Statistics and data analysis for microarrays using R and bioconductor
Material type:
- 9781439809754
- 572.8636 DRA

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Applied Sciences Library Lending Section | DVD Collection | 572.8636 DRA (Browse shelf(Opens below)) | Available | 112953CD | |
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Applied Sciences Library Lending Section | DVD Collection | 572.8636 DRA (Browse shelf(Opens below)) | Available | 112952CD | |
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Applied Sciences Library Lending Section | Lending Collection | 572.8636 DRA (Browse shelf(Opens below)) | Available | 112952 | |
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Applied Sciences Library Lending Section | Lending Collection | 572.8636 DRA (Browse shelf(Opens below)) | Available | 112953 | |
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Applied Sciences Library Reference Section | Reference Collection | 572.8636DRA (Browse shelf(Opens below)) | Available | 112733 | |
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Applied Sciences Library Lending Section | DVD Collection | 572.8636DRA (Browse shelf(Opens below)) | Available | 112733CD |
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572.86 WEL Recombinant DNA technology | 572.8633 BIO Biological sequence analysis | 572.8636 DRA Statistics and data analysis for microarrays using R and bioconductor | 572.8636 DRA Statistics and data analysis for microarrays using R and bioconductor | 572.8645 KOR DNA replication | 572EDE Basic biochemistry | 572GRE Biological science 2 |
"Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and
The Cell and Its Basic Mechanisms --
Microarrays --
Reliability and Reproducibility Issues in DNA Microarray Measurements --
Image Processing --
Introduction to R --
Bioconductor: Principles and Illustrations --
Elements of Statistics --
Probability Distributions --
Basic Statistics in R --
Statistical Hypothesis Testing --
Classical Approaches to Data Analysis --
Analysis of Variance (ANOVA) --
Linear Models in R --
Experiment Design --
Multiple Comparisons --
Analysis and Visualization Tools --
Cluster Analysis --
Quality Control --
Data Pre-Processing and Normalization. Methods for Selecting Differentially Regulated Genes --
The Gene Ontology (GO) --
Functional Analysis and Biological Interpretation of Microarray Data --
Uses, Misuses, and Abuses in GO Profiling --
A Comparison of Several Tools for Ontological Analysis --
Focused Microarrays --
Comparison and Selection --
ID Mapping Issues --
Pathway Analysis --
Machine Learning Techniques
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