Supervised Adversarial Alignment of Single-Cell RNA-seq Data.

ABSTRACT: Dimensionality reduction is an important first step in the analysis of single-cell RNA-sequencing (scRNA-seq) data. In addition to enabling the visualization of the profiled cells, such representations are used by many downstream analyses methods ranging from pseudo-time reconstruction to clustering to alignment of scRNA-seq data from different experiments, platforms, and laboratories. Both supervised and unsupervised methods have been proposed to reduce the dimension of scRNA-seq. However, all methods to date are sensitive to batch effects. When batches correlate with cell types, as is often the case, their impact can lead to representations that are batch rather than cell-type specific. To overcome this, we developed a domain adversarial neural network model for learning a reduced dimens

SUBMITTER: Ge S 

PROVIDER: S-EPMC8418522 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature