Statistical analysis of next generation sequencing data / edited by Somnath Datta, Dan Nettleton.

Springer eBooks

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.

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