Microarray Data Analysis Using BASE

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Microarray Data Analysis Using BASE

• Danny Park
• MGH Microarray Core
• March 15, 2004

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Youve 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

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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 tutorials focus binary experiments

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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 tutorials focus using BASE

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Todays Presentation

• Demonstrate the most basic analysis techniques
• Using our most frequently used software (BASE)
• For the most common kind of experiments

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Work Flow
analysis
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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?

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Experimental Design naïve
From Gary Churchill, Jackson Labs
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Experimental Design tech repl
From Gary Churchill, Jackson Labs
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Experimental Design bio repl

• Treatment
• Biological Replicate
• Technical Replicate
• Dye
• Array

From Gary Churchill, Jackson Labs
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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

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BASE _at_ 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

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BASE Login page
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BASE Login page
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BASE Login page
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BASE Login page
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BASE Logged in
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BASE Logged in
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BASE Sidebar

• Reporters

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BASE Sidebar

• Reporters

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BASE Sidebar

• Array LIMS

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BASE Sidebar

• Array LIMS

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BASE Sidebar

• Biomaterials

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BASE Sidebar

• Biomaterials

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BASE Sidebar

• Hybridizations

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BASE Sidebar

• Hybridizations

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BASE Sidebar

• Analyze Data

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BASE Sidebar

• Analyze Data

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BASE Sidebar

• Users

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BASE Sidebar

• Users

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BASE My Account
Change your password and access defaults
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BASE My Account
Change your password and access defaults
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BASE My Account
Change your password and access defaults
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BASE My Account
Change your password and access defaults
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Find your experiment
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Find your experiment
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Find your experiment
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Find your experiment
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Experiment view Four Tabs
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Experiment view Four Tabs
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Experiment view Four Tabs
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Experiment view Four Tabs
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Experiment view Four Tabs
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Experiment view Four Tabs
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Experiment view Four Tabs
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Experiment view Four Tabs
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Group slide data together
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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.
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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.
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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.
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Group slide data together
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Group slide data together
Give your data set a descriptive name to
distinguish it from other slide groupings. In
this Myd88 knockout experiment, there is only one
grouping, so a generic name is fine.
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Group slide data together
Give your data set a descriptive name to
distinguish it from other slide groupings. In
this Myd88 knockout experiment, there is only one
grouping, so a generic name is fine.
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Group slide data together
Give your data set a descriptive name to
distinguish it from other slide groupings. In
this Myd88 knockout experiment, there is only one
grouping, so a generic name is fine.
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Analysis Begin
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Analysis Begin
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Analysis Begin
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Analysis Begin
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
Oligos are annotated with species codes, but
control spots are not. Set species to your
two-letter code of choice (Mm, Hs, Dr, Pa, etc)
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
Oligos are annotated with species codes, but
control spots are not. Set species to your
two-letter code of choice (Mm, Hs, Dr, Pa, etc)
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Analysis Filter Setup
Bad spots are marked with a negative Flag value.
Oligos are annotated with species codes, but
control spots are not. Set species to your
two-letter code of choice (Mm, Hs, Dr, Pa, etc)
70
Analysis Filter Setup
Bad spots are marked with a negative Flag value.
Oligos are annotated with species codes, but
control spots are not. Set species to your
two-letter code of choice (Mm, Hs, Dr, Pa, etc)
71
Analysis Filter Setup
Bad spots are marked with a negative Flag value.
Oligos are annotated with species codes, but
control spots are not. Set species to your
two-letter code of choice (Mm, Hs, Dr, Pa, etc)
72
Analysis Filter Setup
Bad spots are marked with a negative Flag value.
Oligos are annotated with species codes, but
control spots are not. Set species to your
two-letter code of choice (Mm, Hs, Dr, Pa, etc)
73
Analysis Filter Setup
Bad spots are marked with a negative Flag value.
Oligos are annotated with species codes, but
control spots are not. Set species to your
two-letter code of choice (Mm, Hs, Dr, Pa, etc)
74
Analysis Filter Setup
Bad spots are marked with a negative Flag value.
Oligos are annotated with species codes, but
control spots are not. Set species to your
two-letter code of choice (Mm, Hs, Dr, Pa, etc)
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Analysis Filter Setup
Naming the filter and the child data set are
essential to reducing confusion later.
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Analysis Filter Setup
Naming the filter and the child data set are
essential to reducing confusion later.
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Analysis Filter Setup
Naming the filter and the child data set are
essential to reducing confusion later.
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Analysis Filter Run
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Analysis Quality Data
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Analysis Quality Data
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Analysis Unfiltered Data
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Analysis Filter Parameters
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Analysis Limit-Int Setup
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Analysis Limit-Int Setup
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Analysis Limit-Int Setup
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Analysis Limit-Int Setup
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Analysis Limit-Int Setup
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Analysis Limit-Int Setup
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Analysis Check job status
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Analysis Check job status
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Analysis Check job status
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Analysis Check job status
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Analysis Check job status
All done indicates the job is complete.
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Analysis Check job status
All done indicates the job is complete.
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Analysis Limit-Int Output
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Analysis Limit-Int Output
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Analysis Limit-Int Output
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Analysis Limit-Int Output
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Analysis Limit-Int Output
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Analysis Limit-Int Output
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Analysis Change data set name
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Analysis Change data set name
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Analysis Change data set name
Change the name of this set to Intensity limited
Data
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Analysis Change data set name
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Analysis Change data set name
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Analysis Change data set name
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Analysis Change data set name
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Analysis LOWESS Setup
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Analysis LOWESS Setup
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Analysis LOWESS Setup
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Analysis LOWESS Setup
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Analysis LOWESS Setup
