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Immediately preceding CHI's Data Visualization and Interpretation
September 12-13, 2002, Renaissance Washington DC Hotel, Washington, D.C.
Corporate Sponsor:
Corporate Support:
Sponsoring Publications: Genome Research
PharmacogenomicsWeb Partners: Lab-on-a-Chip.com
Pharmacogenomicsonline.com
Microarrays have exploded in the field of biological research. In fact, over 80 percent of laboratories using microarrays expect to increase their utilization over the next year. However, with the "boom" of the technology also comes the need for careful navigation through interpretation of the data minefield. The facilitation of adoption of standards for microarray experimental annotation, data representation, and the introduction of standard experimental controls and data normalization is critical for the resulting "boom" to be either destructive or productive.
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SCIENTIFIC ADVISORS AND SESSION CHAIRS ADDITIONAL SPEAKERS |
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KEYNOTE PRESENTATION
Statistics and Standards for the Omics Revolution
DATA QUALITY AND EXPERIMENTAL DESIGN
Focusing on Experimental Design
Designing Gene Expression Studies
Minimizing Data Variation
Assessing Data Reliability
NORMALIZATION AND STANDARDIZATION
Using a Standard Data Set
Implementing Microarray Data Standards
Measuring Errors and Data Transformation
Linear Normalization
Probe Profiling
Array Imaging Parameters
STATISTICAL EVALUATION
Big Picture of Statistical Analysis
Analysis of Nonnormal Data
Gene Expression Matrices
Phylogenomic Analysis
Robust Singular Value Decompostition
Analysis of Affymetrix Data
DATA INTERPRETATION AND ANALYSIS
(APPLICATIONS)
Comparison of Expression Patterns in Silico
Post Gene Selection Analysis
Transcript Profiling
Information Management
Time Course Analysis
Model-Based Analysis
CONFERENCE SHORT COURSES
Stats 101 for Biologists
Bio 101 for Statisticians
Interactive Data Visualization and Exploration
MONDAY, SEPTEMBER 9
1:00pm Preconference Short Course Registration
2:00-5:00 Preconference Short Course
Stats 101 for Biologists
Dr. Robert Nadon, Director of Informatics, Imaging Research Inc.; and Brock University
The study of gene expression with printed arrays and prefabricated chips is evolving from a qualitative to a quantitative science. However, problems inherent to the technologies have raised important issues of how to apply adequate statistical tests. This workshop will focus on data processing and primary statistical analysis. Particular attention will be paid to normalization, quality control, determination of differential expression on a gene-by-gene basis, and statistical power analysis for estimating number of replicates needed to detect effects of interest. Examples will be drawn from various technologies (radioisotopic and fluorescent spotted arrays; Affymetrix). Note that data mining and visualization will not be discussed. (Background article: Nadon, R. and Shoemaker, J. [2002]. Statistical issues with microarrays-processing and analysis. Trends in Genetics, 18, 265-271.)Bio101 for Statisticians
Dr. Anoop Grewal, Director of Customer Support, Silicon Genetics
Microarrays and associated instrumentation provide high-throughput methods for indirectly measuring gene expression from biological samples. This half-day tutorial takes a step back to review the underlying biology as a means to help the statistically literate better examine the validity of available statistical methods for analyzing and interpreting data produced by this technology. The tutorial begins with a brief introduction to the basic principles of molecular biology with more detailed coverage of transcription, gene regulation, and biological pathways. Additional topics include common microarray study objectives, experimental designs, biological sample preparation, microarray platforms, and instrumentation.*Separate Registration Required
5:00-7:00 Early Conference Registration and Poster and Exhibit Set-up
TUESDAY, SEPTEMBER 10
7:30am Conference Registration and Poster and Exhibit Viewing with Light Continental Breakfast
8:00-8:40 Advancements in Low Level Affymetrix Data Analysis
Presented by Jason GoncalvesSponsored by:
DATA QUALITY AND EXPERIMENTAL DESIGN
9:00 Chair's Opening Comments
Mr. Thomas J. Downey, President, Partek, Inc.
