Statistical Analysis of DNA Microarray Data
Course Materials:
- Syllabus
- Reading
List html doc
- Homework
- Code
| Familiar with the dchip software html doc | |
| Homework #1 from Steve | |
| Steve’s R code for k-means, k-medoid and hierarchical clustering from 2/13’s lecture | |
| Steve’s S-plus code for PCA and MDS analysis from 2/25’s lecture |
| Jun Dong's presentation for the paper by K. Y. Yeung and W. L. Ruzzo | ||
| PowerPoint presentation for the paper by Golub and Lander | ||
| PowerPoint presentation for the paper by Heping Zhang | ||
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PRINCIPAL COMPONENT ANALYSIS: Yijing Shen (statistics) |
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K. Y. Yeung and W. L. Ruzzo Principal component analysis for clustering gene expression data Bioinformatics 2001 17: 763-774. |
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CLUSTERING: Andy Ming Ham Yip (math department) |
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Tibshirani R, Walther G, Hastie T (2000) Estimating the Number of Clusters in a Dataset via the Gap Statistic. Technical report, Department of Biostatistics, Stanford University . | |
RECURSIVE PARTITIONING : Weihua Huang (statistics) |
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Heping Zhang, Chang-Yung Yu, Burton Singer, Momiao Xiong (2001),"Recursive partitioning for tumor classification with gene expression microarray data", Proceedings of the National Academy of Sciences, 98(12):6730-6735 |
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STANDARD MICROARRAY DATA ANALYSIS: Stephanie Tsung (biostats) |
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Ruty Shai, Tao Shi, Thomas J Kremen, Steve Horvath, Linda M Liau, Timothy F Cloughesy, Paul S Mischel, and Stanley F Nelson (2003) Gene expression profiling identifies molecular subtypes of gliomas. Oncogenomics. Vol 22, No 31, Pages 4918-4923. Messages: gene voting, MDS plots, leave one out error, gene filtering. |
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GENE VOTING and more : Ying-Chia Cheng |
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TR Golub, DK Slonim, P Tamayo, C Huard, M Caasenbeek, JP Mesirov, H Coller, ML Loh, JR Downing, MA Caligiuri, CD Bloomfield, ES Lander (1999),"Molecular classification of cancer: class discovery and class prediction by gene expression monitoring", Science, 286:531-537. Related technical report (postscript): Class Prediction and Discovery using Gene Expression Data. The published version of this report appeared in the Proceedings of the fourth annual international conference on computational molecular biology April 8 - 11, 2000 , Tokyo Japan . RECOMB 2000, p263-272, 2000 |
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COMPARING DIFFERENT PREDICTORS : Tun-Hsiang Yang |
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Sandrine Dudoit, Jane Fridlyand, Terry Speed, "Comparison of discrimination methods for the classification of tumors using gene expression data", preprint #576 (Statistics Dept, UC Berkeley, June 2000). See also the JASA article. |
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| Hierarchical Clustering | ||
| Kmeans Clustering | ||
Addictional lectures notes that were in part found on the internet |
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| Supervised learning | ||
| Self organizing maps | ||
| Boosting | ||
| Tree-based methods for Analyzing Tissue Microarray Data | ||
Data Set:
- data
set for Steve’s homework #1
- comma delimited array data
Some Introductory Papers
1. M Schena, D Shalon, R Heller, A Chai, PO Brown, RW Davis (1996)
"Parallel human genome analysis: microarray-based expression monitoring of 1000 genes",
Proceedings of the National Academy of Sciences, 93(20):10614-10619.
2. JL DeRisi, VR Iver, PO Brown (1997),
"Exploring the metabolic and genetic control of gene expression on a genomic scale",
Science, 278(5338):680-686.
3. DA Lashkari, JL DeRisi, JH McCusker, AF Namath, C Gentile, SY Hwang, PO Brown, RW Davis (1997),
"Yeast microarrays for genome wide parallel genetic and gene expression analysis",
Proceedings of the National Academy of Sciences, 94(24):13057-13062.
4. R Lipshutz, S Fodor, T Gingeras, D Lockhart (1999), “High density synthetic oligonucleotide arrays”,
Nature Genetics, 21(1 suppl):20-24
5 . P Brown, D Bostein (1999),
"Exploring the new world of the genome with DNA microarrays",
Nature Genetics, 21(1 suppl):33-37.
Instructors:
Professor David Elashoff
Rm: 21-254C
Ph# 794-7835
Professor Steve Horvath
Rm: 51-236
Ph# 825-9299
Copyright © 2002 Tao Shi,
Department of Human Genetics, UCLA. All Rights Reserved
Last Revision: 2/27/04
Contact: Ai Li