» » Statistical Analysis of Genomic Data

## by G Shu

 Author: G Shu Subcategory: Mathematics Language: English Publisher: John Wiley and Sons Ltd (May 15, 2005) Pages: 550 pages Category: Math and Science Rating: 4.3 Other formats: docx lit mbr rtf

In this chapter we describe methods for statistical analysis of GWAS data with the goal of quantifying evidence for genomic effects associated with trait variation, while avoiding spurious associations due to evidence not being well quantified or due to population structure.

In this chapter we describe methods for statistical analysis of GWAS data with the goal of quantifying evidence for genomic effects associated with trait variation, while avoiding spurious associations due to evidence not being well quantified or due to population structure. Single marker analysis and imputation are discussed in Sect. 1, and a Bayesian multi-locus analysis using the BayesQTLBIC R package (1, 2) is described in Sect. 2. The multi-locus analysis, applied in a genomic window, enables local inference of the QTL genetic architecture and is an alternative to imputation.

In this chapter we describe methods for statistical analysis of GWAS data with the goal of quantifying evidence for genomic effects associated with trait .

This chapter deals with the statistical analysis of genomic, transcriptomic and proteomic data. Emphasis is given to the analysis of gene expression data including hints on experimental designs to sound data generation. Epigenetic variation is also addressed as a mean to enhance the analyses. Do you want to read the rest of this article?

The analysis of cancer genomic data has long suffered the curse of dimensionality. Various methods have been proposed to leverage

The analysis of cancer genomic data has long suffered the curse of dimensionality. The analysis of cancer genomic data has long suffered the curse of dimensionality.

Genomic data analysis necessitates a sound mastery of the underlying mathematical and in particular .

Genomic data analysis necessitates a sound mastery of the underlying mathematical and in particular statistical principles. A common application is the statistical similarities or differences among DNA samples, from animals (including humans) or plants. From the study of similarities, evolutionary model (or trees) relationships can be inferred which is the area of phylogenetics.

This book is an ideal reference for users who want to address massive and complex datasets with novel statistical . He started developing what eventually became the Handbook of Statistical Analysis and Data Mining Applications (co-authored with Drs.

This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions.

We present a series of statistical solutions to challenges that commonly arise in the production and analysis of genomic tag libraries. Tag libraries are collections of fragments of DNA or RNA, with each unique fragment often present in millions or billions of copies

We present a series of statistical solutions to challenges that commonly arise in the production and analysis of genomic tag libraries. Tag libraries are collections of fragments of DNA or RNA, with each unique fragment often present in millions or billions of copies. Inferences can be made from data obtained by sequencing a subset of the library. The statistical approaches outlined in this paper are divided into three parts. First, we demonstrate the application of classical capture-recapture theory to the question of library complexity, . the number of unique fragments in the library.

Many methods have been developed for identifying genes in regression frameworks. However, most of the procedures for identifying the biologically relevant genes do not utilize the known pathway information.

oceedings{, title {Statistical and Functional Analysis of Genomic and .

oceedings{, title {Statistical and Functional Analysis of Genomic and Proteomic Data}, author {Yingchun Liu}, year {2007} }. Yingchun Liu. High-throughput technologies have led to an explosion in the availability of data at the genome scale. Such data provide important information about cellular processes and causes of human diseases, as well as for drug discovery. Deciphering the biologically relevant results from these data requires comprehensive analytical methods. In this dissertation, we present methods for gene and protein expression data analysis.