Wednesday 11 April 2012

Statistical Analysis of Microarray Data with R


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).

For India Registration and pricing, please visit us at www.india.statistics.com.

If you have any query please feel free to call me or write to me. 

For More details contact at
Call: 020 66009116

Websites:

Acceptance Sampling for Quality Control


Sampling plans are used in industry world-wide as a means of assisting quality assurance personnel to decide whether to accept or reject lots of products presented for inspection. Many organizations require the use of ISO standards for assuring the quality of incoming (or outgoing) batches of goods. The standards provide inspectors with information on the size of sample to be selected and a set of rules and procedures, which enables the inspector, having examined the sample, to decide on the acceptance or rejection of the lot. Based on statistical principles, sampling standards provide organisations with an effective and cost efficient means of selecting sample sizes and devising sampling plans. This is covered in Dr. Galit Shmueli’s online course “Acceptance Sampling for Quality Control”. For more detail please visit at http://www.statistics.com/sampleinspect.

In this course, participants will become familiar with popular sampling inspection plans for attribute data, measurement data, and continuous processes. They will understand the statistical logic behind ISO inspection plans and learn how to use the standards, and how to choose and employ such plans judiciously. The course will also introduce participants to a convenient interactive Web resource for obtaining standard inspection plans.

Dr. Galit Shmueli is the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics and Information Systems at the Indian School of Business, Hyderabad. She is also Associate Professor of Statistics in the department of Decision, Operations & Information Technologies at the Smith School of Business, University of Maryland. Dr. Shmueli's research has been published in the statistics, information systems, and marketing literature; she is a co-author of Data Mining for Business IntelligenceModeling Online AuctionsStatistical Methods in e-Commerce Research, and author of Practical Time Series Forecasting and Practical Acceptance Sampling.

Who Should Take This Course?
This course will benefit quality engineers and, quality inspectors and other quality control personnel who employ or supervise inspection plans at their organization, as well as quality assurance staff in charge of setting quality standards. Six Sigma black belts and trainers can also benefit from this course.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Introduction to Acceptance Sampling, and Single-stage Plans for Attribute Data
  • Acceptance Sampling terminology
    • Acceptable Quality Level (AQL)
    • Lot Tolerance Percent Defective (LTPD)
    • Probability of Acceptance
    • Producer and consumer risks
    • Sampling plans parameters
  • Binomial and Hypergeometric distribution computations
  • Single-stage plans for pass/fail data
    • Operating Characteristics (OC) Curve
    • Effects of sample size and rejection criteria
    • Using ISO 2859 (ANSI/ASQC Z1.4, Mil-Std 105) Tables
    • Using interactive plans (www.sqconline.com)
  • Single stage plans for multiple defects
SESSION 2: Double-stage Plans and Rectifying Plans for Attribute Data
  • Double-stage sampling plans
    • Average Sample Number (ASN)
    • Using ISO 2859 (ANSI/ASQC Z1.9, Mil-Std 414) Tables
    • Using SQCOnline interactive plans
  • Rectifying sampling palns for pass/fail data
    • Average outgoing quality (AOQ)
    • Average Total Inspection (ATI)
    • Single and double-stage plans
SESSION 3: Sampling Plans for Variables (Measurement Data)
  • Single and double specification limit
  • Probability of acceptance
  • k-Method and M-Method
  • Sigma known and unknown
  • Using ANSI/ASQC Z1.9 (Mil-Std 414) Tables
  • Using interactive plans (www.sqconline.com)
SESSION 4: Continuous Sampling Plans
  • Sampling frequency and clearance number
  • CSP-1 and CSP-2 plans
  • Rectifying and non-rectifying plans
  • Average Outgoing Quality Limit (AOQL)
  • Average Fraction Inspection (AFI)
  • Using Mil-Std 1235C Tables and interactive plans (www.sqconline.com)
  • Skip-lot sampling

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).

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 66009116

Websites:

Thursday 5 April 2012

Introduction to Quantitative Risk Analysis


To his short list of eternal ills (death and taxes), Benjamin Franklin could have added "risk."  While bad for the economy, risk and uncertainty are good for statisticians - much of what we do would be unnecessary in their absence. 

"Introduction to Quantitative Risk Analysis" (Groenendaal and Nolder) covers how to frame a risk analysis problem, best practices of risk modeling, selecting the appropriate probability distribution, using data and expert opinion, and presenting risk analysis results.  The course will also cover common mistakes made when doing quantitative risk analysis and how to avoid them. For more details please visit at
http://www.statistics.com/course-catalog/risk/

Huybert Groenendaal is Managing Partner at EpiX Analytics, helps clients in industry and government with financial investment evaluations, project risk analysis, forecasting, operations, transportation logistics, epidemiology and more.  He also organizes and teaches risk analysis courses and workshops worldwide and in the executive MBA program of the University of Texas at Dallas.

Greg Nolder is an experienced risk analysis consultant, working with clients in computer hardware, pharmaceuticals, banking, finance, construction, mining, oil & gas, chemicals, consumer packaged goods, transportation and engineering.

Aim of Course:
This course will cover the most important principles, techniques and tools in Quantitative Risk Analysis. The focus of the course is on how to conduct accurate and effective risk analyses, including framing a risk analysis problem, best practices of risk modeling, selecting the appropriate probability distribution, using data and expert opinion, and presenting risk analysis results. In addition, the course will cover an introduction to probability and statistics theory and various stochastic processes, which is critically important to a solid understanding of quantitative risk analysis.

The course will also familiarize participants with risk analysis modeling environments (in this case ModelRisk with Excel, but the lessons and techniques apply equally well to other modeling environments). The course will also cover common mistakes made when doing quantitative risk analysis and how to avoid them.

Who Should Take This Course:
Anyone in business, government and science with an interest in quantitative risk analysis such as professionals needing to perform quantitative risk analysis in areas indcluding, but not limited to, finance, business development, economics, operations, engineering, six sigma, project risk analysis, marketing, epidemiology and microbiology.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Introduction to Risk Analysis

  • Core ideas of risk analysis
  • Going from data to knowledge to a decision-making tool
  • Introduction to statistical descriptors
    • Mean, mode, standard deviation, skewness, kurtosis, percentiles
  • Probability concepts

 

SESSION 2: Probability Theory

  • Graphical representations of risk events: Venn diagrams, fault trees and event trees
  • Introduction to risk modeling
    • Monte Carlo simulation, ModelRisk and Excel
    • Brief tutorial on ModelRisk
    • Calculation vs. simulation - the pros and cons of Monte Carlo

 

SESSION 3: Building Risk Analysis Models

  • Most commonly used probability distributions
  • Good practices in risk modeling
  • Common mistakes and how to prevent them

 

SESSION 4: Presenting Results

  • Typical risk analysis results, their presentation and interpretation
  • Example quantitative risk analyses, including:
  • Project costs risk analysis
    • Engineering
    • Marketing
    • Operations
    • Financial risk analysis
    • Health and Epidemiology

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).

For India Registration and pricing, please visit us at www.india.statistics.com.

If you have any query please feel free to call me or write to me. 

For More details contact at
Call: 020 66009116

Websites: