Transcript STAT 254 -lecture1 An overview

STAT 254 -lecture1
An overview
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Cell biology, microarray, statistics
Bioinformatics and Statistics
Topics to cover
Keep a skeptical eye on everything you read or
hear
• Keep an eye on bigger picture; while working on
specifics
• The shaping of bioinformatics falls on your
shoulders
• What to take home : not just microarray, or high
throughput data analysis methods, but a set of
skills, ways of thinking about quantitative biology
20 min
Exploratory data analysis
multivariate
high dimensional
IMS ENAR Conference
Time : March 31, 2003
Place:Tampa, FL
Study of Gene Expression:
Statistics, Biology, and Microarrays
Ker-Chau Li
Statistics Department
UCLA
[email protected]
Outline
• Review of cell biology
Microarray gene expression data collection
• Cell-cycle gene expression (Main Data set)
• PCA/Nested regression; SIR (Dim. red.)
• Similarity analysis - clustering (Why Popular?)
• Liquid association
• Closing remarks
New statistical
concept, fueled
by Stein’s
lemma
Justification for IMS
PART I. Cellular Biology
Macromolecules: DNA, mRNA,
protein
Why Biology hot?
Because of
Human Genome Project
Begun in 1990, the U.S. Human Genome Project is a 13-year effort coordinated by the U.S.
Department of Energy and the National Institutes of Health. The project originally was planned to last
15 years, but effective resource and technological advances have accelerated the expected completion date to 2003. Project
goals are to
■ identify all the approximate 30,000 genes in human DNA,
■ determine the sequences of the 3 billion chemical base pairs that
make up human DNA,
■ store this information in databases,
■ improve tools for data analysis,
■ transfer related technologies to the private sector, and
■ address the ethical, legal, and social issues (ELSI) that may arise from the project.
Recent Milestones:
■ June 2000 completion of a working draft of the entire human genome
■ February 2001 analyses of the working draft are published
Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001
Future Challenges:
What We Still Don’t Know
• Predicted vs experimentally determined gene function {1}
•Gene regulation {2} (upstream regulatory region)
• Coordination of gene expression, protein synthesis, and posttranslational events {3}
• Gene number, exact locations, and functions
• DNA sequence organization
• Chromosomal structure and organization
• Noncoding DNA types, amount, distribution, information content, and functions
• Interaction of proteins in complex molecular machines
• Evolutionary conservation among organisms
• Protein conservation (structure and function)
• Proteomes (total protein content and function) in organisms
• Correlation of SNPs (single-base DNA variations among individuals) with health and disease
• Disease-susceptibility prediction based on gene sequence variation
• Genes involved in complex traits and multigene diseases
• Complex systems biology including microbial consortia useful for environmental restoration
• Developmental genetics, genomics
Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001
Medicine and the New Genomics
• Gene Testing
• Gene Therapy
•Pharmacogenomics
Anticipated Benefits
•improved diagnosis of disease
•earlier detection of genetic predispositions to disease
•rational drug design
•gene therapy and control systems for drugs
•personalized, custom drugs
Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001
Anticipated Benefits
Agriculture, Livestock Breeding, and Bioprocessing
• disease-, insect-, and drought-resistant crops
• healthier,
more productive, disease-resistant farm
animals
• more nutritious produce
• biopesticides
• edible vaccines incorporated into food products
• new environmental cleanup uses for plants like tobacco
Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001
How does the cell work?
The guiding principle is the so-called
Medicine and the New Genomics
• Gene Testing
• Gene Therapy
•Pharmacogenomics
Anticipated Benefits
•improved diagnosis of disease
•earlier detection of genetic predispositions to disease
•rational drug design
•gene therapy and control systems for drugs
•personalized, custom drugs
Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001
Anticipated Benefits
Agriculture, Livestock Breeding, and Bioprocessing
• disease-, insect-, and drought-resistant crops
• healthier,
more productive, disease-resistant farm
animals
• more nutritious produce
• biopesticides
• edible vaccines incorporated into food products
• new environmental cleanup uses for plants like tobacco
Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001
How does the cell work?
The guiding principle is the so-called
Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001
Gene to protein
4 Nucleotides and 20 amino acids
Protein is synthesized from amino acids by
ribosome
Gene to Protein
Transcription
Translation
Transcription and translation
PART II. Microarray
Genome-wide expression profiling
Exploring the Metabolic and Genetic Control of
Gene Expression on a Genomic Scale
Joseph L. DeRisi, Vishwanath R. Iyer, Patrick O. Brown*
Microarray
MicroArray
• Allows measuring the mRNA level of thousands
of genes in one experiment -- system level
response
• The data generation can be fully automated by
robots
• Common experimental themes:
–Time Course (when)
–Tissue Type (where)
–Response (under what conditions)
–Perturbation: Mutation/Knockout, Knock-in
Over-expression
Mic roArra y T
ec hniq ue:
Synthesize Gene
Sp ec ific DNA Oligos
Tissue or Cell
Atta c h oligo to
Solid Sup p ort
extra c t m RNA
Am p lific a tion
a nd La b eling
Hyb rid ize
Reverse-transcription
Color : cy3, cy5
green, red
Sc a n a nd Qua ntita te
5 min
Example 1
Comparative expression
Normal versus cancer cells
ALL versus AML
E.Lander’s group at MIT
PART III. Statistics
Low-level analysis
Comparative expression
Feature extraction
Clustering/classification
Pearson correlation
Liquid association
(not to be covered)
Issues related to image qualities
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Convert an image into a number representing the ratio of the levels of
expression between red and green channels
Color bias
Spatial, tip, spot effects
Background noises
cDNA, oligonucleotide arrays,
Genome-wide expression profile
A basic structure
cond1 cond2 …….. condp
Gene1 x11
x12 …….. x1p
Gene2
x21
x22 …….. x2p
…
…
...
