WEBINARS

GeneSifter 101: Getting Started with GeneSifter

This brief webinar, designed as an quick overview for researchers considering or just beginning to use their GeneSifter trial account, looks at loading data and beginning an analysis with GeneSifter. 27 minutes. Note: it is not necessary to view this webinar before any of the others below.

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GeneSifter 201: Advanced Features

Part 2 of the 2 webinar complete course presented live each week for researchers with GeneSifter trial accounts. This section covers more advanced project creation and analysis features. Topics include ANOVA, two-way ANOVA, clustering, search functions, and data visualization. 37 minutes. Prerequisite: GeneSifter 101.

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Comparison of gene expression profiles of primary and metastatic melanoma: from raw data to biological interpretation

Was presented on August 19, 2008 by Peter Roberts, Ph.D.

The presentation is an alternate analysis of the data described in a recent paper by Riker et al. BMC Medical Genomics 2008, 1:13 and deposited in the Gene Expression Omnibus under accession GSE7553. The data was derived from a series of metastatic and non-metastatic cutaneous tumor samples. RNA samples were hybridized to Affymetrix U133 plus 2 human whole genome GeneChips. The metastatic and non-metastatic tumors will be compared using t tests and ANOVA with false discovery rate corrections. Genes of interest will be identified and the function of the genes assessed using gene ontologies and pathway analysis.

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GeneSifter 101: Introduction to GeneSifter

Part 1 of the 2 webinar complete course presented live each week for researchers with GeneSifter trial accounts. Everything you need to get started and more, covering uploads, pairwise analysis, t-tests, corrections, scatter plots, Z-scores, ontologies, and pathways. 36 minutes.

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GeneSifter 201: Advanced features

Creating projects, multiple group analysis, ANOVA, two way ANOVA, post-hoc analysis, hierarchical clustering, partition clustering, search functions and more.

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Effect of exogenous oxylipins on Arabidopsis thaliana gene expression

Presented on July 2, 2008 by Peter Roberts, Ph.D.

The presentation is an alternate analysis of Arabidopsis thaliana gene expression microarray data recently deposited in the Gene Expression Omnibus (GEO accession: GSE10749) and described by Mueller et al. (Plant Cell, 20(3): 768-785, 2008). Affymetrix ATH1 GeneChips were used to generate gene expression data. The study compares mixotropic cell cultures and 10 day old plants treated with A1 phytoprostanes (PPA1) or 12-oxo-phytodienoic acid (OPDA). The role of TGA transcription factors in oxylipid responses was studied, the gene expression of triple mutants defective in three TGA transcription factors (tga2-5-6) and wildtype plants being compared after treatment with PPA1 or OPDA. In this reanalysis, statistically significant differentially expressed genes will be identified using t tests and two way ANOVA. The significant biological functions associated with those genes will be characterized from gene ontology and pathway analysis.

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Gene expression microarray analysis to identify differences in prostate tumors correlating with ethnic background

Presented on January 22, 2008 by Peter Roberts, Ph.D.

It has been recognized that there is marked variation in prostate cancer incidence and mortality between different ethnic groups. This presentation is an analysis of data from the Gene Expression Omnibus (GEO), accession GSE6956. The data is from paired and unpaired normal and tumor tissues derived from prostates of either African American or Caucasian men. The samples were analyzed using Affymetrix U133 plus 2 human whole genome GeneChips. Differences in gene expression profiles between the two groups will be identified using t tests, ANOVA and false discovery rate (FDR) corrections. Additional analysis will identify pathways and biological functions that may be involved in the clinical differences between the two groups.

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Microarray data analysis: How to get from raw data to significant biological information

Presented on December 11, 2007 by Peter Roberts, Ph.D.

