Download Statistical Analyses of Microarray Data

Statistical Analyses of
Microarray Data
Rafael A. Irizarry
Department of Biostatistics
[email protected]
http://biosun01.biostat.jhsph.edu/~ririzarr
Outline
•
•
•
•
Scientific questions
Review of technology
Role of statistics
Two case studies
Scientific Questions
• Expression
“To understand gene function, it is helpful to know when and
where it is expressed and…”
• Differential expression
“…under what circumstances the expression level is affected.”
• Expression patterns
“… questions concerning functional pathways and how cellular
components work together to regulate and carry out cellular
processes.”
Lipshutz et al. (1999) Nature genetics, 21, pp. 20-21
What do Microarrays do?
Interrogate labeled nucleic acid samples
• RNA samples
model systems, microdissections, cell lines, human tissue bank
• Oligonucleotide barcodes
UPTAG
kanR
DOWNTAG
How do they do it?
Labeled targets
Probes
cDNA Arrays
cDNA clones
(probes)
excitation
laser 2
scanning
laser 1
emission
PCR product amplification
purification
printing
mRNA target
overlay image and normalize
0.1nl/spot
microarray
Hybridize
target to
microarray
analysis
High Density Oligonucleotide Arrays
GeneChip Probe Array
Hybridized Probe Cell
Single stranded,
labeled RNA target
*
*
*
*
*
Oligonucleotide probe
24µm
1.28cm
Millions of copies of a specific
oligonucleotide probe
>200,000 different
complementary probes
Image of Hybridized Probe Array
Compliments of D. Gerhold
Role of Statistics
Biological question
Differentially expressed genes
Sample class prediction etc.
Experimental design
Microarray experiment
Image analysis
Quantify Expression
Normalization
Estimation
Testing
Clustering
Biological verification
and interpretation
Discrimination
Part of the image of one channel false-coloured on a
white (v. high) red (high) through yellow and green (medium)
to blue (low) and black scale
Does one size fit all?
Segmentation: limitation of the
fixed circle method
SRG
Fixed Circle
Inside the boundary is spot (fg), outside is not.
Some local backgrounds
Single channel
grey scale
We use something different again: a smaller, less variable value.
Quantification of Expression
For each spot on the slide we calculate
Red intensity = Rfg – Rbg
fg = foreground, bg = background, and
Green intensity = Gfg – Gbg
and combine them in the log (base 2) ratio
Log2( Red intensity / Green intensity)
we now have one differential expression for each gene for
each array
The red-green ratios can be spatially biased
Top 2.5%of ratios red, bottom 2.5% of ratios green
Another example
Oligo Array Image Analysis
• About 100 pixels per
probe cell
• These intensities are
combined to form one
number representing
expression for the
probe cell oligo
Normalization at Probe Level
Normalization at Probe Level
Dilution Experiment Data
Dilution Experiment Data
PM MM
Default until 2002
• GeneChip® software uses Avg.diff
1
Avg.diff 

 ( PM
j
j
 MM j )
with A a set of “suitable” pairs chosen by software.
• Log ratio version is also used.
• For differential expression Avg.diffs are compared
between chips.
What is the evidence?
Lockhart et. al. Nature Biotechnology 14 (1996)
Two case studies
Spike-In Experiments
• Add concentrations (0.5pM – 100 pM) of 11
foreign species cRNAs to hybridization mixture
• Set A: 11 control cRNAs were spiked in, all at the
same concentration, which varied across chips.
• Set B: 11 control cRNAs were spiked in, all at
different concentrations, which varied across
chips. The concentrations were arranged in 12x12
cyclic Latin square (with 3 replicates)
Set A: Probe Level Data (12 chips)
Spike-In B
Probe Set
Conc 1
Conc 2
Rank
BioB-5
100
0.5
1
BioB-3
0.5
25.0
2
BioC-5
2.0
75.0
3
BioB-M
1.0
35.7
4
BioDn-3
1.5
50.0
5
DapX-3
35.7
3.0
6
CreX-3
50.0
5.0
7
CreX-5
12.5
2.0
8
BioC-3
25.0
100
9
DapX-5
5.0
1.5
10
DapX-M
3.0
1.0
11
Later we consider 23 different combinations of concentrations
Observed Ranks
Gene
AvDiff MAS 5.0 Li&Wong AvLog(PM-BG)
BioB-5
6
2
77
1
BioB-3
16
1
33
2
BioC-5
74
6
22
5
BioB-M
30
3
6
3
BioDn-3
44
5
27
4
DapX-3
239
24
796
7
CreX-3
333
73
386
11
CreX-5
3276
33
43
9
BioC-3
2709
8572
12
10300
DapX-5
2709
102
59
17
DapX-M
165
19
30
6
A
B
DOWNTAG
kanR
UPTAG
CEN/ARS
CEN/ARS
Circular pRS416
EcoRI linearized PRS416
Transformation into deletion pool
Select for Ura+ transformants
Genomic DNA preparation
Cy5 labeled PCR products
PCR
Cy3 labeled PCR products
Oligonucleotide array hybridization
NHEJ Defective
YKU70
NEJ1
YKU80
YKU70
NEJ1
YKU80
• .
Average Red and Green Scatter Plot
Average Red and Green MVA plot
Histograms
QQ-Plot
Z-Scores
Average Red and Green MVA Plot
Average Red and Green Scatter Plot
Summary
• Simple data exploration useful tool for
quality assessment
• Statistical thinking helpful for interpretation
• Statistical models may help find signals in
noise
Acknowledgements
Biostatistics
Karl Broman
Leslie Cope
Carlo Coulantoni
Giovanni Parmigiani
Scott Zeger
MBG (SOM)
Jef Boeke
Siew-Loon Ooi
Marina Lee
Forrest Spencer
UC Berkeley Stat
Ben Bolstad
Sandrine Dudoit
Terry Speed
Jean Yang
Gene Logic
WEHI
Francois Colin
Bridget Hobbs
Uwe Scherf’s Group Natalie Thorne
PGA
Tom Cappola
Skip Garcia
Joshua Hare