Transcript Statistical analysis of DNA microarray data

Gene Expression
BMI 731 week 5
Catalin Barbacioru
Department of Biomedical Informatics
Ohio State University
Thesis: the analysis of gene
expression data is going to be big in
21st century statistics
Many different technologies, including
High-density nylon membrane arrays
Serial analysis of gene expression (SAGE)
Short oligonucleotide arrays (Affymetrix)
Long oligo arrays (Agilent)
Fibre optic arrays (Illumina)
cDNA arrays (Brown/Botstein)*
Total microarray articles indexed in Medline
600
Number of papers
500
400
300
200
100
0
1995
1996
1997
1998
Year
1999
2000
2001
(projected)
Common themes
• Parallel approach to collection of very large
amounts of data (by biological standards)
• Sophisticated instrumentation, requires some
understanding
• Systematic features of the data are at least as
important as the random ones
• Often more like industrial process than single
investigator lab research
• Integration of many data types: clinical, genetic,
molecular…..databases
Biological background
Transcription
DNA
G TAAT C C T C
| | | | | | | | |
CATTAG GAG
RNA
polymerase
mRNA
Idea: measure the amount of mRNA to see which
genes are being expressed in (used by) the cell.
Measuring protein might be better, but is currently
harder.
Reverse transcription
Clone cDNA strands, complementary to the mRNA
mRNA
G U AA U C C U C
Reverse
transcriptase
cDNA
CATTAG GAG
CCAATCTTATATAG
G
A
G
T
A
G
GG
AAGG
G
G
A
C
A
T
T
A
G
G
CATTAG GAG
cDNA microarray experiments
mRNA levels compared in many different contexts
Different tissues, same organism (brain v. liver)
Same tissue, same organism (ttt v. ctl, tumor v. non-tumor)
Same tissue, different organisms (wt v. ko, tg, or mutant)
Time course experiments (effect of ttt, development)
Other special designs (e.g. to detect spatial patterns).
• DNA microarrays represent an important new
method for determining the complete expression
profile of a cell.
• Monitoring gene expression lies at the heart of a
wide variety of medical and biological research
projects, including classifying diseases,
understanding basic biological processes, and
identifying new drug targets.
Affymetrix Instrument System
®
Platform for GeneChip® Probe Arrays
•
Integrated
• Exportable
• Easy to use
•Versatile
Photolithography
Synthesis of Ordered Oligonucleotide Arrays
Light
(deprotection)
Mask
OOOOO
HO HO O O O
T–
TTOOO
Substrate
Light
(deprotection)
Mask
C AT A T
AGCTG
T TCCG
TTCCO
TTOOO
Substrate
C–
REPEAT
Affymetrix GeneChip arrays
GeneChip Probe Arrays
®
Hybridized Probe Cell
GeneChip Probe Array
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
Analysis of expression level from probe sets
A single, contiguous gene set for the rat B-actin gene.
Each pixel is quantitated and integrated for each
oligo feature (range 0-25,000)
Perfect Match (PM)
Mis Match (MM) Control
log(PM / MM) = difference score
All significant difference scores are averaged to
create “average difference” = expression level of
the gene.
Expression screening by GeneChip
• each oligo sequence (20-25 mer) is synthesized
as a 20 µ square (feature)
• each feature contains > 1 million copies of the oligo
• scanner resolution is about 2 µ (pixel)
• each gene is quantitated by 16-20 oligos and
compared to equal # of mismatched controls
• 22,000 genes are evaluated with 20 matching oligos
and 10 mismatched oligos = 480,000 features/chip
• 480,000 features are photolithographically synthesized
onto a 2 x 2 cm glass substrate
Affymetrix GeneChip arrays
• Global views of gene expression are often essential for obtaining
comprehensive pictures of cell function.
• For example, it is estimated that between 0.2 to 10% of the 10,000
to 20,000 mRNA species in a typical mammalian cell are
differentially expressed between cancer and normal tissues.
