A Report on CAMDA’01 Biointelligence Lab School of Computer Science and Engineering Seoul National University Kyu-Baek Hwang and Jeong-Ho Chang

Outline Critical Assessment of Microarray Data Analysis Techniques 2001  Oct. 15 ~ 16 at Duke University in Durham, NC.  Organized by Duke Bioinformatics Shared Resource.  16 papers were presented at the conference including 12 oral presentations.  Vendor fair (Silicon Genetics, Packard BioScience, SPSS Science, and Stratagene BioCrest)  Data Set  Rosetta compendium on the yeast (4)  NCI60 cell lines data set on the human cancer (11)  1 (?)

Oct. 15 (1/4) Keynote (R. Stoughton, Rosetta InPharmatics)  “Lessons learned” from researches on microarray data analysis  Error model hierarchy  Single spot (pixel intensities, unbiased control)  Single transcript (reproducibility)  Array repeats (incorporation)  Large data sets (pattern matching  no free lunch) –Distance metric  the goal  Biclustering of genes and experiments  Find some small block in a dendro matrix  Null hypothesis problem  Automated annotation (PubMed + Unigene, Swiss Prot, etc.)  Inkjet oligo (25,000 oligos / 1  3 inches, sensitive 60 mers)

Oct. 15 (2/4) Keynote (cont’d)  Standard Dataset Project (SDS) by Dec.,  200 arrays  ~ 2,000 genes  5 exons  2 probes (experimentally confirmed for differential expression)  Data, clusters, literature DB  (annotating engine) annotated clusters Application of Bayesian Decomposition to Gene Expression Analysis of Deletion Mutation Data  Matrix decomposition by MCMC simulation (Rosetta data)  Clustering based on the multiple cluster membership

Oct. 15 (3/4) Using Functional Genomic Units to Corroborate User Experiments with the Rosetta Compendium  Functional genomic units  ICA or latent variables  Published data + own experimental data Biologically-Driven Clustering of Microarray Data: Applications to the NCI60 Data Set  Important genes for cancer and chromosomal abnormalities  UniGene  locus link  gene ontology  Why (biological process), what (function), where (cellular component)  Clustering based on genes in one chromosome (functional category)

Oct. 15 (4/4) Extracting Global Structure from Gene Expression Profiles  GeneCut Program (clustering) Fishing Expedition – A Supervised Approach to Extract Patterns from a Compendium of Expression Profiles  Unified maximum separability Analysis

Oct. 16 (1/3) Keynote (D.J. Lockhart, Affymetrix)  Experiments without hybridization  Signal intensities and reproducibility (experimental findings, upper 10% ~ lower 60%) Analysis of Gene Expression and Drug Activity Data by Knowledge-based Association Mining  Association rules finding  DFNA  Taxol in colon cancer cell line Extracting Knowledge from Genomic Experiments by Incorporating the Biomedical Literature  Poster  (issue of this year in the microarray analysis)

Oct. 16 (2/3) Closing Remarks (J. Weinstein, NCI)  The variability between cell lines ~ How many replicates should we use?  Omic and hypothesis - driven research (a necessary synergy)  Genomics, proteomics, transcriptomics, toxicomics, clinomics, economics, just comics, etc.  ~ 35,000 human genes  ~ 100,000 splice variation  > 500,000 protein states (post-protein modifications)   A very wiring diagram of the cell  Methods for gene expression analysis  SAGE  Differential display, restriction display  Flat surface hybridization, etc.

Oct. 16 (3/3) Closing Remarks (Cont’d)  Protein  2D gels and Mass spectrometry ID.  mRNA  cDNA and oligos  DNA  SNPs, etc.  Integrate expression DB with other DBs  Design study (replicates, controls, internal stds, etc.)  Types of Bioinformatics  Applied Bioinformatics –Exploration of public DBs –Data analysis  Developmental Bioinformatics –Statistical procedure development –Algorithms, software development

Conclusions CAMDA of this year  America under attacks (?)  Less participants than the last year The primary issue  Incorporating the knowledge base extracted from the literature databases with the analysis method