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A Beginner Guide Microarray Gene Expression Data Analysis 1st Edition by Helen Causton, John Quackenbush, Alvis Brazma ISBN 1405106824 9781405106825

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Microarray Gene Expression Data Analysis 1st Edition Helen Causton John Quackenbush Alvis Brazma Digital Instant Download

Author(s): Helen Causton John Quackenbush Alvis Brazma
ISBN(s): 1405106824
Edition: 1
File Details: PDF, 60.13 MB
Year: 2003
Language: english
SKU: EB-2143410 Category: Tag:

A Beginner Guide Microarray Gene Expression Data Analysis 1st Edition by Helen Causton, John Quackenbush, Alvis Brazma – Ebook PDF Instant Download/Delivery: 1405106824, 9781405106825
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ISBN 10: 1405106824 
ISBN 13: 9781405106825
Author: Helen Causton, John Quackenbush, Alvis Brazma

This guide covers aspects of designing microarray experiments and analysing the data generated, including information on some of the tools that are available from non-commercial sources. Concepts and principles underpinning gene expression analysis are emphasised and wherever possible, the mathematics has been simplified. The guide is intended for use by graduates and researchers in bioinformatics and the life sciences and is also suitable for statisticians who are interested in the approaches currently used to study gene expression. Microarrays are an automated way of carrying out thousands of experiments at once, and allows scientists to obtain huge amounts of information very quickly Short, concise text on this difficult topic area Clear illustrations throughout Written by well-known teachers in the subject Provides insight into how to analyse the data produced from microarrays

A Beginner Guide Microarray Gene Expression Data Analysis 1st Table of contents:

  • Chapter 1: Understanding Microarray Data

    • Raw Data from Microarrays: What You Need to Know

    • Types of Microarrays: cDNA Arrays vs. Oligonucleotide Arrays

    • Data Format and Structure (e.g., CEL files, Gene Expression Matrix)

    • Quality Control and Preprocessing of Microarray Data

  • Chapter 2: Preprocessing Microarray Data

    • Data Normalization: Methods and Importance

    • Background Correction and Signal Transformation

    • Filtering Low-Quality Data and Outlier Detection

    • Handling Missing Data in Gene Expression Datasets

    • Batch Effect Correction

  • Chapter 3: Exploratory Data Analysis (EDA)

    • Visualizing Microarray Data: Heatmaps, Boxplots, and PCA

    • Clustering Methods: K-Means and Hierarchical Clustering

    • Principal Component Analysis (PCA) for Dimensionality Reduction

    • Identifying Patterns and Trends in Gene Expression Data

  • Chapter 4: Statistical Methods for Gene Expression Analysis

    • Basic Statistical Concepts: p-values, T-tests, and ANOVA

    • Differential Gene Expression Analysis: Limma and EdgeR

    • Statistical Models for Gene Expression Data

    • Adjusting for Multiple Comparisons (FDR, Bonferroni Correction)

    • Understanding Statistical Significance and Biological Relevance

  • Chapter 5: Biological Interpretation of Gene Expression Data

    • Gene Ontology (GO) and Pathway Enrichment Analysis

    • Functional Annotation and Gene Set Enrichment Analysis (GSEA)

    • Mapping Differentially Expressed Genes to Biological Pathways

    • Understanding Gene Networks and Regulatory Mechanisms

  • Chapter 6: Tools and Software for Microarray Data Analysis

    • Introduction to Popular Software for Gene Expression Analysis

    • Using R and Bioconductor for Data Analysis

    • Overview of Commercial Software Packages (e.g., GeneSpring, Partek)

    • Workflow for Analyzing Microarray Data with Open-Source Tools

    • Tips for Using Web-Based Tools for Data Analysis

  • Chapter 7: Advanced Topics in Microarray Analysis

    • Time-Series Analysis of Gene Expression Data

    • Integrating Microarray Data with Other Omics Data (e.g., Proteomics, Metabolomics)

    • Multi-Omics Approaches in Systems Biology

    • Cross-Platform Data Integration

  • Chapter 8: Practical Case Studies

    • Case Study 1: Analyzing Gene Expression in Cancer Research

    • Case Study 2: Understanding Differential Gene Expression in Drug Treatment

    • Case Study 3: Microarray Data Analysis in Environmental or Ecological Studies

    • Lessons Learned and Best Practices

  • Chapter 9: Challenges and Limitations in Microarray Analysis

    • Common Pitfalls in Microarray Data Analysis

    • Sources of Bias and Variability

    • Dealing with Low-Quality Data

    • Ethical Considerations in Gene Expression Studies

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Tags: Helen Causton, John Quackenbush, Alvis Brazma, Microarray Gene, Data Analysis