sisua 0.4.4

SemI-SUpervised generative Autoencoder for single cell data

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

1. The basics:

   • Datasets description

   • Models specification

   • Basic API and work-flow

2. Single-cell analysis:

   • Latent space

   • Imputation of genes expression

   • Prediction of protein markers

3. Advanced technical topics:

   • Probabilistic embedding

   • Hierarchical modeling (coming soon)

   • Causal analysis (coming soon)

   • Cross datasets analysis (coming soon)

4. Benchmarks:

   • Scalability test

   • Fine-tuning networks

   • Data normalization

5. Further development:

   • Roadmap

   • SISUA 2

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