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Microarray Data Analysis Using BASE. Danny Park MGH Microarray Core March 15, 2004. You’ve got data!. What was I asking? – remember your experimental design How do I analyze the data? How do I find interesting stuff? – learn some analysis tools
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Microarray Data Analysis Using BASE Danny Park MGH Microarray Core March 15, 2004
You’ve got data! • What was I asking? – remember your experimental design • How do I analyze the data? • How do I find interesting stuff? – learn some analysis tools • How do I trust the results? – statistics is key
What was I asking? • Typically: “which genes changed expression levels when I did ____” • Common ____: • Binary conditions: knock out, treatment, etc • Continuous scales: time courses, levels of treatment, etc • Unordered discrete scales: multiple types of treatment or mutations • This tutorial’s focus: binary experiments
How do I analyze the data? • BASE – BioArray Software Environment • Data storage and distribution • Simple filtering, normalization, averaging, and statistics • Export/Download results to other tools • MS Excel • TIGR Multi Experiment Viewer (TMEV) • This tutorial’s focus: using BASE
Today’s Presentation • Demonstrate the most basic analysis techniques • Using our most frequently used software (BASE) • For the most common kind of experiments
QC & label RNA Labeled cDNA hybridize Slides Researcher scan, segment BASE Images & data files upload Work Flow analysis
The Most Common experiment • Two-sample comparison w/N replicates • KO vs. WT • Treated vs. untreated • Diseased vs. normal • Etc • Question of interest: which genes are (most) differentially expressed?
A B Experimental Design – naïve From Gary Churchill, Jackson Labs
A B Experimental Design – tech repl From Gary Churchill, Jackson Labs
A A B B Experimental Design – bio repl • Treatment • Biological Replicate • Technical Replicate • Dye • Array From Gary Churchill, Jackson Labs
The Most Common Analysis • Filter out bad spots • Adjust low intensities • Normalize – correct for non-linearities and dye inconsistencies • Filter out dim spots • Calculate average fold ratios and p-values per gene • Rank, sort, filter, squint, sift data • Export to other software
BASE @ MGH • BASE is a microarray data storage and analysis package • BASE resides on our web server • Data is stored at our facility • Computation is performed on our machines • All you need is a web browser • https://base.mgh.harvard.edu/ • A Microarray Core technician will provide you with a username, password, and experiment name
Reporters BASE – Sidebar
Reporters BASE – Sidebar
Array LIMS BASE – Sidebar
Array LIMS BASE – Sidebar
Biomaterials BASE – Sidebar
Biomaterials BASE – Sidebar
Hybridizations BASE – Sidebar
Hybridizations BASE – Sidebar
Analyze Data BASE – Sidebar
Analyze Data BASE – Sidebar
Users BASE – Sidebar
Users BASE – Sidebar
BASE – My Account Change your password and access defaults
BASE – My Account Change your password and access defaults
BASE – My Account Change your password and access defaults
BASE – My Account Change your password and access defaults
Group slide data together Select the slides that measure the same thing. Later in analysis, they will be averaged together. In this experiment, all ten slides are replicates, so there is only one grouping.
Group slide data together Select the slides that measure the same thing. Later in analysis, they will be averaged together. In this experiment, all ten slides are replicates, so there is only one grouping.
Group slide data together Select the slides that measure the same thing. Later in analysis, they will be averaged together. In this experiment, all ten slides are replicates, so there is only one grouping.
