Microarray Analysis
Description:
Particular emphasis in ‘Microarray Analysis’ is placed on the understanding, designing and
analysis of different platforms of microarray. This course sheds light on the transcriptomic data
analysis of microarrays experiments that have been obtained from different organisms including
human beings. As microarray data is composed of thousands of variables that may ultimately pose
challenge to the scientist who have to analyze them. In this context, this course will deal with
different types of tests, analysis and visualization techniques that may be used in the analysis of
different types of microarray platforms. Overall, deep understanding of the microarray architecture
and computational analysis will be made available which are required in the fields of biology and
medicine.
Educational Objectives: Primary focus of the program under which the proposed course will be
conducted is
1. Analysis and interpretation of different platforms of microarray data using R statistical language.
2. Computational techniques and algorithms designed for the microarray analysis.
3. Be able to conduct expression microarray data analyses.
4. Biological interpretation of data.
Course Outcomes:
After the course the students will be able to apply different concepts of microarray analysis on various practical problems.
5. Recommended Text/Reference Books:
1. DNA Microarray Analysis Using Bioconductor, Jarno Tuimala CSC, the Finnish IT center for
Science
2. Statistics and Data Analysis for Microarrays Using R and Bioconductor Second Edition SorinDraghici
3. Advanced Analysis of Gene Exression microarray data by Aidong Zhang.
Lecture Plan
S.No. Content
1. Introduction
Overview of Central Dogma of Molecular Biology
High density oligonucleotides
2. Spotted complementary cDNA technologies
High throughput genomic technologies
3. Introduction to microarrays, data analysis and R programming
Microarray platforms
Affymetrix structure and function
4. File formats
Experimental designs
Gene ontology (GO) based enrichment analysis
5. Overview of statistical techniques and practical application using R and
microarray data
MA and Volcano Plotting
Parametric (Pearson, t-test, one way ANOVA)
6. Non-parametric (Spearman, Wilcoxon)Multiple Comparison/FDR Linear Models
7. Data Analysis of Affymetrix Data using Bio-conductor, R and Linux Data pre-processing
Background Correction (RMA Calibration of the Data) Normalization (Different Types and their applications in Microarray analysis), log transformation Data manipulation and quality control
8. Principal component analysis
Annotation of Transcript Cluster NUSE and RLE plots Summarization
9. Analysis of microarrays provided by Illumina using Bio-conductor, R and Linux
10. Analysis of microarrays provided by Agilent using Bio-conductor, R and Linux
One Color Channel
Two color Channel
11. Meta-analysis in microarray analysis
