Microarray Data Analysis

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Microarray Data Analysis

• The Bioinformatics side of the bench

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The anatomy of your data files from Affymetrix
array analysis

• .DAT image file (107 pixels)
• .CEL measured cell intensities
• .CDF cell descriptions files (identify probe
  sets and probe set pairs)
• .CHP calculated probe set data
• .RPT report generated from .CHP

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Quality Control (QC) of the chip visual
inspection

• Look at the .DAT file or the .CHP file image
• Scratches? Spots?
• Corners and outside border checkerboard
  appearance (B2 oligo)
• Positive hybridization control
• Used by software to place grid over image
• Array name is written out in oligos!

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Chip defects
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Internal controls

• B. subtilis genes (added poly-A tails)
• Assessment of quality of sample preparation
• Also as hybridization controls
• Hybridization controls (bioB, bioC, bioD, cre)
• E. coli and P1 bacteriophage biotin-labeled cRNAs
  
• Spiked into the hybridization cocktail
• Assess hybridization efficiency
• Actin and GAPDH assess RNA sample/assay quality
• Compare signal values from 3 end to signal
  values from 5 end
• ratio generally should not exceed 3
• Percent genes present (P)
• Replicate samples - similar P values

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Microarray Data Process/Outline

• Experimental Design
• Image Analysis scan to intensity measures (raw
  data)
• Normalization clean data
• More low level analysis-fold change, ANOVA,
  data filtering
• Data mining-how to interpret gt 6000 measures
• Databases
• Software
• Techniques-clustering, pattern recognition etc.
• Comparing to prior studies, across platforms?
• Validation

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Experimental Design

• A good microarray design has 4 elements
• A clearly defined biological question or
  hypothesis
• Treatment, perturbation and observation of
  biological materials should minimize systematic
  bias
• Simple and statistically sound arrangement that
  minimizes cost and gains maximal information
• Compliance with MIAME (minimal information about
  microarray experiment)

• The goal of statistics is to find signals in a
  sea of noise
• The goal of exp. design is to reduce the noise so
  signals can be found with as small a sample size
  as possible

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Observational Study vs. Designed Experiment

• Observational study-
• Investigator is a passive observer who measures
  variables of interest, but does not attempt to
  influence the responses
• Designed Experiment-
• Investigator intervenes in natural course of
  events
• What type is our DMSO exp?

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Experimental Replicates

• Why?
• In any exp. system there is a certain amount of
  noiseso even 2 identical processes yield
  slightly different results
• Sources?
• In order to understand how much variation there
  is it is necessary to repeat an exp a of
  independent times
• Replicates allow us to use statistical tests to
  ascertain if the differences we see are real

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Technical vs. Biological Replicates
As we progress from the starting material to the
scanned image we are moving from a system
dominated by biological effects through one
dominated by chemistry and physics noise Within
Affy platform the dominant variation is usually
of a biological nature thus best strategy is to
produce replicates as high up the experimental
tree as possible
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Low level data analysis / pre-processing

• Varying biological or cellular composition among
  sample types.
• Differences in sample preparation, labeling or
  hybridization
• Non specific cross-hybridization of target to
  probes.
• Lead to systemic differences between individual
  arrays

• Raw Data Quality Control
• Scaling
• Normalization and filtering.

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Image Analysis - Raw Data
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From probe level signals to gene abundance
estimates
The job of the expression summary algorithm is to
take a set of Perfect Match (PM) and Mis-Match
(MM) probes, and use these to generate a single
value representing the estimated amount of
transcript in solution, as measured by that
probeset.
To do this, .DAT files containing array images
are first processed to produce a .CEL file, which
contains measured intensities for each probe on
the array. It is the .CEL files that are
analyzed by the expression calling algorithm.
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MAS 5.0 output files

• For each transcript (gene) on the chip
• signal intensity
• a present or absent call (presence call)
• p-value (significance value) for making that call
• Each gene associated with GenBank accession
  number (NCBI database)

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How are transcripts determined to be present or
absent?

• Probe pair (PM vs. MM) intensities
• generate a detection p-value
• assign Present, Absent, or Marginal call
  for transcript
• Every probe pair in a probe SET has a potential
  vote for presence call

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PM and MM Probes

• The purpose of each MM probe is to provide a
  direct measure of background and stray-signal
  (perhaps due to cross-hybridization) for its
  perfect-match partner. In most situations the
  signal from each probe-pair is simply the
  difference PM - MM.
• For some probe-pairs, however, the MM signal is
  greater than the PM value we have an apparently
  impossible measure of background.

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Thank goodness for software!!!

• MAS 5.0 does these calculations for you
• .CHP file
• Basic analysis in MAS 5.0, but it wont handle
  replicates
• Import MAS 5.0 (.CHP) data into other software,
  Genesifter, GCOS, SpotFire, and many others

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Signal Intensity

• Following these calculations, the MAS 5.0
  algorithm now has a measure of the signal for
  each probe in a probeset.
• Other algortihms, ex RMA, GCRMA, dCHIP, PLIER and
  others have been developed by academic teams to
  improve the precision and accuracy of this
  calculation
• In our Exp we will use RMA and GCRMA

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How do we want to analyze this data?

