Welcome to DEPF’s documentation!
Date: March 15, 2023. Version: 1.0.0
Contact: Thank you for using DEPF! Any questions, suggestions or advices are welcome. Email address: fanyi21@mails.jlu.edu.cn, lixt314@jlu.edu.cn.
paper: Reliable Identification and Interpretation of Single-cell Molecular Heterogeneity and Transcriptional Regulation using Dynamic Ensemble Pruning
Overview:
DEPF is constructed based on four modules (Normalization, Hierarchical Autoencoder, Clustering Ensemble, Dynamic Ensemble Pruning) developed by ourselves.
Normalization: The expression data are rescaled to a range of 0 to 1 for each cell.
Hierarchical Autoencoder: The normalized data are mapped to multiple low-dimensional latent spaces.
Clustering Ensemble: An effective basic clustering algorithm is employed to address the non-linear embedding in the latent space to produce multiple underlying cluster results to generate cluster ensemble.
Dynamic Ensemble Pruning: The low-quality basic clusterings in the ensemble are dynamically pruned away.
Getting Started
API
History