scLINE: A multi-network integration framework based on network embedding for representation of single-cell RNA-seq data

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2021.103899 ◽

2021 ◽

pp. 103899

Author(s):

Huoyou Li ◽

Xuesong Xiao ◽

Xiaohui Wu ◽

Lishan Ye ◽

Guoli Ji

Keyword(s):

Single Cell ◽

Rna Seq ◽

Network Embedding ◽

Network Integration ◽

Integration Framework

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Network embedding-based representation learning for single cell RNA-seq data

Nucleic Acids Research ◽

10.1093/nar/gkx750 ◽

2017 ◽

Vol 45 (19) ◽

pp. e166-e166 ◽

Author(s):

Xiangyu Li ◽

Weizheng Chen ◽

Yang Chen ◽

Xuegong Zhang ◽

Jin Gu ◽

...

Keyword(s):

Single Cell ◽

Representation Learning ◽

Rna Seq ◽

Network Embedding

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ECBN: Ensemble Clustering based on Bayesian Network inference for Single-cell RNA-seq Data

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188589 ◽

2020 ◽

Author(s):

Dexin Zhang ◽

Yuan Zhu

Keyword(s):

Bayesian Network ◽

Single Cell ◽

Network Inference ◽

Ensemble Clustering ◽

Rna Seq ◽

Bayesian Network Inference

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Faculty Opinions recommendation of Single-cell RNA-seq reveals dynamic paracrine control of cellular variation.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718445581.793514072 ◽

2016 ◽

Author(s):

Cole Trapnell ◽

Serena Liu

Keyword(s):

Single Cell ◽

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Faculty Opinions recommendation of Single-cell RNA-seq identifies a PD-1hi ILC progenitor and defines its development pathway.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726853812.793538588 ◽

2017 ◽

Author(s):

Michael J Holtzman

Keyword(s):

Single Cell ◽

Rna Seq ◽

Development Pathway

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Faculty Opinions recommendation of SCnorm: robust normalization of single-cell RNA-seq data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727515021.793534656 ◽

2017 ◽

Author(s):

Lauren McIntyre

Keyword(s):

Single Cell ◽

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Faculty Opinions recommendation of Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727539520.793534943 ◽

2017 ◽

Author(s):

Philippe Saas ◽

Sylvain Perruche

Keyword(s):

Dendritic Cells ◽

Single Cell ◽

Human Blood ◽

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Faculty Opinions recommendation of Detecting Activated Cell Populations Using Single-Cell RNA-Seq.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.731978868.793538401 ◽

2017 ◽

Author(s):

Yi Eve Sun ◽

Weihong Ge

Keyword(s):

Single Cell ◽

Cell Populations ◽

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Faculty Opinions recommendation of Challenges in unsupervised clustering of single-cell RNA-seq data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.734773782.793581119 ◽

2020 ◽

Author(s):

Sushmita Roy

Keyword(s):

Single Cell ◽

Unsupervised Clustering ◽

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Faculty Opinions recommendation of Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.735170781.793574482 ◽

2020 ◽

Author(s):

Vijay Tiwari ◽

Mohammed Inayatullah

Keyword(s):

Single Cell ◽

Disease Progression ◽

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Faculty Opinions recommendation of A systematic evaluation of single cell RNA-seq analysis pipelines.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.736736304.793566365 ◽

2019 ◽

Author(s):

Hans-Rudolf Hotz

Keyword(s):

Single Cell ◽

Systematic Evaluation ◽

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