational Biology, 2019. [pdf]
Installation
You only need Python 3.6, the stable version of SISUA installed via pip:
pip install sisua
Install the nightly version on github:
pip install git+https://github.com/trungnt13/sisua@master
For developers, we create a conda environment for SISUA contribution sisua_env
conda env create -f=sisua_env.yml
Getting started
- The basics:
Models specification
- Single-cell analysis:
Latent space
Imputation of genes expression
Prediction of protein markers
- Advanced technical topics:
Hierarchical modeling (coming soon)
Causal analysis (coming soon)
Cross datasets analysis (coming soon)
- Benchmarks:
Fine-tuning networks
Data normalization
Toolkits
We provide binary toolkits for fast and efficient analyzing single-cell datasets:
sisua-train: train single-cell modeling algorithms, support training multiple systems in parallel.
sisua-analyze: evaluate, compare, and interpret trained model.
sisua-embed: probabilistic embedding for semi-supervised training.
sisua-data: coming soon
Some important arguments:
- -model
name of function declared in models
scvi: single-cell Variational Inference model
dca: Deep Count Autoencoder
vae: single-cell Variational Autoencoder
movae: SISUA
- -ds
name of dataset declared in data.
Description of all predefined datasets is in docs.
Some good datasets for practicing:
pbmc8k_ly
cortex
pbmcecc_ly
pbmcscvi
pbmcscvae
Configuration
By default, the data will be saved at your home folder at ~/bio_data, and the experiments’ outputs will be stored at ~/bio_log
You can customize these two paths using the environment variables:
For storing downloaded and preprocessed data: SISUA_DATA
For the experiments: SISUA_EXP
For example:
import os
os.environ['SISUA_DATA'] = '/tmp/bio_data'
os.environ['SISUA_EXP'] = '/tmp/bio_log'
from sisua.data import EXP_DIR, DATA_DIR
print(DATA_DIR) # /tmp/bio_data
print(EXP_DIR) # /tmp/bio_log
or you could set the variables in advance:
export SISUA_DATA=/tmp/bio_data
export SISUA_EXP=/tmp/bio_log
python sisua/train.py
# or using the provided toolkit: sisua-train
Project details
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