9:10 Keynote Presentation:
Statistics and Standards for the Omics Revolution
Dr. John N. Weinstein, Senior Research Investigator, Laboratory of Molecular Pharmacology,
National Cancer Institute, National Institutes of Health9:45 Proper Design of Gene Expression Studies
Dr. Eric R. Fairfield, President, Fairfield Enterprises
Microarrays offer the promise of rapid accurate measurement of gene expression under many experimental conditions. The results so far have been disappointing. Very few successful predictions have been made based on microarray data alone. Standard procedures are needed. There seem to be two classes of difficulties in current practice. The first is local and technical. There are technical subtleties in each step that are critical for success and that cross many disciplines. It has been difficult for an experimenter or even a team of experimenters to master each of these subtleties so that the experiments are uniformly well done. The second is global and conceptual. It has been hard to understand what hypotheses are testable by gene expression experiments and how to test these hypotheses. Even questions such as "Is there enough data and is it good enough?" are seldom addressed. In this talk, I will emphasize the conceptual problems because their answers control the local questions that need to be answered. I will start to answer questions including: (1) What hypotheses are testable with microarrays? (2) What controls, positive and negative, are needed to understand the results? (3) How do you tell whether a particular study is controlled well enough to discriminate among the hypotheses? My worldview will be that of an experimentalist. I will attempt to connect the biological, genetic, genomic, physical chemical, computer science, and mathematical aspects of this technology into a coherent whole without getting us all lost in the myriad technical details.10:15 Poster and Exhibit Viewing, Refreshment Break
11:15 Experimental Designs to Minimize Data Variation in Expression Profiling
Dr. Jay P. Tiesman, Senior Scientist, Corporate Biotechnology, Miami Valley Laboratories, Procter & Gamble
Microarrays have become the tool of choice for the global analysis of gene expression. Powerful statistical tools are now available to analyze this expression and to gain an understanding of how changes in gene expression patterns impact biological systems. However, these tools are particularly sensitive to the myriad sources of biological and experimental variation associated with analyzing thousands of genes at one time. In order to effectively use these potent analysis tools, we need to: 1) isolate the primary sources of variation, and 2) design expression profiling experiments in such a way that this variation does not confound analysis of the data. We will show how sources of experimental variation can lead to the misuse of global analytical tools such as clustering and ultimately result in erroneous biological conclusions. We will also present results of a series of carefully controlled experiments that isolate the most significant sources of biological variation in each of the steps leading to the production of GeneChip targets. This information has been instrumental in our rethinking of how expression profiling experiments are designed and we will share these key learnings with the goal of making microarrays even more powerful through high-level statistical analyses.11:45 Assessing the Reliability of Microarray Data
Dr. Phillip Stafford, BioStatistics, Amersham Genomics
Motorola Life Sciences manufactures the CodeLink™ Gene Expression line of microarray products. We have invested considerable effort into the reduction of variability obtained from microarray experiments. To accomplish this goal, we have devised multiple techniques for quality control and to assess the performance of our platform. The topics and techniques I wish to speak about are (1) design and implementation of manufacturing controls, (2) experimental setup to measure different performance metrics of a microarray platform, (3) methods for determining the reliability of a microarray platform, (4) statistical methods to assess performance, (5) applications of these statistical methods, and (6) example analyses of the performance of the CodeLink Expression platform.12:15 Panel Discussion with Questions from Audience for Morning Speakers
12:45 Lunch (on your own)
NORMALIZATION AND STANDARDIZATION
2:00 Chair's Comments
Dr. Hrissi Samartzidou, Senior Project Manager, Bioinformatics, Amersham Pharmacia Biotech2:05 Evaluation of Different Data Analysis Methods with a Standard Data Set
Dr. Yudong D. He, Rosetta Inpharmatics, Inc.
We recently generated a data set comprising ~200 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. The ~24,000 60-mer oligonucleotides that report ~2,500 known genes and associated hybridization experiments were thoughtfully constructed to include built-in redundancy, multiple experimental designs, and variable signal strengths in order to support the performance evaluation of alternative data processing approaches and of alternative experiment and array designs. We introduce two standard figures of merit-one for measuring the success in detecting individual up and down differential regulations or significant hybridization intensities and the other for detecting similarities and differences in expression patterns across genes and experiments. We also describe a baseline-processing stream that includes background correction, normalization within and between arrays, error modeling, and the choice of similarity metrics for pattern recognition purposes. Finally, we evaluate the performance of several advanced data analysis methods based on transcript ratio and hybridization intensity measurements. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve image processing, expression pattern recognition algorithms, experimental design, and statistical methods relevant to microarray data analysis.2:35 Implementing Microarray Data Standards in ArrayExpress
Dr. Ugis Sarkans, Software Architect, European Bioinformatics Institute
Infrastructure that enables sharing of gene expression experiment information has to contain several components with various degrees of formality and different coverage. Minimum Information About Microarray Experiments (MIAME) is a recommendation that describes the level of detail needed to enable interpretability of gene expression experiments. MAGE is an object model that formally describes the domain of microarray experiments, covering MIAME requirements and more, and can serve as a blueprint for various implementations like MAGE-ML, an XML language for data exchange. The Microarray Gene Expression Data group is a grass-roots movement that is the driving force behind MIAME, MAGE, and other efforts, like development of sample and other ontologies, classifying and formalizing different data normalization and processing approaches, and developing a query language for gene expression data repositories. ArrayExpress is a public repository for microarray data that implements the MAGE model and is able to accept data in the MAGE-ML format.3:05 Measurement Errors and Data Transformation for Gene Expression Microarray Data
Dr. David M. Rocke, Department of Applied Science, Division of Biostatistics, School of Medicine, and Center for Image Processing and Integrated Computing, University of California, Davis
Gene expression microarrays comprise a suite of related technologies for measuring the expression of thousands of genes simultaneously from a single biological sample. There are also numerous other high-throughput biological assays that can measure large numbers of proteins, lipids, and other biologically active compounds. In this talk, I will describe an important statistical challenge in the use of such data. Using raw data, logarithms, or ratios, the variability of the measurements is strongly dependent on the level of expression, causing a failure of the assumptions of most standard methods of statistical analysis. We present a solution to this problem via a specially tuned data transformation.3:35 Poster and Exhibit Viewing, Refreshment Break
4:15 A Microarray Linear Normalization Technique
Mr. Leon J. Bodzin, Associate Director, Software Engineering, Genicon Sciences Corporation
A linear normalization technique is applied between microarrays to ensure accurate and repeatable comparisons. The algorithm is a closed-form mathematical solution and offers an easily applied solution to the linear normalization problem. When applied across microarrays, it results in corrected density values that statistically meet the model assumptions underlying the experimental design. The assumptions are that equally prepared microarray features should express equally and that the processes used to prepare the microarray apply uniformly (linearly). Therefore, linear normalization aims to enforce known relationships between microarray features, on an intra- or interarray basis, by correcting for biochemical, mechanical, and optical processes involved. Specifically, the normalization process involves the pairing of known microarray features across a control and data set and uses the differentials between them to encode the exhibited fluctuations into a linear model. The linear model is, in turn, used to transform the data set to align it coincidentally with the coordinate system of the control data-a requirement for achieving accuracy and repeatability in a comparative analysis study. Further application of the technique using reciprocal relations enables an indicator for sighting individual features exhibiting anomalous behavior.4:45 Probe Profiling to Improve Quality and Sensitivity of the High-Density Oligo Arrays
Dr. Jim Veitch, President, Corimbia, Inc.
