scSemiAE

The implementation of "scSemiAE: A deep model with semi-supervised learning for single-cell transcriptomics". Single cell Semi-supervised AutoEncoder is a dimensionality reduction approach for better identification of cell subpopulations. For reproducing the paper results, please visit ***.

Install

pip install git+https://github.com/PlusoneD/scSemiAE.git

About file

│ README.md // help └─code // related codes of scSemiAE │ data.py // data simulation for test │ run.py // an example │ dataset │ expression_matrix.txt
└─ metadata.txt
└─model │ dataset.py // dataset type for training model │ inference.py // classification method │ loss.py // loss function │ metrics.py // metrics calculation │ net.py // model network │ scSemiAE.py // model class └─ utils.py // some tool function

Usage

To train a model based on a dataset, please run 'run.py'. The parameters are listed below.

--data_path: path to the dataset folder, default="./dataset/"

--save_path: path to the output directory, default="./output/"

--lab_size: labeled set size for each cell type, default=10

--lab_ratio: labeled set ratio for each cell type, default= -1

--cuda: enables cuda

--pretrain_batch: batch size for pretraining, default=100

--epochs: number of epochs to train for, default=60

--nepoch_pretrain: number of epochs to pretrain for, default=50

--learning_rate: learning rate for the model, default=0.001

--lr_scheduler_step: StepLR learning rate scheduler step, default=10

--lr_scheduler_gamma: StepLR learning rate scheduler gamma, default=0.5

--Lambda: weight for L2, default=1

--visual: visualization of data. default=False

if you don't want to change the parameter, you can only put the data (two files) into "./dataset/" and execute:

python run.py

of note，the data need to meet some requirements such as "expression_matrix.txt" and "metadata.txt".