Bioinformatics II
        (Spring 2003)

Meetings: Tue, Thur 2.30-3.45pm, 124 Ag Eg

Instructors:
Francesca Chiaromonte
411 Thomas Bldg, 5-7075, chiaro@stat.psu.edu
Off. Hours: Wed 12.00-1.00pm, Thur 10.00-11.00am
Webb Miller
326A Pond Lab, 5-4551, webb@cse.psu.edu

Syllabus
Questions
Groups

Special announcements

• Class is cancelled on Thur Jan 23rd. A reading assignment is given below.
• On Thur Feb 13th, we will visit the microarray facility (meet in the elevator lobby of Wartik Lab. at 2.30pm)

Notes, general introduction and spotted arrays

Affymetrix arrays:

• Lipshutz R.J., Fodor S.P.A., Gingeras T.R., Lockhart D.J. (1999). High density synthetic oligonucleotide arrays. Nature Genetics supplement, vol. 21, 20-24. (description of affymetrix arrays)
• Affymetrix, Microarray Suite version 5.0 (2002). Statistical Algorithms Reference Guide (single array analysis)
• Complete information: Statistical algorithms description document (available at http://icg.cpmc.columbia.edu/Bioinformatics/MAS_5.pdf )

• Lee M.L., Cuo F.C., Whitmore G.A., Sklar J. (2000). Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. PNAS vol 97, n. 18, 9834-9839.

Reading assignment: Naef F./Magnasco M. et al. (2001), From features to expression: high density oligonucleotide arrays analysis revisited. Write a summary and your comments in 2 pages (max), to hand in on Thur Jan 30th (in class).

2. Preprocessing of Microarray data I: Normalization and Missing Values

Notes

An instance of "global" normalization:

• David B Finkelstein, Rob Ewing, Jeremy Gollub, Fredrik Sterky, Shauna Somerville, J Michael Cherry (2002): "Iterative linear regression by sector", in Methods of Microarray Data Analysis , eds. SM Lin, KF Johnson (Kluwer Academic), pp. 57-68 (Stanford preprint).

• AJ Hartemink, DK Gifford, TS Jaakkola, RA Young, "Maximum likelihood estimation of optimal scaling factors for expression array normalization". SPIE BiOS 2001, San Jose, California (MIT preprint).

• Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., and Altman, R. B. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520-525 (available at http://ismb00.sdsc.edu/9_8_pdfs/SunMethods/Troyanskaya.pdf).
• Guest lecture by Ofer Harel:  Two-Stage Multiple Imputation (slides). For more information on multiple imputation see http://www.multiple-imputation.com

Reading assignment: (this concerns both module 2 and 3) Tsodikov A., Szabo A., Jones D. (2002). Adjustments and measures of differential expression for microarray data. Bioinformatics 18(2), 251-260 (article). Write a summary and your comments in 4 pages (max), to hand in on Thur Mar 6th (in class). Work in groups.
 

3. Preprocessing of Microarray data II: Filtering, differentially expressed genes, other Transformations

Notes

• Dudoit, S., Yang, Y. H, Speed, T. P., and Callow, M. J. (2001). Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica. UC Berkeley Statistics tech. report (postscript)
• Efron B., Tibshirani R., Storey J.D., Tusher V. (2001). Empirical bayes analysis of a microarray experiment. JASA 96(456) 1151-1160.

4. Dimension reduction and microarray data

Notes A
Notes B
Notes C

• Holter, N.S, Mitra, M., Maritan, A., Cieplak, M., Banavar, J.R., Fedoroff N.V., (2000). Fundamental patterns underlying gene expression profiles: Simplicity for complexity. PNAS v. 97, n. 15, 8409-8414.
• Yeast Cell Cycle example (Minitab file). Data from Spellman P.T., Sherlock G., Zhang M.Q., Vishwanath R.I., Anders K.,

Eisen M.B., Brown P.O., Botstein D. (1998), Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces Cerevisiae by Microarray Hybridization, Molecular Biology of the Cell, 9, 3273-3297.
CDC, 15 time points. 678 out of 800 genes (no missing values). 12: restriction to 12 time points. c: use of correlation matrix. s: row-standardization.
• Notes on Multidimensional Scaling, from S. Holmes' website (pdf).

5. Clustering and microarray data

Notes A
Notes B
Notes C

• Yeung K.Y., Ruzzo W.L. (2001): Principal component analysis for clustering gene expression data. Bioinformatics 17 (9) 762-744.
• Yeast Cell Cycle example (Minitab file ) (plots). Data from Spellman et al. CDC, restricted to first 12 time points, row-standardized.
• Some abstracts and papers on clustering (list )
• Bartolucci and Chiaromonte, draft on custering with multivariate normal mixtures (ps).
• Ben-Hur A. Elisseeff A. and Guyon E. (2002) A stability based method for discovering structure in clustered data. PSB 02 (ps).

Reading List

Data analysis assignment: (this concerns both module 4 and 5). Hand in on Tue Apr 22 (in class). Work in groups. Instructions. Data (Excell file).
Data analysis assignment: (for final discussion on Thur May 1). Working in groups, do one of the following
(a) Perform a perturbation analysis to select an appropriate number of clusters (e.g. along the lines proposed by Ben-Hur et al.) for
the Yeast Shock data - previous assignment.
(b) Perform an analysis of your choice (chose the data, preprocess if needed, perform selection of differentially expressed genes,
dimension reduction, clustering, depending on the question you are trying to address).
 
 

Relevant Links