Statistical Analysis of Next Generation Sequencing Data - Tapa blanda

Sinopsis

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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