gene expression and the transcriptome i
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Gene expression and the transcriptome I. Genomics and transcriptome. After genome sequencing and annotation, the second major branch of genomics is analysis of the transcriptome - PowerPoint PPT PresentationTRANSCRIPT
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Gene expression and the transcriptome I
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Genomics and transcriptome
• After genome sequencing and annotation, the second major branch of genomics is analysis of the transcriptome
• The transcriptome is the complete set of transcripts and their relative levels of expression in particular cells or tissues under defined conditions
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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)*
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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
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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
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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
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Idea: measure the amount of mRNA to see which genes are being expressed in (used by) the cell.
Measuring protein directly might be better, but is currently harder.
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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
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Transcriptome datasets
• cDNA microarrays • Oligonucleotide arraysMost suitable for contrasting expression levels
across tissues and treatments of chosen subset of genome
• Serial analysis of gene expression (SAGE)Relies on counting sequence tags to estimate
absolute transcript levels, but less suited to replication
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cDNA microarray experiments mRNA levels compared in many different contexts
Different tissues, same organism (brain v. liver) Same tissue, same organism (treatment v. control, tumor v. non-tumor) Same tissue, different organisms (wildtype v. knock-out, transgenic, or
mutant)
Time course experiments (effect of treatment, development)
Other special designs (e.g. to detect spatial patterns).
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cDNA microarrays
cDNA clones
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cDNA microarraysCompare the genetic expression in two samples of cells
cDNA from one gene on each spot
SAMPLES
cDNA labelled red/green
with fluorescent dyes
e.g. treatment / control
normal / tumor tissueRobotic printing
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HYBRIDIZE
Add equal amounts of labelled cDNA samples to microarray.
SCAN
Laser Detector
Detector measures ratio of induced fluorescence of two samples
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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)
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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.
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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.
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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.
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Some basic problems….
…with automatically scanning the microarrays
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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
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Does one size fit all?
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Segmentation: limitation of the fixed circle method
Segmented regions Fixed Circle
Inside the boundary is spot (foreground), outside is not – Background pixels are those immediately surrounding circle/segment boundary
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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)
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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.
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The red/green ratios can be spatially biased
• .Top 2.5%of ratios red, bottom 2.5% of ratios green
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The red/green ratios can be intensity-biased if one dye is under-incorporated relative to the other
M = log2R/G = log2R - log2G
A = log2(RG)/2 = (log2R + log2G)/2
Values should scatter about zero.
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Orange: Schadt-Wong rank invariant set Red line: Loess smooth
Normalization: how we “fix” the previous problemLoess transformation (Yang et al., 2001)
The curved line becomes the new zero line
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Normalizing: before-4 T
o co
mpa
re w
ith
last
sli
de, t
his
is u
nder
……
.A
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Normalizing: after
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Normalisation of microarray data
Red Green Diff R(G/R) Log2R Norm.
16500 15104 -1396 0.915 -0.128 -0.048
357 158 -199 0.443 -1.175 -1.095
8250 8025 -225 0.973 -0.039 0.040
978 836 -142 0.855 -0.226 -0.146
65 89 24 1.369 0.453 0.533
684 1368 539 2.000 1.000 1.080
13772 11209 -2563 0.814 -0.297 -0.217
856 731 -125 0.854 -0.228 -0.148
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Analysis of Variance (ANOVA) approach
• ANOVA is a robust statistical procedure• Partitions sources of variation, e.g. whether
variation in gene expression is less in subset of data than in total data set
• Requires moderate levels of replication (4-10 replicates of each treatment)
• But no reference sample needed• Expression judged according to statistical
significance instead of by adopting arbitrary thresholds
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Analysis of Variance (ANOVA) approachhas two steps
• Raw fluorescence data is log-transformed and arrays an dye channels are normalised with respect to one another. You get normalised expression levels where dye and array effects are eliminated
• A second model is fit to normalised expression levels associated with each individual gene
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Analysis of Variance (ANOVA) approach
• Advantage: design does not need reference samples• Concern: treatments should be randomised and all
single differences between treatments should be covered
E.g., if male kidney and female liver are contrasted on one set, and female kidney and male liver on another, we cannot state whether gender or tissue type is responsible for expression differences observed
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Analysis of Variance (ANOVA) experimental microarray setups
• Loop design of experiments possible: A-B, B-C, C-D, and D-A
• Flipping of dyes to filter artifacts due to preferential labeling
• Completely or partially randomised designs
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Analysis of Variance (ANOVA)
• Within-array variance among replicated clones is much lower than between-array variance, due to stoichiometry of labeling during reverse transcription
• So do not duplicate spots on same array, this renders effects seemingly large
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Oligonucleotide arrays
• Affymetrix GeneChip
• No cDNA library but 25-mer oligonucleotides
• Oligomers designed by computer program to represent known or predicted open reading frames (ORFs)
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Oligonucleotide arrays
• Up to 25 oligos designed for each exon • Each oligo printed on chip adjacent to (single base
pair) mismatch oligo• Match/mismatch oligos used to calculate signal
intensity and then expression level
• But: not everybody agrees with Affymetrix mismatch strategy: is it biologically relevant?
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Oligonucleotide arrays
• High-density oligonucleotide chips are constructed on a silicon chip by photolithography and combinatorial chemistry
• Several hundred thousand oligos with mismatch control can be rapidly synthesised on thousands of identical chips
• Expensive technology – individual chips cost hundreds of Dollars
• Cost is issue with degree of replication
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SCIENTIFIC: To determine which genes are differentially expressed between two sources of mRNA (treatment, control).
STATISTICAL: To assign appropriately adjusted p-values to thousands of genes.
Basic problems
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Some statistical research stimulated by microarray data analysis
•Experimental design : Churchill & Kerr
•Image analysis: Zuzan & West, ….
•Data visualization: Carr et al
•Estimation: Ideker et al, ….
•Multiple testing: Westfall & Young , Storey, ….
•Discriminant analysis: Golub et al,…
•Clustering: Hastie & Tibshirani, Van der Laan, Fridlyand & Dudoit, ….
•Empirical Bayes: Efron et al, Newton et al,…. Multiplicative models: Li &Wong
•Multivariate analysis: Alter et al
•Genetic networks: D’Haeseleer et al and more
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• 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).
Example 1Apo AI experiment (Callow et al 2000, LBNL)
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Example 2 Leukemia experiments (Golub et al 1999,WI)
Goal. To identify genes which are differentially expressed in acute lymphoblastic leukemia (ALL) tumours in comparison with acute myeloid leukemia (AML) tumours.
• 38 tumour samples: 27 ALL, 11 AML.• Data from Affymetrix chips, some pre-processing.• Originally 6,817 genes; 3,051 after reduction.
Data therefore a 3,051 38 array of expression values.