Computational Methods for Single-Cell Data Analysis /

Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Yuan, Guo-Cheng. (Editor )
Formato: eBook
Lenguaje:English
Publicado: New York, NY : Springer New York : Imprint: Humana, 2019.
Edición:1st ed. 2019.
Colección:Methods in Molecular Biology, 1935
Materias:
Tabla de Contenidos:
  • Quality Control of Single-cell RNA-seq
  • Normalization for Single-cell RNA-seq Data Analysis
  • Analysis of Technical and Biological Variability in Single-cell RNA Sequencing
  • Identification of Cell Types from Single-cell Transcriptomic Data
  • Rare Cell Type Detection
  • scMCA- A Tool Defines Cell Types in Mouse Based on Single-cell Digital Expression
  • Differential Pathway Analysis
  • Differential Pathway Analysis
  • Estimating Differentiation Potency of Single Cells using Single Cell Entropy (SCENT)
  • Inference of Gene Co-expression Networks from Single-Cell RNA-sequencing Data
  • Single-cell Allele-specific Gene Expression Analysis
  • Using BRIE to Detect and Analyse Splicing Isoforms in scRNA-seq Data
  • Preprocessing and Computational Analysis of Single-cell Epigenomic Datasets
  • Experimental and Computational Approaches for Single-cell Enhancer Perturbation Assay
  • Antigen Receptor Sequence Reconstruction and Clonality Inference from scRNA-seq Data
  • A Hidden Markov Random Field Model for Detecting Domain Organizations from Spatial Transcriptomic Data.