Literature DB >> 32232401

Identifying cell types to interpret scRNA-seq data: how, why and more possibilities.

Ziwei Wang, Hui Ding, Quan Zou.   

Abstract

Single-cell RNA sequencing (scRNA-seq) has generated numerous data and renewed our understanding of biological phenomena at the cellular scale. Identification of cell types has been one of the most prevalent means for interpreting scRNA-seq data, based upon which connections are made between the transcriptome and phenotype. Herein, we attempt to review the methods and tools that dedicate to the task regarding their feature and usage and look at the possibilities for scRNA-seq development in the near future.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Keywords:  annotation; cell type; classification; clustering; identity; scRNA-seq

Mesh:

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Year:  2020        PMID: 32232401     DOI: 10.1093/bfgp/elaa003

Source DB:  PubMed          Journal:  Brief Funct Genomics        ISSN: 2041-2649            Impact factor:   4.241


  4 in total

1.  Single-Cell RNA Sequencing Profiles Identify Important Pathophysiologic Factors in the Progression of Diabetic Nephropathy.

Authors:  Xi Lu; Li Li; Luolan Suo; Ping Huang; Hongjie Wang; Su Han; Mingming Cao
Journal:  Front Cell Dev Biol       Date:  2022-05-10

2.  Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning.

Authors:  Xiucai Ye; Weihang Zhang; Yasunori Futamura; Tetsuya Sakurai
Journal:  Cells       Date:  2020-08-21       Impact factor: 6.600

3.  Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field Model.

Authors:  Yishu Wang; Xuehan Tian; Dongmei Ai
Journal:  Biomed Res Int       Date:  2021-05-22       Impact factor: 3.411

Review 4.  Automated methods for cell type annotation on scRNA-seq data.

Authors:  Giovanni Pasquini; Jesus Eduardo Rojo Arias; Patrick Schäfer; Volker Busskamp
Journal:  Comput Struct Biotechnol J       Date:  2021-01-19       Impact factor: 7.271

  4 in total

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