Article

scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses

2020-08-03

Abstract excerpt

<h4>ABSTRACT</h4> Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framewo...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 2149b02a-555c-53fa-955c-1f9d0ff93335
DOI: 10.1101/2020.08.02.233569

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

scGNN: a novel graph neural network framework for single-cell RNA-Seq analysesDOI 10.1101/2020.08.02.233569Read

GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell TranscriptomicsSemantic proximity Read

Joint Gene Network Construction by Single-Cell RNA Sequencing DataSemantic proximity Read

scAURA: Alignment- and Uniformity-based Graph Debiased Contrastive Representation Architecture for Self-Supervised Clustering of Single-Cell TranscriptomicsSemantic proximity Read

scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell TranscriptomicsSemantic proximity Read

Graph Contrastive Learning as a Versatile Foundation for Advanced scRNA-seq Data AnalysisSemantic proximity Read

DOLPHIN Advances Cell Representation Beyond Gene-Level by Integrating Exon-Level Quantification and Junction Reads with Deep Neural NetworksSemantic proximity Read

A multimodal deep learning model to infer cell-type-specific functional gene networksSemantic proximity Read

A Joint Deep Learning Model for Simultaneous Batch Effect Correction, Denoising and Clustering in Single-Cell TranscriptomicsSemantic proximity Read

scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell TranscriptomicsSemantic proximity Read

Reference-free Cell-type Annotation for Single-cell Transcriptomics using Deep Learning with a Weighted Graph Neural NetworkSemantic proximity Read

Inferring Disease Progressive Stages in Single-Cell Transcriptomics Using Weakly-Supervised Deep Learning ApproachSemantic proximity Read

GeneBag: training a cell foundation model for broad-spectrum cancer diagnosis and prognosis with bulk RNA-seq dataSemantic proximity Read