JIND: joint integration and discrimination for automated single-cell annotation.

ABSTRACT:

Motivation

An important step in the transcriptomic analysis of individual cells involves manually determining the cellular identities. To ease this labor-intensive annotation of cell-types, there has been a growing interest in automated cell annotation, which can be achieved by training classification algorithms on previously annotated datasets. Existing pipelines employ dataset integration methods to remove potential batch effects between source (annotated) and target (unannotated) datasets. However, the integration and classification steps are usually independent of each other and performed by different tools. We propose JIND (joint integration and discrimination for automated single-cell annotation), a neural-network-based framework for automated cell-type identification that pe

SUBMITTER: Goyal M 

PROVIDER: S-EPMC9278043 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature