Deep learning of gene relationships from single cell time-course expression data.

IF: 13.994

Cited by: 8

Abstract

Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offer several advantages including the large number of expression profiles available and the ability to focus on individual cells rather than averages. However, the data also raise new computational challenges. Using a novel encoding for scRNA-Seq expression data, we develop deep learning methods for interaction prediction from time-course data. Our methods use a supervised framework which represents the data as 3D tensor and train convolutional and recurrent neural networks for predicting interactions. We tested our time-course deep learning (TDL) models on five different time-series scRNA-Seq datasets. As we show, TDL can accurately identify causal and regulatory gene-gene interactions and can also be used to assign new function to genes. TDL improves on prior methods for the above tasks and can be generally applied to new time-series scRNA-Seq data.

Keywords

Gene Expression

deep learning

single cell RNA-Seq

time-course data

MeSH terms

Algorithms

Animals

Cells, Cultured

Computational Biology

Deep Learning

Epistasis, Genetic

Gene Expression Profiling

Gene Regulatory Networks

Humans

Mice

Models, Genetic

RNA-Seq

Single-Cell Analysis

Time Factors

Authors

Yuan, Ye

Bar-Joseph, Ziv

Recommend literature

1. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data.

2. Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces.

3. Computational methods for the integrative analysis of single-cell data.

4. Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets.

5. jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.

Similar data

1. Developmental diversification of cortical inhibitory interneurons

2. Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells

3. Snapshot and temporal scRNA-seq of progenitor cells to dissect human embryonic stem cells entry into endoderm progenitors

4. Droplet barcoding for single cell transcriptomics applied to embryonic stem cells

5. Single-cell RNA-seq reveal lineage formation and X-chromosome dosage compensation in human preimplantation embryos