Utilizing data generated using Illumina human Beadarrays (ArrayExpress: E-TABM-116) and Affymetrix rat whole genome GeneChips (GEO: GSE5350) as examples, the process of microarray data analysis from beginning to end will be described. The emphasis will be on workflows for analyzing paired microarray data, using the Illumina statin study as an example, and multiple sample groups with ANOVA and two-way ANOVA, using Affymetrix data from the MAQC rat toxicology study as the example. This statistical analysis identifies significantly differentially expressed genes. The significant biological functions associated with those genes will be identified from gene ontology and pathway analysis.

Due to technical difficulties, the recording of this webinar will not be available.

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Using Microarrays to Identify Penile Specific Candidate Genes and Mechanisms of Erectile Dysfunction

Presented October 18, 2007 by Hunter Wessells, M.D., Professor of Urology, University of Washington; Chief of Urology, Harborview Medical Center

Erectile dysfunction (ED) affects one in 5 men over the age of 20 in the US and annual expenditures for the condition exceed $400 million, excluding prescription drug costs. We have used microarrays to expand the scope of candidate genes and pathways that may be relevant to the pathophysiology of ED. First, we examined penile gene expression changes in a rodent model of diabetes associated ED. 529 genes/EST were differentially expressed in diabetic cavernosum, including genes previously shown to mediate vascular dysfunction [e.g.,ceruloplasmin (Cp), lipoprotein lipase, and Cd36] as well as genes involved in the modulation of the smooth muscle phenotype (e.g., Kruppel-like factor 5 and chemokine C-X3-C motif ligand 1). To define molecular phenotype of endothelial cells in the unique hemodynamic environment of the corpus cavernosum, we compared gene expression patterns of cultured endothelial cells (EC) derived from human corpus cavernosum, coronary artery and umbilical vein. Statistical filtering revealed190 genes/transcripts highly expressed in cavernosal EC versus both coronary artery and umbilical vein, including collagens 1 and 6, versican, syndecan, desmoplakin, FGF 13, gremlin 1, and CLDN 11, a tight junction protein not previously described in endothelial cells. In addition to generating novel mechanisitic directions relating to ED, these data have allowed us to develop an a priori a list of candidate genes which we will use to investigate genetic predispostion to erectile dysfunction.

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Identification of biological themes in microarray data: two-group comparisons

(presented April 12, 2007)

N. Eric Olson, PhD, based this presentation on a paper he co-authored which was published by D Huster et al in the March 2007 Journal of Biological Chemistry. This webinar examined the identification of differentially expressed genes and the determination of biological significance of gene lists using Gene Ontology terms, KEGG pathway terms and other resources for two-group comparisons. Emphasis was on determining overall data quality using Affymetrix quality metrics and cluster analysis, comparison statistics and correction for multiple testing. Datasets examined included prostate cancer and liver disease. 69 minutes including questions.

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Comparison of Fanconi anemia patients with aplastic anemia or clonal evolution using gene expression microarrays

(presented August 15, 2007)

Grover Bagby, M.D., the Founder and longtime Director at OHSU Cancer Institute, presented new data and analysis from his laboratory for Fanconi Anemia (FA), an inherited disorder associated with hypoplastic bone marrow failure, developmental anomalies, and a high relative risk of acute myelogenous leukemia and epithelial malignancies in children and young adults. He examines normal and FA marrows with clonal evolution, resistant to interferon or tumor necrosis factor induced apoptosis, showing how gene expression profiling readily enables the differentiation of clinical sample sets. 53 minutes including questions.

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Gene expression microarray analysis of autism spectrum disorder: from raw data to biological interpretation

(presented July 12, 2007)

Gene expression in blood of children with autism spectrum disorder (ASD) was studied using Affymetrix GeneChip human genome U133 plus 2.0 microarrays (GEO accession GSE6575, JP Gregg et al, University of California at Davis, 20 Dec 2006). Transcriptional profiles were compared with age and gender matched, typically developing children from the general population (GP) or IQ-matched children with mental retardation or developmental delay (MR/DD).

