Introduction to Microarray Analysis

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Introduction to Microarray Analysis

• Uma Chandran PhD, MSIS
• Department of Biomedical Informatics
• chandranur_at_upmc.edu
• 412-623-7841
• 12/17/08

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My Background

• Bioinformatics Analysis Service
• UPCI
• Department of Biomedical Informatics
• Clinical Genomics Facility
• Runs expression, SNP and microRNA microarrays
• Bioinformatics tightly integrated with data
  analysis
• Expression, SNP, proteomic, integration of
  proteomic and genomic data

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Workshop Objectives

• Introduction to microarray analysis
• Understand general principles
• BRB Array Tools from NCI
• HSLS also offers Array Assist, Genespring GX
• Not an advanced analysis course offered through
  DBMI and Biostatistics
• Not a statistics course
• Will discuss some statistical issues
• Should consult literature, statistician to
  understand methods in detail

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What is a microarray

• Probes on chips
• Detect target RNA in samples
• High throughput
• 10000s of specific probes
• Measure global gene expression
• Glass beads, chips, slides

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Bioinformatic approaches for analysis

• Measuring 10000s of data points simultaneously
• High dimensional data
• 10 Exp x 50K 500K
• How to find real differences over the noise
• Statistical approaches

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Bioinformatic approaches for analysis

• Class Comparison
• Which genes are up or down in tumors v normal,
  untreated v treated
• Class Discovery
• Within the tumor samples, are there subgroups
  that have a specific expression profile?
• Class prediction, pathway analysis etc

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Challenges in microarray analysis

• Different platforms
• Ilumina, Affymetrix, Agilent.
• Many file types, many data formats
• Need to learn platform dependent methods and
  software required
• Analysis
• How to get started?
• Which methods? Which software? Many freely
  available tools. Some commercial
• How to interpret results

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Public databases

• Many sources for public data labs, consortia,
  government
• Publications require that data files including
  raw files be made public
• GEO http//www.ncbi.nlm.nih.gov/geo/
• Array Express - http//www.ebi.ac.uk/arrayexpress/
  ae-main0

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What tools to use

• Gene Spring GX (HSLS)
• Todays exercise using
• BRB arrays tools from NCI
• Excel Interface
• First install R statistical package from
  Bioconductor
• Fairly easy to use if you dont have access to
  commercial tools
• Analysis is robust, display and graphics are
  minimal
• Learn concepts using BRB

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GEO data for Exercise

• Raetz et al
• Characterization of T-ALL, T-LL, B-ALL
• T-ALL and T-LL are morphologically
  indistinguishable
• Are there expression differences?
• Class comparison, class discovery, prediction

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Files

• C\Desktops\cmcclass\HSLSclass\
• Treeview, Cluster
• Files may also be under C\

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Hands on 1

• Google GEO
• Query Raetz et al
• Open cel files for class
• .cel files
• Affymetrix files
• Has many files including .dat, .cel. chp
• Need Affy software to open these files
• Freely downloadable

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Microarray analysis Data Preprocessing

• Objective
• Convert image of thousands of signals to a a
  signal value for each gene or probe set
• Multiple step
• Image analysis
• Background and noise subtraction
• Normalization
• Expression value for a gene or probe set
• Image analysis and bkg, noise usually done by
  proprietary software

Gene 1 100 Gene 2 150 Gene 3 75 . Gene10000 500

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Normalization
Treated Control

• Corrects for variation in hybridization etc
• Assumption that no global change in gene
  expression
• Without normalization
• Intensity value for gene will be lower on Chip B
• Many genes will appear to be downregulated when
  in reality they are not

Gene 1 100 Gene 2 150 Gene 3
75 . Gene10000 500
50 75 32 250
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How to normalize?

• Many methods Affy MAS5.0
• Median scaling median intensity for all chips
  should be the same
• Known genes, house keeping, invariant genes
• Quantile - RMA
• Normalization method may differ depending on
  platform
• Illumina cubic spline
• Affymetrix
• Choose method
• .cel to .chp file
• Which method to choose?
• Know the biology

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BRB Array tools

• Website
• Excel plug in R and fortran
• Import, choose correct format
• From .cel files
• Process using GCRMA or MAS5.0
• Or directly from processed files
• Attaches annotation
• Create experiment labels

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Ilumina Format
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Hands on 2

• Open Excel
• Click on Array Tools
• Look at data import options
• Wizard
• General format Affy, non Affy
• Affy data
• Import .cel files or already normalized files
• Various normalization options
• Clicking OK will import data dont do this now
  because time and memory intensive
• Already normalized files look at MAS5.0.txt
• For the next step, we will work with data that
  has already been imported into BRB

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Hands on continued

• Look at a normalized file MAS5.0.txt
• Open this file in Excel
• Absent, Present calls unique to Affy
• This already normalized file can also be imported
  into BRB

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Quality control

• Will not go into detail here because platform
  specific
• Read the literature for platform
• Open file lableled MAS5.0 report. Folder cel
  file to import
• Scale factor
• P calls
• Should be at least 40
• If RNA quality poor, then fewer present calls
• Control probes
• GAPDH has 3/, middle and 5 probes
• Ratio of 3/5
• Other spike in probes for over cDNA to cRNA
  synthesis
• For hybridization
• Background/noise
• All platforms will have control probes and
  quality metrics

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BRB import

• Spot filters
• For Affy array, check off
• For other arrays, could exclude if negative
• Set to threshold
• Normalization
• If importing already normalized as in MAS5.0,
  check off
• If RMA, already normalized, check off
• Other methods that do not automatically
  normalized, choose a method here
• Gene Filters
• For now, leave 20
• Rest, check off

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Hands on

• Go to BRB analysis folder
• BRB analysis\Project\Raetz.xls file
• This file shows what an imported project looks
  like
• Experiment Descriptor is the class label for each
  experiment

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Data Analysis

• Part 2- Data analysis
• Class discovery
• Class comparison
• Class prediction
• Biological annotation
• Pathway analysis

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Class Discovery

• Objective?
• Can data tell us which classes are similar?
• Are there subgroups?
• Do T-ALL, T-LL, B-ALL fall into distinct groups?
• Methods
• Hierarchical clustering
• K-means, SOM etc
• These are Unsupervised Methods
• Class Ids are not known to the algorithm
• For example, does not know which one is cancer or
  non cancer
• Do the expression values differentiate, does it
  discover new classes

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Hands on Class discovery

• Multidimensional scaling in BRB
• Raetz.xls
• Choose defaults in BRB
• Eisens Cluster
• Filter
• Accept
• Adjust
• Cluster
• Different Clustering metrics will give diff
  results
• Not a very robust method but very popular
• Use as exploratory tool

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Multidimensional scaling - MDS
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Hands on - Hierarchical Clustering

• Eisen Cluster and Treeview
• Import data
• Filter
• Filter or not to filter, P calls, SD etc
• Accept filter
• Adjust data
• Log transform (important), center, normalize
• Clustering
• Cluster array or genes
• Gene computationally intensive
• Choose distance metric
• .cdt file created
• Open with Treeview

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Class comparison differential expression
analysis

• What genes are up regulated between control and
  test or multiple test conditions
• Normal v tumor
• Treated v untreated
• Fold change
• Not sufficient, need statistics
• Statistics
• t test, non-parametric, fdr,

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Class comparison

• Many analysis methods
• May produce different results
• Different underlying statistics and methods
• t test
• t test with permutations
• SAM
• Emperical bayesian
• Depends on underlying assumptions about data
• High throughput data with many rows and few
  samples
• What is the distribution
• Variance from gene to gene
• Save raw data files to try different methods and
  compare results

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Fold change does not take variation into account
Modified from madB
http//nciarray.nci.nih.gov/
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Hypothesis Testing
Normal
Tumor
d
mean1
mean2
Null hypothesis
Alternative hypotheses
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Statistical power

• t test
• Test hypothesis that the two means are not
  statistically different
• Adding confidence to the fold change value
• Mean
• Standard deviation
• Sample size
• Calculates statistic
• You choose cutoff or threshold
• Give me gene list at a cutoff of p lt0.05
• 95 confidence that the mean for that gene
  between control are treated are different

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Experimental Design Very important!!!

• Sample size
• How many samples in test and control
• Will depend on many factors such as whether
  tissue culture or tissue sample
• Power analysis
• Replicates
• Technical v biological
• Biological replicates is more important for more
  heterogenous samples Need replicates for
  statistical analysis

• To pool or not to pool
• Depends on objective
• Sample acquistion or extraction
• Laser captered or gross dissected
• All experimental steps from sample acquisition to
  hybridization
• Microarray experiments are very expensive. So,
  plan experiments carefully

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t tests

• Results might look like
• At a plt0.05, there are 300 genes up and 200 genes
  downregulated
• 95 confidence that the means of these genes in
  the two groups is different
• At a p lt 0.05, x genes up and y genes down with a
  fold change of at least 3.0

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Multiple comparison

• Microarrays have multiple comparison problem
• p lt 0.05 says that 95 confidence means are
  different therefore 5 due to chance
• 5 of 10000 is 500
• 500 genes are picked up by chance
• Suppose t tests selects 1000 genes at a p of 0.05
• 500/1000 Approximately 50 of the genes will be
  false
• Very high false discovery rate need more
  confidence
• How to correct?
• Correction for multiple comparison
• p value and a corrected p value

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Corrections for multiple comparisons

• Involve corrections to the p value so that the
  actual p value is higher
• Bonferroni
• Benjamin-Hochberg
• Significance Analysis of Microarrays
• Tusher et al. at Stanford

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Hands on BRB

• Class comparison
• Choose comparison
• Which tests are available?
• P value cutoff
• How is multiple correction testing being done?
• Stringent p value, fdr
• How is the output reported?
• Can you figure out how many genes are regulated
  at different p values and different cutoffs
• How to interpret results
• Look at gene lists generated by our analysis v
  those generated in the paper

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BRB Hands on

• Check Experiment desc file
• Set up Class Comparison
• T-ALL v T-LL
• Choose p value
• Random variance
• Options
• Save file
• Run

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BRB Class Comparison

• Output folder
• Check the .html file
• Look at results
• P value
• Fold change
• Annotation
• Click on annotation
• Cut and paste save into Excel

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Many studies, many methods
Dupuy and Simon, JNCI 2007
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How to manipulate Gene lists

• Create gene lists
• Venn Diagram
• Can be done even though study done on different
  platforms
• Compare MAS and RMA
• Venn Diagram
• Compare B-ALL v T-LL and T-LL v B-ALL

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Venn Diagram
http//www.pangloss.com/seidel/Protocols/venn.cgi
http//ncrr.pnl.gov/software/VennDiagramPlotter.s
tm
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Conclusion

• Other analysis
• Class prediction
• Gene list from class comparison can be used in
  pathway analysis
• HSLS pathway workshops on Ingenuity, DAVID,
  Pathway Architect
• Future
• Integrate expression data with other data such as
  snp or microRNA
• GEO has some data analysis features