Meeting Summary for 9/15/08

Ina discussed how we can apply hypothesis testing to the gene expression data from microarrays. We got to know difference in Z-statistic formula when applied to observations directly versus the mean of observations (which follows the Normal Distribution according to the Central Limit theorem). The rest of the chapter on Hypothesis Testing will be studied individually and an overview of tests will be discussed on Wednesday, when they are applied, how they are applied and differences between tests conducted in same scenarios.

References :
1. X. Cui, G. A. Churchill, "Statistical tests for differential expression in cDNA microarray experiments", (Review), Genome Biology 2003, 4:210.
2. J. A. C. Sterne, G. D. Smith, "Sifting the evidence - what's wrong with significance tests?", (Education and Debate)BMJ, 27 January 2001, Volume 322.

Meeting Summary for 9/10/08

Ina went over a brief summary of Point Estimation and a general overview of Hypothesis Testing. Mike went over a paper he had found on normalization and statistical analysis of microarray experiments. We decided to finish the chapter on Hypothesis Testing, and read some papers on microarray normalization. I've posted the review papers on normalization on Citeulike (tagged by csbstudy and normalization).

Meeting Summary for 9/8/08

Today we talked about data pre-processing for microarrays. We reviewed chapter 12 in Sorin Draghici's "Data Analysis Tools for DNA Microarrays". We also looked at some slides from Dr. Kim's class on the topic. (Archana can you post these as a comment?)

For next time we're going to each look up some review papers on normalization techniques, and hopefully study the pros and cons of each. Archana will look for some information pertaining to chip-chip data. I'm going to review my notes from Stafford's class on M-A plots. Jang is going to summarize the distribution plot he sent in the email last week.

We'll also begin our discussion of hypothesis testing.

Meeting Agenda for 9/8/08

Please add comments to this post to modify the agenda.

1. (Main topic) Discuss data pre-processing for DNA microarray data. Sources include but are not limited to the Knudsen, Draghici and Speed books on my desk.
2. (Archana) Discuss binomial distribution for GO term enrichment.
3. (All) Discuss where the Gamma distribution is used in biology.

Meeting Summary for 9/3/08

We worked through chapter 3 of the Montgomery book and talked about various statistical distributions, i.e. normal, lognormal, gamma, poisson, etc. We also tried to relate them to our area of study, which is elaborated by Archana's post earlier.

For next time we decided to discuss data pre-processing for DNA microarrays. I have a few books on my desk, one by Knudsen, one by Draghici, and one by Speed that could lend some insight. Archana already addressed the other goal of assumption of normal distribution of such data. Next time Archana should also brief us on use of the binomial distribution as it pertains to enrichment of gene ontology terms. We all decided to independently investigate when and where the gamma distribution is used in biology besides the one place we knew already, namely as a step in getting the beta distribution for discrete Bayesian network analysis.

Microarray Data- Normally Distributed ?

I applied the normality tests on the microarray data (expression values for one gene) and it looks like it is NOT normally distributed contrary to our discussion.

The tests are Andersen-Darling & Kolmogorov-Smirnov. Here are the histograms and test results :

Study Group Schedule Fall 2008

Week	Date	Topic
1	9/3	Statistical Distributions
2	9/8	Normalization
2	9/10	Hypothesis Testing
3	9/15	Hypothesis Testing
3	9/17	Hypothesis Testing
4	9/22	Regression Analysis
4	9/24	Regression Analysis
5	9/29	Matrices, Gaussian Elimination, Vector Spaces (Ch 1&2)
5	10/1	Matrices, Gaussian Elimination, Vector Spaces (Ch 1&2)
6	10/6	Orthogonality and Determinants (Ch 3& 4)
6	10/8	Orthogonality and Determinants (Ch 3& 4)
7	10/13	Eigenvalue and EigenVectors (Ch5)
7	10/15	Eigenvalue and EigenVectors (Ch5)
8	10/20	Positive Definite Matrices (Ch6)
8	10/22	Computations with Matrices (Ch7) if possible else Ch 6
9	10/27	Error Estimation
9	10/29	Error Estimation
10	11/3	Feature Selection
10	11/5	Feature Selection
11	11/10	Kernel Methods (Classification)
11	11/12	Kernel Methods (Classification)
12	11/17	EM Optimization (Clustering)
12	11/19	EM Optimization (Clustering)
13	11/24
13	11/26
14	12/1
14	12/3
15	12/8
15	12/10

Textbooks:

Engineering Statistics: Montgomery, Runger, Hubele

Linear Algebra & its Applications: Gilbert Strang