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