TO BE RESCHEDULED - Microarray Workshop (2 day)
When: Oct. 3rd, 2013 - Oct. 4th, 2013 9:30 am - 5:00 pm
To Know
About this Class
Due to the recent Government Furlough this talk had been POSTPONED and wil be rescheduled at a later date.
This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of microarray gene expression analysis. Learn everything from experimental design to statistical analysis and several downstream pathway and pattern discovery methods using both commercial (Partek) and open source software. Those who successfully complete this course will receive a certificate, that will not only look good on their wall, but will also entitle their lab to an additional subsidy from OSTR towards the cost of microarrays processed by the LMT Core.
Day 1 - AM (9:30-12) Introductory Lecture
(Maggie Cam, PhD - CCR, NCI)
Introduction
Historical Perspective
Microarray Technologies, Sample Processing Methods
Microarray comparisons to RNA-Seq
Data Analysis
Experimental Design
QC methods
Preprocessing: Normalization and low level analysis algorithms
Statistical Analysis
Common statistical models used for analysis of microarray data
Examples of blocking
Batch effects and removal methods
Validation and Downstream Analysis
Validation methods
Gene Ontology Enrichment and Pathway analysis tools
Major Software applications
Public Repositories of Microarray Data
Bioinformatics Core Presentation (Manjula Kasoji - CCRIFX)
Lessons learned and how to work with the core
Day 1 - PM (1-5 pm): Hands-on Microarray analysis using Partek Genomics Suite
(Xiaowen Wang, PhD - Partek)
Partek Genomics Suite Analysis Workflow
Process Cel files (RMA)
Looking at data distributions, histograms, bar plots, MA plots, etc.
Statistical Analysis (Anova)
Create contrasts
False Discovery Analysis
Making lists of significant genes
Venn Diagrams
Work independently on dataset
Day 2 AM (9:30-12): Hands-on Partek Genomics Suite Analysis and Partek Pathway
(Xiaowen Wang, PhD - Partek)
Unsupervised Clustering
Custom Filtering
Pathway ANOVA
Work independently on another dataset
Day 2 PM (1-5): GeneSet Enrichment Analysis (GSEA)
(Alan Berger, PhD - School of Medicine Johns Hopkins University)
GSEA is a computational method that determines which (if any) a priori defined sets of genes are significantly differentially expressed, as an ensemble, between two biological states. It is an open-source program developed by the Broad Institute: http://www.broadinstitute.org/gsea/index.jsp
Lecture
The general approach of gene set enrichment methods and comparison with DAVID
How GSEA measures differential expression for each set of genes
Controlling effects of multiple comparisons in GSEA (false discovery rate)
The Broad Institute library of groups of gene sets (MSigDB)
What files and formats are needed for GSEA
User options and running GSEA
Hands-on
Loading the GSEA required input files for an example dataset
Using and choosing values in the GSEA GUI interface
Rank-based analysis
Full dataset analysis
Understanding the GSEA outputs and judging significance in the results
Work independently on another dataset