Friday, 5 October 2012

Statistical Analysis of Microarray Data with R

Do you like to learn the statistical tools required for the analysis of microarray data, how to apply them using R software and how to interpret the results meaningfully. We will review the biology relevant to microarray data, then cover microarray experiment set up, quantification of information generated from the experiment, pre-processing of data including statistical tools for between array and within array normalization, statistical inference procedures to identify differentially expressed genes under two different conditions, and its extension to situations involving more than two conditions. The course will also introduce multivariate statistical methods, such as principal component analysis & cluster analysis. These methods help to identify differentially expressed genes, sets of co-regulated genes, which in turn will help to assign functions to genes. An appendix is provided which will introduce the bioconductor packages and their use in the analysis of Affy data and two commonly used designs viz. Dye swap and Reference.

Learn with Dr. Sudha Purohit in online course " Statistical Analysis of Microarray Data with R" at For more details please visit at

Who Should Take This Course:
Biologists and geneticists who need to use statistical methods to analyze microarray data; also computer scientists and statisticians involved in microarray analysis projects. The course is designed to bridge the gap between several disciplines by providing the necessary information to participants with varied background.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Microarrays and Normalization; Bioconductor
  • Microarray experimental set up and quantification of information available from microarray experiments.
  • Data cleaning.
  • Transformation of data.
  • Between array and within array normalization.
  • Concordance coefficients and their use in normalization.
  • Numerical illustration for 4-6 with complete set of annotated R-commands.
  • Instructions for the use of Bioconductor

SESSION 2: Statistical Inference Procedures in Comparative Experiments
  • Basics of statistical hypothesis testing.
  • Two sample t- test.
  • paired t-test.
  • Tests for validating assumptions of t-test.
  • Welch test.
  • Wilcoxon rank sum test, signed rank test.
  • Adjustments for Multiple hypotheses testing including false discovery rate.
  • Numerical illustration for 2-8 with complete set of annotated R-commands.
  • One way ANOVA.

SESSION 3: Multivariate Techniques
  • Principal component analysis.

SESSION 4: Clustering
  • Cluster analysis.

The instructor, Dr. Sudha Purohit is visiting Lecturer in Statistics at the University of Pune and, before her retirement in 2000, was Head of the Department of Statistics at A. G. College, Pune, India. Her areas of interest are survival analysis, reliability, programming with R and analysis of microarray data. She has published a number of research papers in various peer-reviewed journals.

You will be able to ask questions and exchange comments with Dr. Sudha Purohit via a private discussion board throughout the course.   The courses take place online at in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

For India Registration and pricing, please visit us at

Call: 020 66009116


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