StemVAE: identify temporal information from endometrium cells via Deep generative model

Contact: Yuanhua Huang, Dandan Cao, Yijun Liu

Email: yuanhua@hku.hk.

Introduction ()

StemVAE use the probabilistic latent space model to infer the pseudo-time of cells. StemVAE input consists of an mRNA expression matrix and real-time labels of cells, and output is the reconstruction of the expression matrix and predicted time. StemVAE, based on canonical variation atuo-encoder (VAE), includes an encoder, a cell-decoder, and a time-decoder.

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Contents

• Latest Updates
• Installations
• Usage
  • Model training
  • Performance evaluation
  • Spatial inference

Latest Updates

• v0.1 (Sep, 2023): Initial release.

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Installation

To install stemVAE, python 3.9 is required and follow the instruction

1. Install Miniconda3 if not already available.
2. Clone this repository:

  git clone https://github.com/awa121/stemVAE_endometrium

3. Navigate to stemVAE_endometrium directory:

  cd stemVAE_endometrium

4. (5-10 minutes) Create a conda environment with the required dependencies:

  conda env create -f environment.yml

5. Activate the stemVAE environment you just created:

  conda activate stemVAE

6. Install pytorch: You may refer to pytorch installtion as needed. For example, the command of installing a cpu-only pytorch is:

conda install pytorch torchvision torchaudio cpuonly -c pytorch

Usage

StemVAE contains 2 main function: k fold test on dataset; predict on a new donor. And we also provide code to reproduce the result in the paper.

To check available modules, run:

prepare the preprocess data:

Todo list

k fold test

The result will save to folder results, log file wile save to folder logs

python -u VAE_fromDandan_testOnOneDonor.py 
--vae_param_file=supervise_vae_regressionclfdecoder 
--file_path=preprocess_02_major_Anno0717_Gene0720 --time_standard_type=neg1to1 
--train_epoch_num=100 
--result_save_path=230728_newAnno0717_Gene0720_18donor_2type_plot 
> logs/log.log