Gene expression profiling experiments are often multivariate studies designed to look for changes among different mRNA populations from samples with differing biological attributes. Analysis of data obtained from such studies confounds the effects of biological attributes with sources of variation due to sample accrual, processing, and instrumentation; for example, in a tumor necrosis factor study mRNA degradation from cell death is confounded with effects of TNF. Further, these sources of variation, even if standardized within one site or lab, can be systematically different between labs or sites; in another study using different labs, lab-to-lab differences dominate observed effects. In this talk we show how to detect, quantify, and sometimes correct for such issues on the Affymetrix chip platform by profiling the response of the oligo probes on the chip. As a side benefit we get improvements in signal/noise ratio of three times and between 20% and 25% better detection of up- and downregulation.5:15 Ultimate Hardware Tool for Microarray Experiment Standardization
Dr. Thomas Kaiser, CLONDIAG® Chip Technologies GmbH
DNA microarrays have become a major research tool in functional genomics over the last few years. Nevertheless, array experiments are still lacking comparability. This circumstance prohibits the use of array-based assays in high-throughput applications. One main reason for this is the variance of imaging parameters applied in array readouts. As multiple systems with diverse imaging software tools are available, the need for standardization increases. Here we describe a fluorescent hardware tool and software utilities, which allow the comparison of array imaging parameters independent of the technological readout system. Consisting of a chip with spot features of defined shape, size, and fluorescence intensity, the tool opens up the possibility to normalize array-borne data sets generated at various sites.5:45 Panel Discussion with Questions from Audience for Afternoon Speakers
6:15 Pizza and "Micro" brews (sponsored by Cambridge Healthtech Institute)
7:15 Close of Day One
WEDNESDAY, SEPTEMBER 11
7:30am Poster and Exhibit Viewing with Light Continental Breakfast
STATISTICAL EVALUATION
8:00 Chair's Comments
Dr. Robert Nadon8:05 The Big Picture of Statistical Analysis of Microarray Data: Seeing the Forest, Not Just the Trees
Mr. Thomas J. Downey, President, Partek, Inc.
As sound statistical methodology gradually makes its way into the field of microarray data analysis, there is a tendency for the scientist new to statistics to apply a limited set of tools to a broad range of problems. Cluster analysis, for example, was one of the first statistical techniques applied to microarray data and was used to attempt to answer questions that it was not designed to answer. This talk will give a brief overview of the broader range of statistical techniques required to make valid discovery in microarray data analysis. These techniques include experimental design, exploratory data analysis, statistical inference, and predictive modeling. I will describe the fundamental concepts of each of these families of techniques and describe where they fit into the process of making discovery from microarray data.8:35 Microarrays: Experimental Design and Statistics Matter!
Dr. Grier P. Page, Assistant Professor, Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham
Microarray arrays are a scientific tool that should be viewed in a similar fashion to any other laboratory technique with careful experimental planning, replication, and proper statistical analysis. We have developed a suite of statistical techniques for extracting the maximum amount of valid information from microarray data. These methods take into account highly nonnormal data, small sample sizes, generalized linear models, and mixed models and estimate false positive, true positive, and false negative rates. The statistical analysis is coupled with a consulting laboratory to aid in experimental design and further analysis and interpretation and to contribute to grant development and publication writing.9:05 The Importance of Being Improbable: Generative Models in the Statistical Analysis of Gene Expression Matrices
Dr. Zoltan Szallasi, Children's Hospital Informatics Program, Harvard Medical School
Statistical analysis of large-scale gene expression studies imposes several novel problems. Most importantly, gene expression arrays have a well-defined internal data structure dictated by the genetic network of the living cell. Standard analytical tools often ignore this structure leading to errors of several orders of magnitude in statistical analysis. In this talk, we are outlining a set of simulation-based tools that generate random gene expression matrices that retain the internal data structure dictated by, for example, the overall pattern of gene co-regulation. This will in turn determine the probability that a given feature, such as a cluster or separator, will appear by chance in a gene expression array. We show several examples of how these methods aid the correct statistical analysis of cancer-associated gene expression matrices. We also outline the theoretical foundations for determining the appropriate distance metric for the cluster analysis of gene expression matrices.9:35 Poster and Exhibit Viewing, Refreshment Break