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The microarray data analysis process: from raw data to biological significance - 2007

Based on a paper of the same name published in late 2006 in NeuroRX, and updated by the author with timely information and a new cardiovascular dataset example, this webinar is a more recent and briefer version of the 2-part series described below. Like that webinar, it covers the process of microarray data analysis from beginning to end using examples from various fields. Presented live to hundreds of participants in February 2007, it includes a far-ranging question and answer session. Intended for research biologists; no prior knowledge of microarray data analysis is neccessary to view this webinar.

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Medical genomics: understanding the disease genome through sequence variation and expression analysis

(presented June 7, 2007 by N. Eric Olson, Ph.D.)

Medical genomics is a new field that combines knowledge generated from the Human Genome Project, and analytic methods from bioinformatics, with the practice of medicine. This webinar introduces the field of medical genomics with a focus on two important technology applications: targeted resequencing for mutation and polymorphism discovery, and gene expression analysis using microarrays. Emphasis is on how these applications can help accelerate the process of understanding the molecular basis of disease. The presentation covers an introduction to the methodologies used, and walk-through examples from real data sets in the fields of Cancer and Cardiovascular Biology. 71 minutes with questions.

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The microarray data analysis process - from raw data to biological significance

An extended version of a presentation at the World Microarray Congress in Vancouver, this 2-part online seminar examines the process of microarray data analysis from beginning to end, showing applications using example datasets from various fields including neuroscience, immunology and cardiovascular biology. Part 1 covers all the topics covered in the Vancouver talk in more depth, with the addition of more detail on statistical analysis and information on accessing the Gene Expression Omnibus repository (GEO). Part 2 features discussions of corrections for multiple testing, comparison statistics such as 2-way ANOVA, and meta-analysis using datasets from GEO.

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Using 2-way ANOVA to dissect gene expression following myocardial infarction in mice

This online presentation presented an alternative analysis of microarray data generated by the CardioGenomics Program for Genomic Applications (PGA). The data is from a time series study of gene expression in mouse heart following left coronary artery ligation using 2-way ANOVA. This webinar provided an overview of the microarray data analysis process. Emphasis was placed upon 2-way ANOVA, correction for multiple testing, and determining the biological significance of the results.

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Using two-way ANOVA to dissect the immune response to hookworm infection in mouse lung

This webinar presents an alternative analysis of microarray data from a time series study of gene expression in mouse lung following infection with the nematode Nippostrongylus brasiliensis in wild type and SCID mice using two-way ANOVA. This data was recently made publicly available through the Gene Expression Omnibus (GSE3414) by Reece et al. from the Johns Hopkins University Bloomberg School of Public Health.

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Gene expression microarray analysis of laser microdissected invasive breast carcinomas

Peter C. Roberts, PhD, presents an alternate analysis of data from a breast cancer study published in BMC Cancer, 7:55(2007), comparing gene expression in normal ductal and lobular epithelium tissue to ductal and lobular carcinomas. (1 hour including Q&A)

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Molecular changes in androgen-independent prostate cancer examined using data from microarray studies

This alternative analysis of data generated by Best et al. (Clin Cancer Res. 2005 Oct 1;11:6823-34.) and available through the Gene Expression Omnibus (GSE2443) identified 486 genes that are differentially expressed in androgen-independent prostate cancer primary tumors compared to androgen-dependent tumors using the Affymetrix Human Genome U133A Array and the GeneSifter microarray data analysis system. Genes involved in protein synthesis, cell adhesion and RNA processing were found to be significantly enriched among the 486 genes. The expression of these 486 genes was also examined in metastatic prostate cancer (GSE3325). and breast cancer (GSE3744) data sets.

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Original Article
Data Center

Comparison of differential gene expression in human presymptomatic and clinical malaria

This webinar presents an alternative analysis based on an examination of microarray data originally published in the journal Infection and Immunity (Ockenhouse, C.F., et al., Infect. Immun. (October 2006) 74(10): 5561-5573). Pairwise comparisons, clustering and gene ontologies are among the methods employed and explained.

