Statistical Analysis of Microarray Data with R

taught by Sudha Purohit

Brief Description:

This course will acquaint you with the process of analysis of microarray data. You will learn how to preprocess the data, short list the differentially expressed genes, carryout principal component analysis to reduce the dimensionality and to detect interesting gene expression patterns, and clustering of genes and samples. Illustrations of the statistical issues involved at the various stages of the analysis will use real data sets from DNA microarray experiments; background will be provided on the use of Bioconductor.

Instructor(s):
Level: Intermediate

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.

Dates:
October 19, 2012 to November 16, 2012
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

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Statistical Analysis of Microarray Data with R

taught by Sudha Purohit

Aim of Course:

In this course, 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, 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.

This course is a core requirement or elective in the following Program(s) in Analytics and Statistical Studies (PASS):

• Data Mining

• Using R

Prerequisite(s):
Some familiarity with statistical modeling will also be helpful. Either of the following Statistics.com courses would provide useful background in modeling - Regression or Introduction to Predictive Modeling. Participants should also be familiar with basic molecular biology and microarray experiments, including gene expression, transcription, splicing, and translation.

 

Please also read the note at the end of the course outline concerning the course's review materials in biology and statistics, and the time that you should budget for this course.  If you are not skilled in the use of R, Statistics.com's Introduction to R is a prerequiste to this course.

Course Program:

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.

Note: This course is not intended as a comprehensive introduction to either statistics or the biology of genetics. Rather, it is intended for participants who have some background in one or the other or both. Recognizing that this background may be varied, considerable review material is provided in both biology and statistics, as part of the regular course readings, as noted below. It is anticipated that participants will pick and choose to focus their attention on areas of need. The more of this material you need to cover in the review, the more time (perhaps even beyond the projected 15 hours per week) you should budget for the course.

Supplementary Background in Biology: Genome project, structure of eukaryotic cell, DNA, RNA, gene expression, transcription, splicing, translation, microarray experimental setup, quantification of information generated by microarray experiment.

Supplementary Background in Statistics: Descriptive Statistics for univariate data, correlation and regression for bivariate data, basics of statistical hypothesis testing, one sample and two sample t- test, paired t-test, F-test for equality of variances, Welch test, Shapiro - Wilks test, Wilcoxon rank sum test, signed rank test, one way ANOVA, Bartlett's test, problem of multiple hypothesis testing, false discovery rate, principal component analysis, cluster analysis.

HOMEWORK:

Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

Organization of the Course:

This course takes place over the internet at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

All course materials will be provided in the course, including readings, lessons and assignments.

Software:

The software used in course illustrations and assignments is R, an open-source, freely-available statistical programming environment.  Click Here for information on obtaining a free copy.  Participants should download and install the R software prior to the beginning of the course. If you are not confident and comfortable using R software, you should consider taking Statistics.com's Introduction to R as a prerequisite to this course.

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Statistical Analysis of Microarray Data with R

taught by Sudha Purohit

Instructor(s):
Dates:
October 19, 2012 to November 16, 2012
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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