• Pairwise analysis is most appropriate
• Control vs. DMSO
• List of genes that are upregulated or
  downregulated
• Determine fold up or down cutoffs
• What is significant?
• 1.5 fold up/down?
• 2 fold up/down?
• 10 fold up/down?

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Normalization - clean data

• Normalizing data allows comparisons ACROSS
  different chips
• Intensity of fluorescent markers might be
  different from one batch to the other
• Normalization allows us to compare those chips
  without altering the interpretation of changes in
  GENE EXPRESSION

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• Why Normalize Data?
• The experimental goal is to identify biological
  variation (expression changes between samples)
• Technical variation can hide the real data
• Unavoidable systematic bias should be recognized
  and corrected
• Normalization is necessary to effectively make
  comparisons
• between chips-and sometimes within a single chip.

• There are different methods of normalization the
  assumptions of where variation exist will
  determine the normalization techniques used.
• Always look at data before and after
  normalization
• Spike in controls can help show which method may
  be best

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Caveat

• There is NO standard way to analyze microarray
  data
• Still figuring out how to get the best answers
  from microarray experiments
• Best to combine knowledge of biology, statistics,
  and computers to get answers

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Venn Diagrams
MAS 5.0
GCRMA
RMA
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Data processing is completed now what?Fold
change, ANOVA, Data filtering
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Where are we now?

• Ran analysis, output is a GENE LIST
• List indicates what genes are up or down
  regulated
• p values for t-test
• Graphs of signal levels
• Absolute numbers not as important here as the
  trends you see
• Now what????

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What is the first set of genes on our chips that
will be filtered out?
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Follow the links

• Click on a gene
• Find links to other databases
• Follow links to discover what the protein does
• Now the fun part begins.

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Back to Biology

• Do the changes you see in gene expression make
  sense BIOLOGICALLY?
• If they dont make sense, can you hypothesize as
  to why those genes might be changing?
• Leads to many, many more experiments

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The Gene Ontologies
A Common Language for Annotation of Genes from
Yeast, Flies and Mice
and Plants and Worms
and Humans
and anything else!
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Gene Ontology Objectives

• GO represents concepts used to classify specific
  parts of our biological knowledge
• Biological Process
• Molecular Function
• Cellular Component
• GO develops a common language applicable to any
  organism
• GO terms can be used to annotate gene products
  from any species, allowing comparison of
  information across species

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Sriniga Srinivasan, Chief Ontologist, Yahoo!
The ontology. Dividing human knowledge into a
clean set of categories is a lot like trying to
figure out where to find that suspenseful black
comedy at your corner video store. Questions
inevitably come up, like are Movies part of Art
or Entertainment? (Yahoo! lists them under the
latter.) -Wired Magazine, May 1996
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The 3 Gene Ontologies

• Molecular Function elemental activity/task
• the tasks performed by individual gene products
  examples are carbohydrate binding and ATPase
  activity
• Biological Process biological goal or objective
• broad biological goals, such as mitosis or purine
  metabolism, that are accomplished by ordered
  assemblies of molecular functions
• Cellular Component location or complex
• subcellular structures, locations, and
  macromolecular complexes examples include
  nucleus, telomere, and RNA polymerase II
  holoenzyme

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Example Gene Product hammer
Function (what) Process (why) Drive nail (into
wood) Carpentry Drive stake (into soil)
Gardening Smash roach Pest Control Clowns
juggling object Entertainment
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Biological Examples
Molecular Function
Biological Process
Cellular Component
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Validation

• Not enough to just do microarrays
• Usually validate microarray results via some
  other technique
• rt-PCR
• TaqMan
• Northern analysis
• Protein level analysis
• No technique is perfect

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Yeast Genome and Data Mining
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Dynamic Nature of Yeast Genome
eORF essential kORF known hORF homology
identified shORF short tORF transposon
identified qORF questionable dORF disabled
First published sequence claimed 6274 genes a
that has been revised many times, why?
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6603 4373 1410 820
The Affy detection oligonucleotide sequences are
frozen at the time of synthesis, how does this
impact downstream data analysis?
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Terms, Definitions, IDs
term MAPKKK cascade (mating sensu
Saccharomyces) goid GO0007244 definition
MAPKKK cascade involved in transduction of mating
pheromone signal, as described in
Saccharomyces definition_reference PMID9561267
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SGD
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SGD public microarray data sets available for
public query
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Homework

1. Go to http//www.yeastgenome.org/ and find 3
   candidate genes of known f(x) and one of
   undefined f(x) that you might predict to be
   altered by DMSO treatment
2. What GO biological processes and molecular
   mechanisms are associated with your candidate
   genes?
3. Where, subcellularly does the protein reside in
   the cell?
4. What other proteins are known or inferred to
   interact with yours? How was this interaction
   determined? Is this a genetic or physical
   interaction?
5. Find the expression of at least one of your known
   genes in another public ally deposited microarray
   data set?
6. Name of data set and how you found it?
7. What is the largest Fold change observed for this
   gene in the public study?
8. Now that you are microarray technology experts
   can you give me 3 reasons why the observed
   transcript level difference may not be confirmed
   through a second technology like RTQPCR?

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