1. Terry Speed Wald Lecture III August 9, 2001
Gene expression
Terry Speed
Wald Lecture III
August 9, 2001

2. 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)*

3. Total microarray articles indexed in Medline
100
200
300
400
500
600
(projected)
Year
Number of papers

4. 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

5. Biological background
G U A A U C C
RNA polymerase
mRNA
Transcription
DNA
G T A A T C C T C
| | | | | | | | |
C A T T A G G A G

6. 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.

7. Reverse transcription
Clone cDNA strands, complementary to the mRNA
mRNA
G U A A U C C U C
Reverse transcriptase
cDNA
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
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

8. 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). 4

9. cDNA microarrays
cDNA clones

10. cDNA microarrays
Compare the genetic expression in two samples of cells
PRINT
cDNA from one gene on each spot
SAMPLES
cDNA labelled red/green
e.g. treatment / control
normal / tumor tissue

11. HYBRIDIZE
Add equal amounts of labelled cDNA samples to microarray.
SCAN
Laser
Detector

12. 16-bit TIFF files (Rfg, Rbg), (Gfg, Gbg) R, G 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
Discrimination
Biological verification
and interpretation

13. 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. 4

14. 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. 4

15. 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. 4

16. Part of the image of one channel false-coloured on a white (v
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

17. Does one size fit all?

18. Segmentation: limitation of the fixed circle method
SRG
Fixed Circle
Inside the boundary is spot (foreground), outside is not.

19. Some local backgrounds
Single channel
grey scale
We use something different again: a smaller, less variable value.

20. 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)

21. Gene Expression Data = slide 1 slide 2 slide 3 slide 4 slide 5 …
On p genes for n slides: p is O(10,000), n is O(10-100), but growing,
Slides
slide 1 slide 2 slide 3 slide 4 slide 5 …
Genes 3
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.

23. The red/green ratios can be spatially biased.
Top 2.5%of ratios red, bottom 2.5% of ratios green

24. The red/green ratios can be intensity-biased
M = log2R/G
= log2R - log2G
Values should scatter about zero.
= (log2R + log2G )/2

25. Normalization: how we “fix” the previous problem
The curved line becomes the new zero line
Orange: Schadt-Wong rank invariant set Red line: lowess smooth
Yellow: GAPDH, tubulin Light blue: MSP pool / titration

26. Normalizing: before 2 M -2 -4

27. Normalizing: after 2 -2 -4
M normalised

28. 5 minute break Come back for a fascinating case study
Where are we now?
5 minute break
Come back for a fascinating case study

29. From a study of the mouse olfactory system
Main (Auxiliary)
Olfactory Bulb
VomeroNasal
Organ
Olfactory
Epithelium
From Buck (2000)

30. Axonal connectivity between the nose and the mouse olfactory bulb
>2M, ~1,800 types
Neocortex
Two principles: “zone-to-zone projection”, and “glomerular convergence”

31. Of interest: the hardwiring of the vertebrate olfactory system
Expression of a specific odorant receptor gene by an olfactory neuron.
Targeting and convergence of like axons to specific glomeruli in the olfactory bulb.

32. The biological question in this case
Are there genes with spatially restricted expression patterns within
the olfactory bulb?

34. Layout of the cDNA Microarrays
Sequence verified mouse cDNAs
19,200 spots in two print groups of 9,600 each
4 x 4 grid, each with 25 x24 spots
Controls on the first 2 rows of each grid. 77
pg1
pg2

35. Design: How We Sliced Up the BulbA D P L V M

36. Design: Two Ways to Do the Comparisons
Goal: 3-D representation of gene expression
Compare all samples to a common reference sample (e.g., whole bulb)
Multiple direct comparisons between different samples (no common reference) P D M A V L R P D M A V L

37. An Important Aspect of Our Design
Different ways of estimating the same contrast:
e.g. A compared to P
Direct = A-P
Indirect = A-M + (M-P) or
A-D + (D-P) or
-(L-A) - (P-L) M L V P
How do we combine these?

