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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

Microarray Data Analysis Using BASE

Danny Park

MGH Microarray Core

March 15, 2004

you ve got data
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
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
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
Today’s Presentation
  • Demonstrate the most basic analysis techniques
  • Using our most frequently used software (BASE)
  • For the most common kind of experiments
work flow

QC & label

RNA

Labeled cDNA

hybridize

Slides

Researcher

scan, segment

BASE

Images & data files

upload

Work Flow

analysis

the most common experiment
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?
experimental design na ve

A

B

Experimental Design – naïve

From Gary Churchill, Jackson Labs

experimental design tech repl

A

B

Experimental Design – tech repl

From Gary Churchill, Jackson Labs

experimental design bio repl

A

A

B

B

Experimental Design – bio repl
  • Treatment
  • Biological Replicate
  • Technical Replicate
  • Dye
  • Array

From Gary Churchill, Jackson Labs

the most common analysis
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 @ 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
base my account
BASE – My Account

Change your password and access defaults

base my account1
BASE – My Account

Change your password and access defaults

base my account2
BASE – My Account

Change your password and access defaults

base my account3
BASE – My Account

Change your password and access defaults

group slide data together1
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 together2
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 together3
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 together5
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.

group slide data together6
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.

group slide data together7
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.

analysis filter setup
Analysis: Filter Setup

“Bad” spots are marked with a negative Flag value.

analysis filter setup1
Analysis: Filter Setup

“Bad” spots are marked with a negative Flag value.

analysis filter setup2
Analysis: Filter Setup

“Bad” spots are marked with a negative Flag value.

analysis filter setup3
Analysis: Filter Setup

“Bad” spots are marked with a negative Flag value.

analysis filter setup4
Analysis: Filter Setup

“Bad” spots are marked with a negative Flag value.

analysis filter setup5
Analysis: Filter Setup

“Bad” spots are marked with a negative Flag value.

analysis filter setup6
Analysis: Filter Setup

“Bad” spots are marked with a negative Flag value.

analysis filter setup7
Analysis: Filter Setup

“Bad” spots are marked with a negative Flag value.

analysis filter setup8
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)

analysis filter setup9
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)

analysis filter setup10
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)

analysis filter setup11
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)

analysis filter setup12
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)

analysis filter setup13
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)

analysis filter setup14
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)

analysis filter setup15
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)

analysis filter setup16
Analysis: Filter Setup

Naming the filter and the child data set are essential to reducing confusion later.

analysis filter setup17
Analysis: Filter Setup

Naming the filter and the child data set are essential to reducing confusion later.

analysis filter setup18
Analysis: Filter Setup

Naming the filter and the child data set are essential to reducing confusion later.

analysis check job status4
Analysis: Check job status

“All done” indicates the job is complete.

analysis check job status5
Analysis: Check job status

“All done” indicates the job is complete.

analysis change data set name2
Analysis: Change data set name

Change the name of this set to “Intensity limited Data”

analysis lowess output2
Analysis: LOWESS Output

Change the name of this set to “Normalized Data” using the same steps as before.

analysis change data set name7
Analysis: Change data set name

Change the name of this set to “Normalized Data” using the same steps as before.

analysis change data set name8
Analysis: Change data set name

Change the name of this set to “Normalized Data” using the same steps as before.

analysis filter setup19
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.

analysis change list name2
Analysis: Change list name

Change the name of this list as indicated here.

analysis change list name3
Analysis: Change list name

Change the name of this list as indicated here.

analysis change list name6
Analysis: Change list name

Change the name of this set to “myd88 p-value” using the same steps as before.

analysis change list name7
Analysis: Change list name

Change the name of this set to “myd88 p-value” using the same steps as before.

analysis change list name8
Analysis: Change list name

Change the name of this set to “myd88 p-value” using the same steps as before.

eexplore gene list view3
EExplore: Gene List View

Fill out the table as indicated, then hit Add/Update.

eexplore gene list view12
EExplore: Gene List View

This additional row will restrict hits to P values of 5% or less.

eexplore gene list view13
EExplore: Gene List View

This additional row will restrict hits to P values of 5% or less.

eexplore gene list view15
EExplore: Gene List View

Open MS Excel and tell it to open the file you downloaded (typically called base.tsv).

eexplore gene list view16
EExplore: Gene List View

Open MS Excel and tell it to open the file you downloaded (typically called base.tsv).

have fun
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.
  • Don’t forget to go back to the raw data sets and repeat this entire analysis for any other slide groupings.
acknowledgements
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

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