Participants will 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, preprocessing of data including statistical tools for between array and within array normalization and introduction to bioconductor, use of bioconductor packages for preprocessing of affydata. This will be followed by statistical inference procedures to identify differentially expressed genes under two different conditions, and its extension to situations involving more than two conditions using classical t- test and anova. Furthermore we include use of limma package of bioconductor to identify the differentially expressed genes in two and more conditions. This will be followed by discussion of two commonly used microarray specific designs and identification of differentially expressed genes using marray and limma packages of bioconductor. The course will also introduce multivariate statistical methods, such as principal component analysis and 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.
April 17, 2015 to May 15, 2015
About 15 hours per week, at time of your choosing.
INR 37,740 (assuming $ = INR 60)
Full Time/ Part time:
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.