38. Analysis using a linear model
Define a matrix X so that E(M)=X
Use least squares estimates for A-L, P-L, D-L, V-L, M-L
In practice, we use robust regression.
Estimates for other estimable contrasts follow in the usual way.

39. The Olfactory Bulb Experiments
completed so far

40. Contrasts & Patterns
Because of the connectivity of our experiment, we can estimate all 15 different pairwise comparisons directly and/or indirectly.
For every gene we thus have a pattern based on the 15 pairwise comparisons.
Gene #15,228

41. Contrasts & patterns:another way
Instead of estimating pairwise comparisons between each of the six effects, we can come closer to estimating the effects themselves by doing so subject to the standard zero sum constraint (6 parameters, 5 d.f.).
What we estimate for A, say, subject to this constraint, is in reality an estimate of
A - 1/6(A + P + D + V + M + L).
This set of parameter estimates gives results similar to, but better than, the ones we would have obtained had we carried out the experiments with whole-bulb reference tissue.
In effect we have created the whole-bulb reference in silico.

42. Alternative pattern representation
Gene #15,228
once again.

43. Reconstruction of the Bulb as a Cube: Expression of Gene # 15,228
High
Low
Expression
Level

44. Patterns, More Globally...
Can we identify genes with interesting patterns of expression across the bulb?
Two approaches:
1. Find the genes whose expression fits specific, predefined patterns.
2. Perform cluster analysis - see what expression patterns emerge.

45. Clustering procedure
Start with a sets of genes exhibiting some minimal level of differential expression across the bulb; here ~650 were chosen from all 15 contrasts.
Carry out hierarchical clustering, building a dendrogram: Mahalanobis distance and Ward agglomeration (minimum variance) were used.
Now consider all clusters of 2 or more genes in the tree. Singles are added separately.
Measure the heterogeneity h of a cluster by calculating the 15 SDs
across the cluster of each of the pairwise effects, and taking the largest.
Choose a score s (see plots) and take all maximal disjoint clusters with
h < s. Here we used s = and obtained 16 clusters.

46. PA DA VA LA DP VP MP MA LP VD MD LV LM MV LD
Red :genes chosen
Blue:controls
15 p/w effects

48. The 16 groups systematically arranged (6 point representation)

50. Validation of Gene # 15,228 Expression Pattern by RNA In Situ Hybridization
gluR
CTX
MOB
AOB
#15,228
CTX
AOB
MOB

51. Gene 15,228: another in situ view

52. In closing: The pervasiveness of microarray technology
and the statistical problems that go with it
Hybridization of target DNA or RNA to large numbers of probes attached to a solid support in a microarray format has a much wider applicability.
All such applications have their own statistical problems. Here are two relating to the previous lectures.

53. Meiosis data in which all exchanges
are precisely located (from microarrays)
Yeast
Figure courtesy of J Derisi

54. Exon Arrays can validate Exon Predictions and assemble Gene Structures
One or more Probes per Predicted Exon
Predicted exon
Predicted exon
Verify predicted exons on a genome-wide scale.
Group exons into genes via co-regulation.
This and the next slide courtesy of Rosetta

55. Tiling arrays can identify exons and refine gene structures
Predicted exon
Predicted exon
Oligonucleotides
60 bp in length
“60-mers”
10 bp steps

56. 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

57. Acknowledgments Statistical collaborators Yee Hwa Yang (Berkeley)
Sandrine Dudoit (Berkeley)
Ingrid Lönnstedt (Uppsala)
Natalie Thorne (WEHI)
Mauro Delorenzi (WEHI)
CSIRO Image Analysis Group
Michael Buckley
Ryan Lagerstorm
WEHI
Glenn Begley
Suzie Grant
Rob Good
PMCI
Chuang Fong Kong
Ngai Lab (Berkeley)
Cynthia Duggan
Jonathan Scolnick
Dave Lin
Vivian Peng
Percy Luu
Elva Diaz
John Ngai
LBNL
Matt Callow
RIKEN Genomic Sciences Center
Yasushi Okazaki
Yoshihide Hayashizaki