Statistics for Gene Expression

The notes have been updated, and equations can now be read in the black and white version.

Title	Slides	Slides in Handout Format	Reading
Introduction to molecular biology	PDF	PDF	1) The WWW Virtual Library of Cell Biology 2) Nature Genetics Special Issue: the Chipping Forecast
Introduction to DNA Array Technology	PDF	PDF	1) A Concise Guide to cDNA Microarray Analysis, 2) Expression profiling using cDNA microarrays
Pre-processing cDNA Array Data	PDF	PDF	1) Normalization for cDNA Microarray Data
Pre-processing Affymetrix GeneChip Data	PDF	PDF	1) Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. 2) Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data
Statistical considerations	PDF	PDF	1) H Wainer (1984) How to display data badly. American Statistician 38(2):137-147 2) Multiple hypothesis testing in microarray experiments
Differential Expression in Two Populations	PDF	PDF	1) Significance Analysis of Microarrays Tusher, Tibshirani and Chu (2001) PNAS
Classification	PDF	PDF	1) S. Dudoit, J. Fridlyand, and T. P. Speed (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, Vol. 97, No. 457, p. 77-87. 2) Tibshirani, Hastie, Narashiman and Chu (2002): "Diagnosis of multiple cancer types by shrunken centroids of gene expression" . PNAS 2002 99:6567-6572.
Clustering	PDF	PDF
Clustering: extra notes		PDF
Dimension Reduction	PDF	PDF
Annotation		PDF

Project: The class project is described in full detail here [pdf file].

Data-sets and R-scripts, should you wish to try your hand:

Intro to R [text file]

The Golub data is a very famous traning/test microarray dataset. The training data includes 38 samples representing two types of Leukemia, AML and ALL. The test data includes 33 additional samples.

Golub paper [pdf file]
Golub training data set [text file]
training set pheno data [text file]
Golub test data set [text tar file]
test set pheno data [text file]

Affymetrix prepared a large "spike-in" experiment. Several transcripts, both human and bacterial, were added in specific amounts to background human rna that did not contain any of these transcripts. The amounts vary from chip to chip according to a latin square design described in the pheno data. Only spiked in transcripts are differentially expressed, the samples are otherwise identical. Three replicates of each of 3 spikein levels are included in the data here.

Affy spikein data set [text file]
Affy spikein pheno data [text file]
Affy Cel files [compressed archive]

This the the data for the zebrafish experiment included as an example in the Maffy package available from Bioconductor.

Cdna Data [compressed archive]

References There is a great list of microarray references here

Bibliography

Some of the key references from the list are collected here

Short Biboliography

Resources

Class General Info

Course title: Statistics for Gene Expression (140.888)

Instructor: Leslie Cope

Oncology Biostatistics
The course syllabus is here [pdf file].
email: cope@jhu.edu
Grading: Class Project
Course description: Introduces statistical concepts and tools necessary to interpret and critically evaluate the literature on gene expression array data, and perform basic analysis of gene expression array data. Includes an overview of technologies, normalization, identification of diferentially expressed gene, clustering, classification, and statistical pattern recognition.

Acknowledgements Thanks to Elizabeth Garrett, Giovanni Parmigiani and Rafael Irizarry from whom I shamelessly borrowed most course content, including the design of this web page.