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Original Article

Microarray analysis of gene expression in male germ cell tumors

This set of alternative analyses of data generated by Korkola et al. (Oncogene, 2005, 24, 5101–5107) and available through the Gene Expression Omnibus (GSE3218) examines the pathways and molecular functions associated with differentially expressed genes in embryonal carcinoma, seminoma, teratoma and yolk sac tumors compared to normal testis using the Affymetrix Human Genome U133A Array and the GeneSifter microarray data analysis system. Part 1, presented in 2006, relied on MAS5 normalization and the annotation and pathway databases extant at that time; in Part 2, the data is revisited in 2007 using GC-RMA normalization and the updated ontologies, pathways and other annotation. Both focus on differential gene expression patterns common to all four tumor types, as well as patterns unique to each; part 2 adds an additional data set, for choriocarcinoma, into the analysis. Comparing the distinct observations and insights in each of the two presentations demonstrates the flexibility and dynamic nature of microarray analysis.

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Original Article

Microarray Analysis of Gene Expression in Huntington's Disease Peripheral Blood - a Platform Comparison

This online webinar examining gene expression data from both the Affymetrix GeneChip® and the GE CodeLink® microarray platforms describes an alternative analysis of datasets available from the Gene Expression Omnibus, accession numbers GSE1751 and GSE1767. Both datasets were originally reported in an article in the Proceedings of the National Academy of Sciences.

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Original Article

Extended Analysis of the MAQC rat toxicogenomic data

The publication of papers from the MicroArray Quality Control project (Nature Biotechnology, September 2006) focused on the reproducibility of data between test sites and across platforms. One test was to compare results from a biologically relevant toxicogenomics data set. This webinar focuses on the data set obtained using the GE Healthcare CodeLink Rat Whole Genome Bioarray, and looks more deeply at the biological interpretation of that data using GeneSifter.

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Microarray Analysis of Gene Expression in Muscle after Statin Treatment

This alternative analysis of Illumina BeadChip data published in December 2006 by Laaksonen R, et al. on ArrayExpress, examined the pathways and molecular functions associated with genes in human skeletal muscle which were differentially expressed during treatment with simvastatin, atorvastatin or placebo. (Clinical use of these pharmaceuticals for lowering lipids is associated with increased risk of myopathy by a mechanism yet unknown.)

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The influence of experimental design on statistical analysis of GeneChip data

Biomedical researchers using microarrays frequently find data analysis a bottleneck due to uncertainties concerning the choice and application of statistical methods. This educational seminar focuses on how workflows for statistical analysis are influenced by experimental designs, with particular emphasis on two-group and time series protocols. Examples will be drawn from the GeneSifter Data Center, an online repository, publishing portal, and training resource that provides publicly available GeneChip data from studies in cancer, neuroscience, vascular biology, etc.

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More Information

Analysis of single channel arrays: Affymetrix® GeneChip® and GE CodeLink.

This presentation covers the basics of analyzing single channel data, particularly GE CodeLink and Affymetrix® GeneChip® data. Topics covered include uploading data, pairwise analysis, identification of differentially expressed genes. Data mining for relevant pathways and gene ontology terms is also discussed.

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Affymetrix Data Analysis Series: Genomic analysis of the myeloid differentiation program

In February 2004 Affymetrix® hosted the GeneChip® Expression Data Analysis Webcast series. A model cell line (MPRO) was induced by retinoic acid to study the progression of granulocyte differentiation.

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Advanced analysis of microarray data

This presentation will teach you how to perform multiple group comparisons in GeneSifter using Project Analysis. After watching the webinar, you will be able to create a project, basic statistics, clustering, and mining for biological information including pathway analysis.

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GeneSifter 101: Introduction to GeneSifter

Part 1 of the 2 webinar complete course presented live each week for researchers with GeneSifter trial accounts. Everything you need to get started and more, covering uploads, pairwise analysis, t-tests, corrections, scatter plots, Z-scores, ontologies, and pathways. 36 minutes.

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