Computational Methods for Single-Cell Data Analysis
This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-ty...
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| Format: | eBook |
| Language: | English |
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New York, NY
Humana
2019, 2019
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| Edition: | 1st ed. 2019 |
| Series: | Methods in Molecular Biology
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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 Modelfor Detecting Domain Organizations from Spatial Transcriptomic Data