scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
Authors
Keywords
-
Journal
Nature Communications
Volume 12, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-03-25
DOI
10.1038/s41467-021-22197-x
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis
- (2020) Rebecca Elyanow et al. GENOME RESEARCH
- Putative cell type discovery from single-cell gene expression data
- (2020) Zhichao Miao et al. NATURE METHODS
- IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq
- (2020) Anjun Ma et al. NUCLEIC ACIDS RESEARCH
- Integrative Methods and Practical Challenges for Single-Cell Multi-omics
- (2020) Anjun Ma et al. TRENDS IN BIOTECHNOLOGY
- scIGANs: single-cell RNA-seq imputation using generative adversarial networks
- (2020) Yungang Xu et al. NUCLEIC ACIDS RESEARCH
- An entropy-based metric for assessing the purity of single cell populations
- (2020) Baolin Liu et al. Nature Communications
- Single-cell RNA-seq denoising using a deep count autoencoder
- (2019) Gökcen Eraslan et al. Nature Communications
- Single-cell transcriptomic analysis of Alzheimer’s disease
- (2019) Hansruedi Mathys et al. NATURE
- Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments
- (2019) Luyi Tian et al. NATURE METHODS
- A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases
- (2019) Danuta R. Gawel et al. Genome Medicine
- QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data
- (2019) Juan Xie et al. BIOINFORMATICS
- Data denoising with transfer learning in single-cell transcriptomics
- (2019) Jingshu Wang et al. NATURE METHODS
- LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data
- (2019) Changlin Wan et al. NUCLEIC ACIDS RESEARCH
- Exploring single-cell data with deep multitasking neural networks
- (2019) Matthew Amodio et al. NATURE METHODS
- Revealing dynamics of gene expression variability in cell state space
- (2019) Dominic Grün NATURE METHODS
- A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation
- (2019) Alexandra Grubman et al. NATURE NEUROSCIENCE
- Visualizing structure and transitions in high-dimensional biological data
- (2019) Kevin R. Moon et al. NATURE BIOTECHNOLOGY
- Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
- (2018) David van Dijk et al. CELL
- Integrating single-cell transcriptomic data across different conditions, technologies, and species
- (2018) Andrew Butler et al. NATURE BIOTECHNOLOGY
- SAVER: gene expression recovery for single-cell RNA sequencing
- (2018) Mo Huang et al. NATURE METHODS
- An accurate and robust imputation method scImpute for single-cell RNA-seq data
- (2018) Wei Vivian Li et al. Nature Communications
- A general and flexible method for signal extraction from single-cell RNA-seq data
- (2018) Davide Risso et al. Nature Communications
- Deep generative modeling for single-cell transcriptomics
- (2018) Romain Lopez et al. NATURE METHODS
- A test metric for assessing single-cell RNA-seq batch correction
- (2018) Maren Büttner et al. NATURE METHODS
- Power analysis of single-cell RNA-sequencing experiments
- (2017) Valentine Svensson et al. NATURE METHODS
- Reversed graph embedding resolves complex single-cell trajectories
- (2017) Xiaojie Qiu et al. NATURE METHODS
- Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer
- (2017) Woosung Chung et al. Nature Communications
- The Transcription Factor Sp3 Cooperates with HDAC2 to Regulate Synaptic Function and Plasticity in Neurons
- (2017) Hidekuni Yamakawa et al. Cell Reports
- Alzheimer’s Disease Risk Polymorphisms Regulate Gene Expression in the ZCWPW1 and the CELF1 Loci
- (2016) Celeste M. Karch et al. PLoS One
- Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells
- (2015) Allon M. Klein et al. CELL
- Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
- (2015) Jacob H. Levine et al. CELL
- Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation
- (2015) Aleksandra A. Kolodziejczyk et al. Cell Stem Cell
- Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
- (2015) A. Zeisel et al. SCIENCE
- Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development
- (2014) Sean C. Bendall et al. CELL
- Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?
- (2014) Fionn Murtagh et al. JOURNAL OF CLASSIFICATION
- Linking T-cell receptor sequence to functional phenotype at the single-cell level
- (2014) Arnold Han et al. NATURE BIOTECHNOLOGY
- The Genetics of Alzheimer Disease
- (2013) R. E. Tanzi Cold Spring Harbor Perspectives in Medicine
- Oxidative Stress Signaling in Alzheimers Disease
- (2008) B. Su et al. Current Alzheimer Research
- Fast unfolding of communities in large networks
- (2008) Vincent D Blondel et al. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search