• Whole-genome analyses also benefit studies where the end goal is
to focus on small numbers of genes, by providing an efficient tool to
sort through the activities of thousands of genes, and to recognize
the key players.
• In addition, monitoring multiple genes in parallel allows the
identification of robust classifiers, called "signatures", of disease.
• Global analyses frequently provide insights into multiple facets of a
project. A study designed to identify new disease classes, for
example, may also reveal clues about the basic biology of disorders,
and may suggest novel drug targets.
cDNA microarrays
• In ‘‘spotted’’ microarrays, slides carrying spots of target DNA are
hybridized to fluorescently labeled cDNA from experimental and
control cells and the arrays are imaged at two or more wavelengths
• Expression profiling involves the hybridization of fluorescently
labeled cDNA, prepared from cellular mRNA, to microarrays carrying
thousands of unique sequences.
• Typically, a set of target DNA samples representing different genes
is prepared by PCR and transferred to a coated slide to form a 2-D
array of spots with a center-to-center distance (pitch) of about 200
μm, providing a pan-genomic profile in an area of 3 cm2 or less.
• cDNA samples from experimental and control cells are labeled with
different color fluors (cytochrome Cy5 and Cy3) and hybridized
simultaneously to microarrays, and the relative levels of mRNA for
each gene are then determined by comparing red and green signal
intensities
cDNA microarrays
Scanning Technology
• Microarray slides are imaged with a modified fluorescence
microscope designed for scanning large areas at high resolution
(arrayWoRx, Applied Precision, Issaquah, WA, Affymetrix).
• Fluorescence illumination are obtained from a metal halide arc lamp
focused onto a fiber optic bundle, the output of which is directed at
the microarray slide and emission recorded through a microscope
objective (Nikon) onto a cooled CCD (charge-coupled device)
camera.
• Interference filters are used to select the excitation and emission
wavelengths corresponding to the Cy3 and Cy5 fluorescent probes
(Amersham Pharmacia).
• Each image covered a 2.4 x 2.4 mm area of the slide at 5-μm
resolution. To scan the entire microarray, a series of images
(‘‘panels’’) were acquired by moving the slide under the microscope
objective in 2.4-mm increments.
http://www.bio.davidson.edu/course
s/genomics/chip/chip.swf
Jump to Animation
Biological question
Differentially expressed genes
Sample class prediction etc.
Experimental design
Microarray experiment
16-bit TIFF files
Image analysis
(Rfg, Rbg), (Gfg, Gbg)
Normalization
R, G
Estimation
Testing
Clustering
Biological verification
and interpretation
Discrimination
Some statistical questions
Image analysis: addressing, segmenting,
quantifying
Normalisation: within and between slides
Quality: of images, of spots, of (log) ratios
Which genes are (relatively) up/down regulated?
Assigning p-values to tests/confidence to results.
Some statistical questions, ctd
Planning of experiments: design, sample size
Discrimination and allocation of samples
Clustering, classification: of samples, of genes
Selection of genes relevant to any given
analysis
Analysis of time course, factorial and other
special experiments…..…...& much more.
Some bioinformatic questions
Connecting spots to databases, e.g. to
sequence, structure, and pathway databases
Discovering short sequences regulating sets of
genes: direct and inverse methods
Relating expression profiles to structure and
function, e.g. protein localisation
Identifying novel biochemical or signalling
pathways, ………..and much more.
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 (foreground), 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 (PM) = Rfg - Rbg
fg = foreground, bg = background, and
Green intensity (MM) = Gfg - Gbg
and combine them in the log (base 2) ratio
Log2( Red intensity / Green intensity)
Log2( PM / MM)
Gene Expression Data
On p genes for n slides: p is O(10,000), n is O(10-100), but growing,
Slides
Genes
slide 1
slide 2
slide 3
slide 4
slide 5
…
1
2
3
4
0.46
-0.10
0.15
-0.45
0.30
0.49
0.74
-1.03
0.80
0.24
0.04
-0.79
1.51
0.06
0.10
-0.56
0.90
0.46
0.20
-0.32
...
