Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.

ABSTRACT: In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsupervised clustering problems and show its effectiveness in the field of single-cell RNA sequencing (scRNA-Seq). The goal of scRNA-Seq experiments is often the definition and cataloguing of cell types from the transcriptional output of individual cells. To improve the clustering of small disease- or tissue-specific datasets, for which the identification of rare cell types is often problematic, we propose a transfer learning method to utilize large and well-annotated reference datasets, such as those produced by the Human Cell Atlas. Our approach mod

SUBMITTER: Mieth B 

PROVIDER: S-EPMC6937257 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature