Statistical Analysis of Next Generation Sequencing Data
Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical in...
| Συγγραφή απο Οργανισμό/Αρχή: | |
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| Άλλοι συγγραφείς: | , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
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| Σειρά: | Frontiers in Probability and the Statistical Sciences
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| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- Statistical Analyses of Next Generation Sequencing Data: An Overview
- Using RNA-seq Data to Detect Differentially Expressed Genes
- Differential Expression Analysis of Complex RNA-seq Experiments Using edgeR
- Analysis of Next Generation Sequencing Data Using Integrated Nested Laplace Approximation (INLA)
- Design of RNA Sequencing Experiments
- Measurement, Summary, and Methodological Variation in RNA-sequencing
- Functional PCA for differential expression testing with RNA-seq data
- Mapping of Expression Quantitative Trait Loci using RNA-seq Data
- The Role of Spike-In Standards in the Normalization of RNA-seq
- Cluster Analysis of RNA-sequencing Data
- Classification of RNA-seq Data
- Isoform Expression Analysis Based on RNA-seq Data
- RNA Isoform Discovery Through Goodness of Fit Diagnostics
- MOSAiCS-HMM: A Model-based Approach for Detecting Regions of Histone Modifications from ChIP-seq Data
- Hierarchical Bayesian Models for ChIP-Seq Data
- Genotype Calling and Haplotype Phasing from Next Generation Sequencing Data.- Analysis of Metagenomic Data
- Detecting Copy Number Changes and Structural Rearrangements using DNA Sequencing
- Statistical Methods for the Analysis of Next Generation Sequence Data from Paired Tumor-Normal Samples
- Statistical Considerations in the Analysis of Rare Variants.
