Titlebook: Statistical Analysis of Next Generation Sequencing Data; Somnath Datta,Dan Nettleton Book 2014 Springer International Publishing Switzerla

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The Role of Spike-In Standards in the Normalization of RNA-seq,ion purposes. We propose a novel approach to normalization that can successfully make use of control sequences to remove unwanted effects and lead to accurate estimation of expression fold-changes and tests of differential expression.

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Statistical Analyses of Next Generation Sequencing Data: An Overview,point that several genomes can now be sequenced simultaneously in a single instrument run in under two weeks. Its applications range from detecting transcription factor binding sites and quantifying gene expression to discovering methylation patterns and comparing genomes. We discuss and review some

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Differential Expression Analysis of Complex RNA-seq Experiments Using edgeR,s are used to capture the quadratic mean-variance relationship that can be observed in RNA-seq data. Conditional likelihood methods are used to avoid bias when estimating the level of variation. Empirical Bayes methods are used to allow gene-specific variation estimates even when the number of repli

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Analysis of Next Generation Sequencing Data Using Integrated Nested Laplace Approximation (INLA),ration sequencing count data: it can account for zero-inflation, random effects and correlation across genomic features. We demonstrate its use and provide some insights on its approximations of marginal posteriors. In high-dimension settings like these, INLA is in particular attractive in combinati

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Design of RNA Sequencing Experiments,s the multiphase nature of RNA-seq experiments and point out the utility of intentionally confounding nuisance factors, that inevitably arise in different design phases, with one another. We cover the concepts of biological and technical replication. We show that experimental designs that prioritize

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