Course Descriptions
Please see our workshops page for dates and locations.
Please note: all workshops are now taught using GeneSpring 7.1.
Course Descriptions
Statistics and Algorithms:
Learn statistics from Silicon Genetics statistician, Peter Lambert. This one-day workshop is designed to provide fundamental concepts of statistics and their applications to microarray analysis. Discover how to identify differentially expressed genes using tools such as 1-way and 2-way ANOVA, multiple testing corrections, post-hoc test, Principal Component Analysis and the GeneSpring error model.
Relevant literature references will be included and ample time will be allocated for questions and discussions. GeneSpring will be used to illustrate examples, but a prior knowledge of statistics is not required.
Microarray Data Analysis - Level I
Level I focuses on the basics of microarray data analysis and workflow. Topics include: data import, normalization, quality control methods, filtering, gene list manipulation, complex experiment specification, fold change analysis, finding differentially expressed genes, and data export. You will learn why normalization is important for microarray data and when to apply certain normalizations, such as global scaling and intensity-dependent Lowess, to your own one- or two-color data sets. You will also learn about integrating gene expression data with known biological annotations available in public and proprietary databases. Finally, you will learn how to export interesting microarray results that can be used for presentation.
Microarray Data Analysis - Level II
Level II helps you understand when, why and how to use statistical analysis, the global error model, hierarchical clustering, pathways and scripts. We will cover the theory and application of statistical analysis and clustering tools to help you tackle the massive amount of data generated by microarray technology. You will also learn how to find genes with similar expression profiles and map gene expression intensity on interesting pathways. Finally, you will acquire scripting skills to help you streamline your analysis.
Microarray Data Analysis - Level III
Level III is for users who wish to master advanced data analysis by making more informed algorithm choices. This course covers the theory and application of self-organizing maps, k-means and QT clustering, as well as associated similarity metrics. You will also learn about the class prediction tool and how to use this supervised learning method to predict the identity of unknown samples. Other advanced methods, such as finding potential regulatory sequences based on shared expression patterns, and cross-referencing results between data from different organisms, will also be covered.
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