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Statistical Analysis of Microarray Data with R

Instructor(s):

Dates:

April 18, 2014 to May 16, 2014

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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 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.

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

WEEK 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 to preprocess affydata

WEEK 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
  • Moderated t-test and empirical Bayes approach of limma package of
    bioconductor
  • One way ANOVA
  • Numerical illustration for 2-8 with complete set of annotated R-commands.

WEEK 3: Multivariate Techniques

  • Principal component analysis

WEEK 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.

In addition to assigned readings, this course also has  lecture notes, end of course data modeling project, and supplemental readings available online.

Statistical Analysis of Microarray Data with R

Be sure you meet all of the minimum requirements before you register, click here to learn more.

Instructor(s):

Dates:
April 18, 2014 to May 16, 2014

Course Fee: $629

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)


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Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

Statistical Analysis of Microarray Data with R

taught by Sudha Purohit

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.

Level:

Intermediate

Prerequisite:
These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

Participants should also be familiar with basic molecular biology and microarray experiments, including gene expression, transcription, splicing, and translation.  Please 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.

Organization of the Course:

This course takes place online 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 R Programming Intro 1 as a prerequisite to this course.


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