ScAIDE: clustering of large-scale single-cell RNA-seq data reveals putative and rare cell types.

ABSTRACT: Recent advancements in both single-cell RNA-sequencing technology and computational resources facilitate the study of cell types on global populations. Up to millions of cells can now be sequenced in one experiment; thus, accurate and efficient computational methods are needed to provide clustering and post-analysis of assigning putative and rare cell types. Here, we present a novel unsupervised deep learning clustering framework that is robust and highly scalable. To overcome the high level of noise, scAIDE first incorporates an autoencoder-imputation network with a distance-preserved embedding network (AIDE) to learn a good representation of data, and then applies a random projection hashing based k-means algorithm to accommodate the detection of rare cell types. We analyzed a 1.3

SUBMITTER: Xie K 

PROVIDER: S-EPMC7671411 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature