Skip to main content

SISUA_design

Semi-supervised Single-cell modeling:

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:
  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:
  5. Further development:

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

Metadata

Release files for sisua 0.4.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for sisua 0.4.4
File Size Uploaded
sisua-0.4.4.tar.gz 114.9 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for sisua 0.4.4
File Interpreter ABI Platform
sisua-0.4.4-py3.6.egg Legacy Egg format - - Details

Total release size: 457.2 kB

Release files / sisua-0.4.4.tar.gz

Download URL sisua-0.4.4.tar.gz
Size 114.9 kB
Tags Source
SHA-256 checksum
How to use checksums
500eeda3795a439fc7c8e31ba4654dd6b5b480e3bbb3c76b516ce35f9814c446
BLAKE2b-256 checksum
How to use checksums
1b590a51a8e3811892819cfc4f90509f0c32e8f8bf14462699995a5616d4f254
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/1.15.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.6.7

Release files / sisua-0.4.4-py3.6.egg

Download URL sisua-0.4.4-py3.6.egg
Size 342.3 kB
Tags Egg
SHA-256 checksum
How to use checksums
812be2c55077d40aea9e254377f0a7e47d75d3236f4e1a1ae7cb3f1e020b4d4c
BLAKE2b-256 checksum
How to use checksums
887d49e476fac4d658b2c328caf6237fa78e869c63c8adbf9555a349e37c0fb5
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/1.15.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.6.7

Release history Release notifications | RSS feed

This release

0.4.4 This release

2 release files

0.4.3

2 release files

0.4.2

2 release files

0.4.1

2 release files

0.4.0

2 release files

0.3.0

2 release files

0.2.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page