Model-based autoencoders for imputing discrete single-cell RNA-seq data

Clustering single-cell RNA-seq data with a model-based deep learning approach

Nature Machine Intelligence ◽

10.1038/s42256-019-0037-0 ◽

2019 ◽

Vol 1 (4) ◽

pp. 191-198 ◽

Cited By ~ 22

Author(s):

Tian Tian ◽

Ji Wan ◽

Qi Song ◽

Zhi Wei

Keyword(s):

Deep Learning ◽

Single Cell ◽

Learning Approach ◽

Rna Seq ◽

Model Based

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TWO‐SIGMA: A novel two‐component single cell model‐based association method for single‐cell RNA‐seq data

Genetic Epidemiology ◽

10.1002/gepi.22361 ◽

2020 ◽

Author(s):

Eric Van Buren ◽

Ming Hu ◽

Chen Weng ◽

Fulai Jin ◽

Yan Li ◽

...

Keyword(s):

Single Cell ◽

Cell Model ◽

Rna Seq ◽

Model Based ◽

Single Cell Model ◽

Two Component ◽

Association Method

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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 A model based criterion for gene expression calls using RNA-seq data.

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

10.3410/f.718016766.793477925 ◽

2013 ◽

Author(s):

Lauren McIntyre

Keyword(s):

Gene Expression ◽

Rna Seq ◽

Model Based

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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 ◽

Rna Seq

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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 ◽

Rna Seq

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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 ◽

Rna Seq

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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 ◽

Rna Seq

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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 ◽

Rna Seq

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