Single‐Cell RNA‐seq: Introduction to Bioinformatics Analysis

Fei Ji; Ruslan I. Sadreyev

doi:10.1002/cpmb.92

Single‐Cell RNA‐seq: Introduction to Bioinformatics Analysis

Current Protocols in Molecular Biology ◽

10.1002/cpmb.92 ◽

2019 ◽

Vol 127 (1) ◽

Cited By ~ 1

Author(s):

Fei Ji ◽

Ruslan I. Sadreyev

Keyword(s):

Single Cell ◽

Bioinformatics Analysis ◽

Rna Seq

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Bioinformatics Analysis of Single-Cell RNA-Seq Raw Data from iPSC-Derived Neural Stem Cells

Methods in Molecular Biology - Neural Stem Cells ◽

10.1007/978-1-4939-9007-8_11 ◽

2019 ◽

pp. 145-159 ◽

Cited By ~ 3

Author(s):

Jeffrey Kim ◽

Marcel M. Daadi

Keyword(s):

Stem Cells ◽

Neural Stem Cells ◽

Single Cell ◽

Bioinformatics Analysis ◽

Rna Seq ◽

Raw Data

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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 ◽

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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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 ◽

Rna Seq

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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 ◽

Rna Seq

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