There is a newer version of the record available.

Published December 6, 2020 | Version v1
Software Open

scBKAP: a clustering model for single-cell RNA-Seq data based on bisecting K-means

Creators

  • 1. College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
  • 2. College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
  • 3. School of Computer Science and Engineering, Central South University, Changsha 410083, China
  • 4. King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, 23955, Saudi Arabia
  • 5. College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; School of Life Sciences, University of Science and Technology of China, Hefei 230027, China

Description

scBKAP, the cornerstone of which is a single-cell bisecting K-means clustering method based on an autoencoder network and a dimensionality reduction model MPDR. Specially, scBKAP utilizes an autoencoder network to reconstruct gene expression values from scRNA-Seq data to alleviate the dropout issue, and the MPDR model composed of the M3Drop feature selection algorithm and the PHATE dimensionality reduction algorithm to reduce the dimensions of reconstructed data. The dimension-ality-reduced data are then fed into the bisecting K-means clustering algorithm to identify the clusters of cells.

Files

scBKAP-main.zip

Files (490.2 MB)

Name Size Download all
md5:d60ea46d4b2d0aa9ec1f62856f684b05
490.2 MB Preview Download