The "Southern Blot,"
commonly regarded as the predecessor to today's genetic microarray analysis,
originated in the 1970's (the term refers to its creator - Edwin
Southern). Since that time, this field
has become a massive data-generating machine, and statistical analysis is a key
component of that machine. You can get a statistical introduction to the topic
in Dr. Sudha Purohit's online course "Statistical Analysis of Microarray
Data Using R," at statistics.com.
For more details please visit at http://www.statistics.com/course-catalog/microarray/.
In "Statistical Analysis of Microarray
Data Using R", Dr. Purohit will cover the statistical methods used to
analyze microarray data, how to apply them using R software, and how to
interpret the results meaningfully.
This course will inform 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.
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. She is a
co-author of three books, "Life-Time Data: Statistical Models and
Methods", "Introduction to Biometry", and (with Dr. Shailaja
Deshmukh) "Microarray Data: Statistical Analysis Using R". She is a
coauthor (jointly with Prof. Shailaja Deshmukh and Dr. Sharad Gore) of
"Statistics Using R". 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.
Participants can ask questions and exchange comments with Dr. Purohit on a
private discussion board throughout the period.
Aim
of the 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.
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: Background
of Microarrays and Normalization
- 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.
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.
You will be able to ask questions and
exchange comments with the instructors via a private discussion board
throughout the course. The courses take place online at
statistics.com 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 statistics.com
accepts registration for its courses at special prices in Indian Rupees through
its partner, the Center for eLearning and Training (C-eLT), Pune (www.c-elt.com).
If you have any query
please feel free to call me or write to me.
For More details contact
at
Email: info@c-elt.com
Call: 020 66009116
Websites:
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