Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.

ABSTRACT:

Background

Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discoveries in biomedical areas. To ensure that the single-cell based gene expression data are interpreted appropriately, it is crucial to develop new computational methods.

Results

In this article, we try to re-construct a neural network based on Gene Ontology (GO) for dimension reduction of scRNA-seq data. By integrating GO with both unsupervised and supervised models, two novel methods are proposed, named GOAE (Gene Ontology AutoEncoder) and GONN (Gene Ontology Neural Network) respectively.

Conclusions

The evaluation results sh

SUBMITTER: Peng J 

PROVIDER: S-EPMC6557741 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature