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  Lineage Inference and Stem Cell Identity prediction Using Single-Cell RNA-Sequencing Data

Sagar, S., & Grün, D. (2019). Lineage Inference and Stem Cell Identity prediction Using Single-Cell RNA-Sequencing Data. In Methods in Molecular Biology (pp. 227-301). Clifton, N.J.: Humana Press.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-A615-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-E6C4-B
Genre: Book Chapter

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 Creators:
Sagar, Sagar1, Author
Grün, Dominic1, Author              
Affiliations:
1Max Planck Institute of Immunobiology and Epigenetics, Max Planck Society, 79108 Freiburg, DE, ou_2243640              

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Free keywords: Singele-cell RNA sequencing, Single-cell data analysis, Stem cell identification, Lineage inference, Pseudo-temporal ordering, Fate bias, Multipotent, RaceID3, StemID2, FateID
 Abstract: With the advent of several single-cell RNA-sequencing (scRNA-seq) techniques, it has become possible to gain novel insights into the fundamental long-standing questions in biology with an unprecedented resolution. Among the various applications of scRNA-seq, (1) discovery of novel rare cell types, (2) characterization of heterogeneity among the seemingly homogenous population of cells described by cell surface markers, (3) stem cell identification, and (4) construction of lineage trees recapitulating the process of differentiation remain at the forefront. However, given the inherent complexity of these data arising from the technical challenges involved in such assays, development of robust statistical and computational methodologies is of major interest. Therefore, here we present an in-house state-of-the-art scRNA-seq data analyses workflow for de novo lineage tree inference and stem cell identity prediction applicable to many biological processes under current investigation.

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Language(s): eng - English
 Dates: 2019
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: org/10.1007/978-1-4939-9224-9_13
 Degree: -

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Title: Methods in Molecular Biology
  Other : Methods Mol. Biol.
Source Genre: Journal
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Publ. Info: Clifton, N.J. : Humana Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 227 - 301 Identifier: ISSN: 1064-3745
CoNE: https://pure.mpg.de/cone/journals/resource/954927725544