10:15 Phylogenomic Analysis of Gene Expression Data Using P-Trees: Towards the Construction of Biological Systems
Mr. Willy A. Valdivia Granda, Plant Stress Genomics and Bioinformatics Group, Department of Plant Pathology, North Dakota State University
The greatest challenge in maximizing the use of gene expression data is to develop new computational tools capable of interconnecting and interpreting results from different organisms and experimental settings. A patented data-mining technology called Peano count tree (P-tree) is used to represent genomic data in a multidimensional plane. Each spot of a microarray is presented as a pixel with its corresponding red and green bands. Each band is stored separately in a reorganized 8-bit bSQ file. Each bit is then converted in a quadrant base tree structure P-tree from which the "super-chip" is constructed. The phylogenomic classification model is introduced with the analysis of more than 1,100 microarray experiments in five different organisms. Our results suggest the need for the reorganization of gene expression data to achieve biological systems inference.10:45 Robust Singular Value Decomposition Analysis of Microarray Data
Dr. Li Liu, Statistical Research Center, Development Operations, Pfizer Global Research & Development
In microarray data there are a number of biological samples, each assessed for the level of gene expression for a typically large number of genes. There is a need to examine these data with statistical techniques to help discern possible patterns in the data. Our method applies a combination of mathematical and statistical methods to progressively take the data set apart so that different aspects can be examined for both general patterns and for very specific effects. The benefits of this analysis will be both the understanding of large-scale shifts in gene effects and the isolation of particular sample-by-gene effects that might either be unusual interactions or the result of experimental flaws. The proposed clustering method with robust SVD and segmentation is resistant to missing values, outliers, and the nonnormal distribution of the data matrix, and its usefulness is illustrated by a data set from the literature.11:15 Processing and Statistical Analysis of Affymetrix Data
Dr. Robert Nadon
The Affymetrix platform provides various sources of information (perfect-match and mismatch oligonucleotides; Signal) that offer rich opportunities for data analysis. I will discuss statistical analysis of differential expression using both raw and processed Affymetrix data.11:45 Panel Discussion with Questions from Audience for Morning Speakers
12:15 Luncheon (sponsored by Cambridge Healthtech Institute)
DATA INTERPRETATION AND ANALYSIS (APPLICATIONS)
1:15 Chair's Comments
Dr. Sorin Draghici, Assistant Professor, Department of Computer Science, Wayne State University1:20 Comparison of Expression Patterns in Silico: A Blast-Inspired Approach for Microarray Data Analysis
Dr. Korkut Vata, Department of Pathology, Duke University
Blast is a basic tool for recognition of sequence similarities. The idea of blast can also be applied to recognize similar expression patterns in microarray data. By using a blast-inspired approach we tried to delineate biologically meaningful expression patterns in a yeast model. Comparison of our experimental data with the Rosetta Compendium Data Set revealed redox-dependent and independent signatures of Rac1 triphosphatase-mediated gene expression.1:50 Microarray Data Analysis: How to Extract Biological Meaning from Microarray Results
Dr. Sorin Draghici
Most of the current efforts in microarray data analysis are focused on providing tools and methods for sifting through the large amount of raw data generated by microarrays in order to obtain a set of genes that are differentially regulated in the condition(s) under study. Usually, once such differentially regulated genes are found, the data analysis process is considered done and the biologist reverts once again to a tedious process of individually analyzing each differentially expressed gene in an attempt to understand the biological phenomenon involved. This talk will present Onto-Express, a tool for the post gene selection analysis. Using as input the list of differentially regulated genes resulting from the classical microarray data analysis and does data mining in the biological information publicly available about such genes. Subsequently, Onto-Express analyzes the resulting lists looking for common elements indicating an impact of the condition studied on specific functional categories. Further navigation allows the user to obtain detailed information about a specific gene and/or function. Statistical confidence parameters are calculated for each functional category. The talk will present the tool as well as examples showing its utility. Onto-Express is freely available at http://vortex.cs.wayne.edu:8080.2:20 Using Transcript Profiling to Compare KDR Inhibitor Chemotypes in a Disease Model
Dr. Yihong Yao, Senior Scientist, Abbott Bioresearch Center
