Statistical Methods for Microarray Data Analysis

Methods and Protocols
2013
ISBN: 978-1-60327-336-7
Verlag: Springer

Buch, Englisch, Band 972, 212 Seiten, HC gerader Rücken kaschiert, Format (B × H): 183 mm x 260 mm, Gewicht: 5709 g

Reihe: Methods in Molecular Biology

Microarrays for simultaneous measurement of redundancy  of RNA species are used in fundamental biology as well as in medical research. Statistically,a microarray may be considered as an observation of very high dimensionality equal to the number of expression levels measured on it. In Statistical Methods for Microarray Data Analysis: Methods and Protocols, expert researchers in the field detail many methods and techniques used to study microarrays, guiding the reader from microarray technology to statistical problems of specific multivariate data analysis. Written in the highly successful Methods in Molecular Biology™ series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory.

 

Thorough and intuitive, Statistical Methods for Microarray Data Analysis: Methods and Protocols aids scientists in continuing to study  microarrays and the most current statistical methods.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Yakovlev, Andrei Y.

Gaile, Daniel

Klebanov, Lev

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Naturwissenschaften Biowissenschaften Molekularbiologie
• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik

Inhaltsverzeichnis

What Statisticians Should Know About Microarray Gene Expression Technology.- Where Statistics and Molecular Microarray Experiments Biology Meet.- Multiple Hypothesis Testing: A Methodological Overview.- Gene Selection with the d-sequence Method.- Using of Normalizations for Gene Expression Analysis.- Constructing Multivariate Prognostic Gene Signatures with Censored Survival Data.- Clustering of Gene-Expression Data via Normal Mixture Models.- Network-based Analysis of Multivariate Gene Expression Data.- Genomic Outlier Detection in High-throughput Data Analysis.- Impact of Experimental Noise and Annotation Imprecision on Data Quality in Microarray Experiment.- Aggregation Effect in Microarray Data Analysis.- Test for Normality of the Gene Expression Data.