Transcript Microarray Data Analysis

Microarray Data Analysis
The Bioinformatics side of the bench
The anatomy of your data files
from Affymetrix array analysis
• .DAT= image file (107 pixels)
• .CEL= measured cell intensities
• .CHP= calculated probe set data
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!
Chip defects
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
Microarray Data Process/Outline
1. Experimental Design
2. Image Analysis – scan to intensity measures (raw
data)
3. Normalization – “clean” data
4. More “low level” analysis-fold change, ANOVA,
data filtering
5. Data mining-how to interpret > 6000 measures
– Databases
– Software
– Techniques-clustering, pattern recognition etc.
– Comparing to prior studies, across platforms?
6. Validation
Experimental Design
A good microarray design has 4 elements
1.
A clearly defined biological question or hypothesis
2.
Treatment, perturbation and observation of biological
materials should minimize systematic bias
3.
Simple and statistically sound arrangement that minimizes
cost and gains maximal information
4.
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
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
Experimental Replicates
• Why?
– In any exp. system there is a certain amount of
noise—so even 2 identical processes yield slightly
different results
– 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
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
Image Analysis - Raw Data
From probe level signals to gene
abundance estimates
The job of the MAS 5.0 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.
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
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
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.
MAS 5.0 gives a first level
look at the data
• MAS 5.0 does the calculations for you
– .CHP file (presence call, p-value and
expression signal).
• Basic analysis in MAS 5.0, but it won’t
handle replicates
Signal Intensity Across
Chips
• Other algorithms, ex. RMA, GCRMA, PLIER and
others have been developed by academic teams
to improve the precision and accuracy of signal
calculations (no mismatch) and comparison
across chips (normalization).
• Import (.CEL) data into other software,
Genesifter, GCOS, SpotFire, and many others.
• In our Exp we will use Genesifter software and
the RMA expression algorithm.
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
– Normalization is necessary to effectively
make comparison between chips-and
sometimes within a single chip.
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
Low level data processing is
completed now what?
Fold change, ANOVA, Data filtering
How do we want to analyze
our data?
• Pairwise analysis is most appropriate
– Control vs. H2O2
• List of genes that are “up-regulated” or
“down-regulated”
(t-test)
Where are we now?
Through this analysis we now have a
list of genes that we believe are
differentially expressed.
– Now what????
Higher Level
Microarray data analysis
• Clustering and pattern detection
• Data mining and visualization
• Linkage between gene expression data and
gene sequence/function/metabolic pathways
databases
Scatter plot of all genes in a
simple comparison of two
control (A) and two
treatments (B: high vs. low
glucose) showing changes in
expression greater than 2.2
and 3 fold.
Types of Clustering
• Herarchical
– Link similar genes, build up to a tree of all
• Self Organizing Maps (SOM)
– Split all genes into similar sub-groups
– Finds its own groups (machine learning)
Cluster by
color/expression
difference
Self Organizing Maps
Back to Biology
• Do the changes you see in gene
expression make sense
BIOLOGICALLY?
• If they don’t make sense, can you
hypothesize as to why those genes
might be changing?
• Leads to many, many more
experiments
The Gene Ontologies
A Common Language for Annotation of
Genes from
Yeast, Flies and Mice
…and Plants and Worms
…and Humans
…and anything else!
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
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
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
Example:
Gene Product = hammer
Function (what)
Process (why)
Drive nail (into wood)
Carpentry
Drive stake (into soil)
Gardening
Smash roach
Pest Control
Clown’s juggling object
Entertainment
S. pombe Genome and Data Mining
Genome Overview and Statistics
Gene Status Overview
25/08/06
27/02/07
Experimentally characterised (or published)
Role inferred from homology
Conserved protein (unknown biological role)
S. pombe specific families
Sequence orphan
Dubious
1560 (31.3%)
2433 (48.9%)
458 (9.2%)
68 (1.4%)
403 (8.1%)
57 (1.1%)
1607 (32.1)%
2329 (46.5)%
572
(11.4) %
47
(0.9)%
364 (7.3) %
60
(1.2)%