...
...
...
5
-0.06
1.06
1.35
1.09
-1.09
...
Gene expression level of gene 5 in slide 4
=
Log2( Red intensity / Green intensity)
These values are conventionally displayed
on a red (>0) yellow (0) green (<0) scale.
The red/green ratios can be spatially biased
• .
Top 2.5%of ratios red, bottom 2.5% of ratios green
Affymetrix vs. cDNA Arrays
Affy Strengths:
- highly reliable: synthesized in situ
- highly reproducible from run to run
- no clone maintenance or ‘drift’
- sealed fluidics and controlled temperature
- standardized chips increase database power
- excellent scanner
- complex, but very reliable labelling
- excellent cost/benefit ratio
- amenable to mutation and SNP detection
Affymetrix weaknesses/limitations
- not easily customized: $300K/chip
- high labeling cost $170/chip
- high per chip cost $350 to $1850
- limited choice of species
- requires knowledge of sequence
- not designed for competitive protocols
Limitations to all microarrays.
- dynamic range of gene expression:
very difficult to simultaneously detect low and high
abundance genes accurately
- each gene has multiple splice variants
2 splice variants may have opposite effects (i.e. trk)
arrays can be designed for splicing, but complexity ^ 5X
- translational efficiency is a regulated process:
mRNA level does not correlate with protein level
- proteins are modified post-translationally
glycosylation, phosphorylation, etc.
- pathogens might have little ‘genomic’ effect
Analysis
• In general the expression level of individual
genes is measured by log(PM/MM) or log(R/G).
• Intensity-dependent normalization methods are
preferred over a global methods.
• To correct intensity- and dye-bias we used
location and scale normalization methods, which
are based on robust, locally linear fits (lowess).
• Global methods use linear regression models,
combined with ANOVA.
Normalization
Why?
To correct for systematic differences between
samples on the same slide, or between slides,
which do not represent true biological variation
between samples.
How do we know it is necessary?
By examining self-self hybridizations, where no
true differential expression is occurring.
We find dye biases which vary with overall spot
intensity, location on the array, plate origin, pins,
scanning parameters,….
Analysis
Pre-normalization
Post-normalization
The simplest cDNA microarray data analysis
problem is identifying differentially expressed
genes using replicated slides
There are a number of different aspects:
• First, between-slide normalization; then
• What should we look at: averages, SDs, t-statistics, other
summaries?
• How should we look at them?
• Can we make valid probability statements?
Apo AI experiment (Matt Callow, LBNL)
Goal. To identify genes with altered expression in the livers of
Apo AI knock-out mice (T) compared to inbred C57Bl/6 control
mice (C).
• 8 treatment mice and 8 control mice
• 16 hybridizations: liver mRNA from each of the 16 mice
(Ti , Ci ) is labelled with Cy5, while pooled liver mRNA from the
control mice (C*) is labelled with Cy3.
• Probes: ~ 6,000 cDNAs (genes), including 200 related to
lipid metabolism.
Which genes have changed?
When permutation testing possible
1. For each gene and each hybridisation (8 ko + 8 ctl),
use M=log2(R/G).
2. For each gene form the t statistic:
average of 8 ko Ms - average of 8 ctl Ms
sqrt(1/8 (SD of 8 ko Ms)2 + (SD of 8 ctl Ms)2)
3. Form a histogram of 6,000 t values.
4. Do a normal q-q plot; look for values “off the line”.
5. Permutation testing (next lecture).
6. Adjust for multiple testing (next lecture).
Histogram & normal q-q plot of t-statistics
ApoA1
Patterns, More Globally...
Can we identify genes with interesting patterns
of expression across arrays?
Two approaches:
1. Find the genes whose expression fits specific,
predefined patterns.
2. Perform cluster analysis - see what expression patterns
emerge.
The 16 groups systematically arranged (6 point representation)