Statistical Analysis of Microarray Data with R

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.

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:
April 20, 2012 to May 18, 2012October 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

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 tools, such as principal component analysis & cluster analysis. These tools help to identify differentially expressed genes, sets of co-regulated genes, which in turn will help to assign functions to genes.

Prerequisite(s):
Some familiarity with statistical modeling will also be helpful. Any of the following statistics.com courses would provide useful background in modeling: Regression, Logistic Regression, Introduction to Data Mining. 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. Also, please note the use of R software, as described below. 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: Background of Microarrays and Normalization

 

• Microarray experimental set up and quantification of information available from microarray experiments.
• Data cleaning.
• Transformation of data.
• Between array & within array normalization.
• Concordance coefficients and their use in normalization.
• Numerical illustration for 4-6 with complete set of annotated R-commands.

 

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.

Organization of the Course:

This course takes place over the internet, at statistics.com 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 you will receive individual feedback on your homework answers.

Credit:
Students come to The Institute for a variety of reasons:

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 (Program in Advanced 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).

As you begin the class, you will be asked to specify your category.

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a record of course completion will be issued by Statistics.com, 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

Instructor(s):
Dates:
April 20, 2012 to May 18, 2012October 19, 2012 to November 16, 2012
Course Fee: $499
Academic Discounted 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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