Gene Expression BMI 731 week 5 Catalin Barbacioru Department of Biomedical Informatics Ohio State University.

Gene Expression

BMI 731 week 5

Catalin BarbacioruDepartment 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)*

1995 1996 1997 1998 1999 2000 2001

0

100

200

300

400

500

600

(projected)

Year

Num

ber

of

papers

Total microarray articles indexed in Medline

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

G T A A T C C T C | | | | | | | | | C A T T A G G A G

DNA

G U A A U C C

RNA polymerase

mRNA

Transcription

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 transcriptionClone cDNA strands, complementary to the mRNA

G U A A U C C U C

Reverse transcriptase

mRNA

cDNA

C A T T A G G A G C A T T A G G A G C A T T A G G A G C A T T A G G A G

T T A G G A G

C A T T A G G A G C A T T A G G A G C A T T A G G A G

C A T T A G G A G

C A T T A G G A G

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 GeneChipPlatform for GeneChip®® Probe Arrays Probe Arrays

• IntegratedIntegrated

• Easy to useEasy to use• ExportableExportable

•VersatileVersatile

Photolithography

Synthesis of Ordered Oligonucleotide Arrays

O O O O O

Light(deprotection)

HO HO O O O T T O O O

T T C C O

Light(deprotection)

T T O O O

C A T A TA G C T GT T C C G

MaskMask

SubstrateSubstrate

MaskMask

SubstrateSubstrate

T –T –

C –C –REPEATREPEAT

Affymetrix GeneChip arrays

GeneChip® Probe Arrays

24µm24µm

Millions of copies of a specificMillions of copies of a specificoligonucleotide probeoligonucleotide probe

Image of Hybridized Probe ArrayImage of Hybridized Probe Array

>200,000 different>200,000 differentcomplementary probes complementary probes

Single stranded, Single stranded, labeled RNA targetlabeled RNA target

Oligonucleotide probeOligonucleotide probe

**

**

*

1.28cm1.28cm

GeneChipGeneChip Probe ArrayProbe ArrayHybridized Probe CellHybridized Probe Cell

A single, contiguous gene set for the rat B-actin gene.

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.

Each pixel is quantitated and integrated for each oligo feature (range 0-25,000)

Analysis of expression level from probe sets

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/courses/genomics/chip/chip.swf

Jump to Animation

Biological questionDifferentially expressed genesSample class prediction etc.

Testing

Biological verification and interpretation

Microarray experiment

Estimation

Experimental design

Image analysis

Normalization

Clustering Discrimination

R, G

16-bit TIFF files

(Rfg, Rbg), (Gfg, Gbg)

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

We use something different again: a smaller, less variable value.

Single channelgrey scale

Quantification of expressionFor each spot on the slide we calculate

Red intensity (PM) = Rfg - Rbgfg = 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,

Genes

Slides

Gene expression level of gene 5 in slide 4

= Log2( Red intensity / Green intensity)

slide 1 slide 2 slide 3 slide 4 slide 5 …

1 0.46 0.30 0.80 1.51 0.90 ...2 -0.10 0.49 0.24 0.06 0.46 ...3 0.15 0.74 0.04 0.10 0.20 ...4 -0.45 -1.03 -0.79 -0.56 -0.32 ...5 -0.06 1.06 1.35 1.09 -1.09 ...

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 ArraysAffy 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-translationallyglycosylation, 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?

• 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.

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).

Apo AI experiment (Matt Callow, LBNL)

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 Mssqrt(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...

1. Find the genes whose expression fits specific, predefined patterns.

2. Perform cluster analysis - see what expression patterns emerge.

Can we identify genes with interesting patterns of expression across arrays?

Two approaches:

The 16 groups systematically arranged (6 point representation)