VEGF has been shown to be the primary mediator of angiogenesis. VEGF has also been identified as a potent inducer of vascular permeability, and recent evidence has implicated this molecule in the development of pathological edema. Inhibitors of KDR would have utility in treating diseases associated with neovascularization or edema such as cancer or anaphylaxis. Although several groups have reported discovery of potent inhibitors of KDR, the mechanism by which these molecules inhibit angiogenesis and vascular permeability remains to be proven. We have used Affymetrix GeneChips to analyze changes in transcript profiles of Balb/c mice treated with specific KDR inhibitors (three small molecules and anti-KDR antibody) to elucidate the role of this receptor in the formation of estrogen-induced uterine edema. Quantitative PCR has been used to validate the Affymetrix GeneChip results. Many of these genes are co-cited with VEGF and/or angiogenesis and are known to be involved in VEGF signal transduction. This study shows that inhibitor chemotyping is an effective way to identify on-target genes for diagnosis and to elucidate the underlying mechanism of drug effects and compound specificity.2:50 Information Management for Microarrays: from Cross Platform Data Acquisition to Biological Analysis
Dr. Hrissi Samartzidou, Senior Product Manager, Bioinformatics, Amersham Biosciences
The past decade has seen dramatic progress in the development of high throughput life science technologies such as microarrays. Soon after its introduction, microarrays has become the technology of choice for gene expression analysis. The microarray applications are enabling researches to conduct experiments that generate rapidly tremendous amounts of data. This creates an urgent need for tools that can manage both the biological and experimental information from the microarray workflow and the data acquisition process more effectively and efficiently. In addition to data management, data integration across various applications, including sequencing, gene expression, genotyping and proteomics is necessary for a systems biology approach. A Microarray Laboratory Workflow System (MA-LWS), as a part of a larger integrated laboratory information management system, has been created to satisfy the above needs. The system manages the entire microarray workflow from the design of the experiment and the submission of the biological sample to the capture and normalization of the numeric expression data. It also allows for integration of multiple technologies and helps delivering the knowledge needed to answer complex questions in system biology and drug discovery efforts. Examples of its functionality, usability and applications will be presented.3:20 Poster and Exhibit Viewing, Refreshment Break
3:45 Time Course Analysis of Gene Expression in Developing Mouse Neutrophils
Dr. David B. Finkelstein, Data Analysis Scientist, Affymetrix, Inc.
Time course studies in gene expression with probe arrays pose a variety of data analysis challenges. Time courses are often exploratory rather than model-driven, which means that any reproducible expression profile may be of biological interest. Consequently, statistical methods that provide measures of confidence for a fixed class of models, such as linear regression, may fail to uncover biologically significant profiles. Also, when applying standard methods such as ANOVA, corrections must be applied for unstable variance across time, as well as to account for multiple comparisons given the large number of genes. We will discuss some of these challenges and demonstrate solutions, in a time course analysis of gene expression in developing mouse blood cells. The analysis utilizes readily available tools and methods and has resulted in the identification of statistically valid gene expression profiles that demonstrate a wide range of intriguing biological behaviors.4:15 Model-Based Analysis of Oligoneucleotide Arrays: Expression Index Computation and Outlier Detection
Ms. Xuemin Fang, Harvard University
There have been various efforts to calculate the gene expression index for the oligonucleotide microarrays. An ideal index should be unbiased, low-variance estimate of the underlying mRNA concentrations of the target gene. Li and Wong (2001) proposed a model-based expression index, which gives good estimates of gene expression level when a gene's real concentration level is high, but when a gene's real concentration is relatively low, the accuracy of the gene expression index may be affected by the cross-hybridization and random noise. The current improvement focuses on a three-step modeling procedure, each step could give highly accurate estimates for a selective set of parameters. We tested our procedure on the Affymetrix's latin square design spike-in experiment, and the comparison with existing estiamtion procedures show that the new methods provide substantial improvement, especially when the real concentration is close to zero.4:45 Panel Discussion with Questions from Audience for Afternoon Speakers
5:15 Close of Conference
5:15-5:45 Postconference Short Course Registration
5:45-8:45 Interactive Data Visualization and Exploration (see Data Visualization for details)
CORPORATE SPONSOR BIOGRAPHIES
Amersham Biosciences, the life sciences business of Amersham plc (LSE, NYSE, OSE: AHM), is a world leader in developing and providing integrated systems and solutions for disease research, drug development and manufacture. Our vision is to enable molecular medicine.
