Computational methods for single-cell data analysis / edited by Guo-Cheng Yuan.

Format
Book
Language
English
Published/​Created
New York : Humana Press ; Springer, [2019]
Description
x, 271 pages : illustrations ; 26 cm.

Availability

Copies in the Library

Location Call Number Status Location Service Notes
ReCAP - Remote StorageQH506 .M45 1984 vol.1935 Browse related items Request

    Details

    Subject(s)
    Editor
    Library of Congress genre(s)
    Series
    Bibliographic references
    Includes bibliographical references and index.
    Contents
    • Quality control of single-cell RNA-seq / Peng Jiang
    • Normalization for single-cell RNA-seq data analysis / Rhonda Bacher
    • Analysis of technical and biological variability in single-cell RNA sequencing / Beomseok Kim, Eunmin Lee, and Jong Kyoung Kim
    • Identification of cell types from single-cell transcriptomic data / Karthik Shekhar and Vilas Menon
    • Rare cell type detection / Lan Jiang
    • scMCA : a tool to define mouse cell types based on single-cell digital expression / Huiyu Sun, Yincong Zhou, Lijiang Fei, Haide Chen, and Guoji Guo
    • Differential pathway analysis / Jean Fan
    • Pseudotime reconstruction using TSCAN / Zhicheng Ji and Hongkai Ji
    • Estimating differentiation potency of single cells using single-cell entropy (SCENT) / Weiyan Chen and Andrew E. Teschendorff
    • Inference of gene co-expression networks from single-cell RNA-sequencing data / Alicia T. Lamere and Jun Li
    • Single-cell allele-specific gene expression analysis / Meichen Dong and Yuchao Jiang
    • Using BRIE to detect and analyze splicing isoforms in scRNA-Seq data / Yuanhua Huang and Guido Sanguinetti
    • Preprocessing and computational analysis of single-cell epigenomic datasets / Caleb Lareau, Divy Kangeyan, and Martin J. Aryee
    • Experimental and computational approaches for single-cell enhancer perturbation assay / Shiqi Xie and Gary C. Hon
    • Antigen receptor sequence reconstruction and clonality inference from scRNA-Seq data / Ida Lindeman and Michael J. T. Stubbington
    • Hidden markov random field model for detecting domain organizations from spatial transcriptomic data / Qian Zhu.
    ISBN
    • 9781493990566 (hardcover)
    • 149399056X (hardcover)
    LCCN
    2018967307
    OCLC
    1057652965
    Statement on language in description
    Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage. Read more...
    Other views
    Staff view