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Computational Methods for Single-Cell Data Analysis

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Cover of 'Computational Methods for Single-Cell Data Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Archean Rare-Metal Pegmatites
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    Chapter 1 Quality Control of Single-Cell RNA-seq
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    Chapter 2 Normalization for Single-Cell RNA-Seq Data Analysis
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    Chapter 3 Analysis of Technical and Biological Variability in Single-Cell RNA Sequencing
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    Chapter 3 Petrography and Mineralogy
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    Chapter 4 Smart Tourism Destination in Smart Cities Paradigm: A Model for Antalya
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    Chapter 4 Geochemistry of LCT Pegmatites
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    Chapter 4 Identification of Cell Types from Single-Cell Transcriptomic Data
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    Chapter 5 Rare Cell Type Detection
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    Chapter 5 Geochronology of Archean LCT Pegmatites
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    Chapter 6 Radiogenic and Stable Isotopes, Fluid Inclusions
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    Chapter 6 scMCA: A Tool to Define Mouse Cell Types Based on Single-Cell Digital Expression
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    Chapter 7 Differential Pathway Analysis
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    Chapter 7 Genesis of Massive Pollucite Mineralisation in Archean LCT Pegmatites
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    Chapter 8 Pseudotime Reconstruction Using TSCAN
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    Chapter 9 Estimating Differentiation Potency of Single Cells Using Single-Cell Entropy (SCENT)
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    Chapter 10 Inference of Gene Co-expression Networks from Single-Cell RNA-Sequencing Data
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    Chapter 11 Single-Cell Allele-Specific Gene Expression Analysis
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    Chapter 12 Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data
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    Chapter 13 Preprocessing and Computational Analysis of Single-Cell Epigenomic Datasets
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    Chapter 14 Experimental and Computational Approaches for Single-Cell Enhancer Perturbation Assay
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    Chapter 15 Antigen Receptor Sequence Reconstruction and Clonality Inference from scRNA-Seq Data
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    Chapter 16 A Hidden Markov Random Field Model for Detecting Domain Organizations from Spatial Transcriptomic Data
Attention for Chapter 15: Antigen Receptor Sequence Reconstruction and Clonality Inference from scRNA-Seq Data
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Chapter title
Antigen Receptor Sequence Reconstruction and Clonality Inference from scRNA-Seq Data
Chapter number 15
Book title
Computational Methods for Single-Cell Data Analysis
Published by
Humana Press, New York, NY, February 2019
DOI 10.1007/978-1-4939-9057-3_15
Pubmed ID
Book ISBNs
978-1-4939-9056-6, 978-1-4939-9057-3
Authors

Ida Lindeman, Michael J. T. Stubbington, Lindeman, Ida, Stubbington, Michael J. T.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 26%
Student > Ph. D. Student 4 21%
Student > Bachelor 2 11%
Other 1 5%
Professor 1 5%
Other 2 11%
Unknown 4 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 37%
Immunology and Microbiology 4 21%
Medicine and Dentistry 3 16%
Economics, Econometrics and Finance 1 5%
Agricultural and Biological Sciences 1 5%
Other 0 0%
Unknown 3 16%