Gene Logic Inc. is a leading genomics-based biocontent and bioinformatics company focused on developing information products related to gene activity in human disease and toxicity to optimize rapid, reliable and cost-effective pharmaceutical and biotechnology research and development. Through its expertise in biosamples, high-throughput genomics technologies and software development, the Company has developed a series of gene expression information solutions based on its core product, the GeneExpress® Suite.
Iobion Informatics presents GeneTraffic software for two-color microarray data management and analysis. GeneTraffic software allows you to access data and projects on a desktop PC, from any location within your Network, manage data, perform computational analyses and query your data. With GeneTraffic software you can qualify and validate your microarray data prior to biological analysis.
Iobion Informatics is a Delaware LLC, headquartered in La Jolla, CA with offices in Toronto, Canada, and Austin, Texas.
HOTEL INFORMATION
Renaissance Washington DC Hotel
999 Ninth Street, N.W.
Washington, DC 20001
T: 202-898-9000
F: 202-289-0947
Room Rate: $199 S/D
Cut-off Date: August 15, 2002Please call the hotel directly to make your room reservation. Identify yourself as a Cambridge Healthtech Institute conference attendee to receive the reduced room rate. Reservations made after the cut-off date or after the group room block has been filled (whichever comes first) will be accepted on a space-and-rate-availability basis. Rooms are limited, so please book early.
TRAVEL INFORMATION
Special Zone and Discount Fares have been established for this conference with United Airlines. Please call United Airlines Meeting Reservation Desk at 800-521-4041 and reference ID #579YS.CALL FOR SPONSORS AND EXHIBITORS
This conference will present the latest techniques for data analysis via visualization and serve as excellent follow-up to the preceding Microarray Data Analysis meeting. We strongly encourage any company with services or products related to microarrays, microarray readers, data visualization, image analysis, informatics, data mining, data storage & retrieval, to consider sponsoring or exhibiting at this event.
For more information on sponsorship opportunities or to reserve a booth, please contact Angela Parsons at 781-972-5467 or aparsons@healthtech.com.The following companies are exhibiting as of July 17, 2003
Microarray Data Analysis Booth # Amersham 125 Affymetrix 123 BioDiscovery 107 Expression Analysis, Inc. 121 Gene Logic 143 GeneData, Inc. 119 Imaging Research Inc. 139 Inpharmatica 109 Insightful Corp. 115 Iobion Informatics LLC 117 NDRI 129 MolMine AS 137 Salford Systems 105 Silicon Genetics 113 Spotfire, Inc. 141 Strand Genomics 135 Sunergia Group 111 Xpogen Inc. 127 CALL FOR POSTERS
Cambridge Healthtech Institute encourages attendees to gain further exposure by presenting their work in the poster sessions. Please fill out the registration form, with the poster title and primary author. To ensure inclusion in the conference binder, a one-page summary must be submitted and registration must be paid in full by August 9, 2002. Click here for poster instructions
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