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An accurate and scalable imputation algorithm based on semi-supervised deep learning for single-cell transcriptome

Project description

PyPI

An accurate and scalable imputation algorithm based on semi-supervised deep learning for single-cell transcriptome

  • Free software: Apache License 2.0

Requirements

Installation

Installation with pip

To install with pip, run the following from a terminal:

pip install DISC

Installation from GitHub

To clone the repository and install manually, run the following from a terminal:

git clone git://github.com/iyhaoo/DISC.git

cd DISC

python setup.py install

Usage

Quick Start
  1. Run DISC:

    disc \
    --dataset=matrix.loom \
    --out-dir=out_dir
    

    where matrix.loom is a loom-formatted raw count matrix with genes in rows and cells in columns and out_dir is the path of output directory.

  2. Results

    • log.tsv: a tsv-formatted log file that records training states.
    • summary.pdf: a pdf-formatted file that visualizes the fitting line and optimal point and it will be updated in real time when running.
    • summary.tsv: a tsv-formatted file that shows the raw data of visualization.
    • result: a directory for imputaion results as below:
      • imputation.loom: a loom-formatted imputed matrix with genes in rows and cells in columns.
      • feature.loom: a loom-formatted dimensionally reduced feature matrix provided by our method based on the imputed matrix above with feature in rows and cells in columns.
      • running_info.hdf5: a hdf5-formatted saved some basic information about the input dataset such as library size, genes used for modelling and so on.
    • models: a directory for trained models in every save interval
Tutorials
  1. Imputation
  2. Reproducing our results:
  3. Supplementary topics:

References

History

1.0.0 (2019-11-XX)

  • First release on PyPI.

Project details


Release history Release notifications

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