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Analysis LOWESS Setup
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Analysis Check job status
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Analysis Check job status
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Analysis LOWESS Output
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Analysis LOWESS Output
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Analysis LOWESS Output
Change the name of this set to Normalized Data
using the same steps as before.
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Analysis Change data set name
Change the name of this set to Normalized Data
using the same steps as before.
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Analysis Change data set name
Change the name of this set to Normalized Data
using the same steps as before.
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Analysis Filter Setup
Set up the filter as indicated, hit Add/Update on
the Gene filter, then hit Accept and select the
resulting data set.
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Analysis Useful Data
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Analysis Useful Data
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MA Plots Raw Myd88 Data
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MA Plots Raw Myd88 Data
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MA Plots Raw Myd88 Data
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MA Plots Raw Myd88 Data
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MA Plots Quality Data
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MA Plots Quality Data
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MA Plots Quality Data
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MA Plots Quality Data
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MA Plots Quality Data
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MA Plots Quality Data
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MA Plots Int-limited Data
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MA Plots Int-limited Data
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MA Plots Int-limited Data
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MA Plots Int-limited Data
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MA Plots Int-limited Data
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MA Plots Int-limited Data
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MA Plots Normalized Data
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MA Plots Normalized Data
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MA Plots Normalized Data
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MA Plots Normalized Data
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MA Plots Normalized Data
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MA Plots Normalized Data
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MA Plots Norm. Corr. Factor
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MA Plots Norm. Corr. Factor
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MA Plots Useful Data
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MA Plots Useful Data
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MA Plots Useful Data
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MA Plots Useful Data
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MA Plots Useful Data
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MA Plots Useful Data
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Analysis Useful Data
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Analysis Useful Data
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Analysis Fold Ratio Setup
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Analysis Fold Ratio Setup
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Analysis Fold Ratio Setup
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Analysis Fold Ratio Setup
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Analysis Fold Ratio Output
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Analysis Fold Ratio Output
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Analysis Fold Ratio Output
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Analysis Fold Ratio Output
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Analysis Fold Ratio Output
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Analysis Fold Ratio Output
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Analysis Fold Ratio Output
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Analysis Fold Ratio Output
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Analysis Change list name
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Analysis Change list name
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Analysis Change list name
Change the name of this list as indicated here.
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Analysis Change list name
Change the name of this list as indicated here.
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Analysis Change list name
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Analysis Change list name
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Analysis Fold Ratio Graphs
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Analysis Fold Ratio Graphs
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Analysis Fold Ratio Graphs
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Analysis Fold Ratio Graphs
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Analysis Fold Ratio Graphs
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Analysis Fold Ratio Graphs
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Analysis t-test Setup
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Analysis t-test Setup
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Analysis t-test Setup
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Analysis t-test Setup
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Analysis t-test Output
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Analysis t-test Output
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Analysis t-test Output
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Analysis t-test Output
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Analysis t-test Output
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Analysis t-test Output
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Analysis Change list name
Change the name of this set to myd88 p-value
using the same steps as before.
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Analysis Change list name
Change the name of this set to myd88 p-value
using the same steps as before.
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Analysis Change list name
Change the name of this set to myd88 p-value
using the same steps as before.
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Analysis t-test Graphs
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Analysis t-test Graphs
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Analysis t-test Graphs
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Analysis t-test Graphs
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Analysis t-test Graphs
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Analysis t-test Graphs
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Analysis Experiment Explorer
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Analysis Experiment Explorer
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EExplore Single Gene View
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EExplore Single Gene View
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EExplore Single Gene View
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EExplore Single Gene View
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EExplore Single Gene View
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EExplore Single Gene View
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EExplore Gene List View
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EExplore Gene List View
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EExplore Gene List View
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EExplore Gene List View
Fill out the table as indicated, then hit
Add/Update.
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EExplore Gene List View
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EExplore Gene List View
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EExplore Gene List View
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EExplore Gene List View
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EExplore Gene List View
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EExplore Gene List View
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EExplore Gene List View
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EExplore Gene List View
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EExplore NCBI Links
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EExplore Gene List View
This additional row will restrict hits to P
values of 5 or less.
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EExplore Gene List View
This additional row will restrict hits to P
values of 5 or less.
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EExplore Single Gene View
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EExplore Single Gene View
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EExplore Single Gene View
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EExplore Single Gene View
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EExplore Single Gene View
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EExplore Single Gene View
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EExplore Gene List View
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EExplore Gene List View
Open MS Excel and tell it to open the file you
downloaded (typically called base.tsv).
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EExplore Gene List View
Open MS Excel and tell it to open the file you
downloaded (typically called base.tsv).
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Have Fun!

• The rest of the analysis is largely driven by
  your biological understanding of the genes
  indicated in these lists. We cannot help much in
  the interpretation of this data.
• Dont forget to go back to the raw data sets and
  repeat this entire analysis for any other slide
  groupings.

232
Acknowledgements
MGH Microarray Core Glenn Short Jocelyn
Burke Najib El Messadi Jason Frietas Zhiyong Ren
MGH Lipid Metabolism Unit Mason Freeman Harry
Bjorkbacka
LUND (Sweden) Dept. Theoretical Physics Dept.
Oncology Carl Troein Lao H. Saal Johan
Vallon-Christersson Sofia Gruvberger Åke
Borg Carsten Peterson
MGH Molecular Biology Bioinformatics Group Chuck
Cooper Xiaowei Wang Harvard School of Public
Health Biostatistics Xiaoman Li