…
…
...
Genen xn1
xn2 …….. xnp
Cond1, cond2, …, condp denote various
environmental conditions, time points, cell types,
etc. under which mRNA samples are taken
Note : numerous cells are involved
Data quality issues : 1. chip (manufacturer)
2. mRNA sample (user)
It is important to have a homogeneous
sample so that cellular signals can be
amplified
Yeast Cell Cycle data : ideally all cells are engaged in the same activities- synchronization
An application
Two classes
problem
ALL (acute lymphoblastic
leukemia)
AML(acute myeloid leukemia)
Which Genes to select?
They have a method
• For each gene (row) compute a score defined by
sample mean of X - sample mean of Y
divided by
standard deviation of X + standard deviation of Y
• X=ALL, Y=AML
• Genes (rows) with highest scores are selected.
That seems to work well.
•34 new leukemia samples
•29 are predicated with 100% accuracy;
5 weak predication cases
Seems to work ! Improvement?
Study of cell-cycle regulated
genes
• Rate of cell growth and division varies
• Yeast(120 min), insect egg(15-30 min); nerve
cell(no);fibroblast(healing wounds)
• Regulation : irregular growth causes cancer
• Goal : find what genes are expressed at each state
of cell cycle
• Yeast cells; Spellman et al (2000)
• Fourier analysis: cyclic pattern
Yeast Cell Cycle
(adapted from Molecular Cell Biology, Darnell et al)
Most visible
event
Example of the time curve:
Histone Genes: (HTT2)
ORF: YNL031C
Time course:
Histone
EBP2: YKL172W
TSM1: YCR042C
YOR263C
Why clustering make sense
biologically?
The rationale is
Rationale behind massive gene expression analysis:
Genes with high degree of
expression
similarity
related and
are likely to be functionally
may participate in common pathways.
They may be co-regulated
regulatory factors.
by
common upstream
Simply put,
Profile similarity implies functional association
Protein rarely works as a single unit
Some protein complexes
Gene profiles and correlation
• Pearson's
correlation coefficient, a simple
way of describing
the strength of linear association
between a pair of random variables, has become the most
popular measure of gene expression similarity.
•1.Cluster analysis: average linkage, self-organizing
map, K-mean, ...
2.Classification: nearest neighbor,linear discriminant
analysis, support vector machine,…
3.Dimension reduction methods: PCA ( SVD)
CC has been used by Gauss, Bravais, Edgeworth …
Sweeping impact in data analysis is due to
Galton(1822-1911)
“Typical laws of heridity in man”
Karl Pearson modifies and popularizes the use.
A building block in multivariate analysis, of which
clustering, classification, dim. reduct. are recurrent themes
As a statistician, how can you
ignore the time order ?
(Isn’t it true that the use of sample
correlation relies on the assumption
that data are I.I.D. ???)
Other methods for
Finding Gene clusters
• Bayesian clustering : normal mixture, (hidden) indicator
• PCA plot, projection pursuit, grand tour
• Multi-Dimension Scaling( bi-plot for categorical
responses, showing both cases (genes) and
variables(different clustering methods), displaying results
from many different clustering procedures)
• Generalized association plot (Chen 2001, Statistica
Sinica)
• PLAID model ( Statistica Sinica 2002, Lazzeroni, Owen)
6178
missing values
1648
complete
4530
non-compliance
compliance
4489
41
insignif icant
cycle comonents
Signif icant cyclle components
2824
1665
Smooth
714
Non-smooth
951
For the non-compliance group, visual examination of each curve pattern is done .
*** of these 41 have visible cycle patterns. l
1st PCA direction
2nd PCA direction
3rd PCA direction
Eigenvalues
Phase Assignment
Smooth
Non-smooth
G1
108
S
31
S/G2
352
G1
103
S
S/G2
27
255
90
295
M/G1
165
G2/M
239
M/G1
90
G2/M
ARG1
Glutamate
ARG2
Book a flight from LA to KEGG, JAPAN
in less than 10 seconds
ARG1
ARG1
aspart at e
8th place
negative
Glut amine CP A2
fumarat e
cit rulline
ARG3
carbamoyl
phosphat e
CP A1
arginine
ornithine
CAR1
urea
CAR2
N-acetylglutamate
Glut amat e
L-arginino- ARG4
succinat e
Y
L-glut am at e-5-semialdehyde
ARG2
P roline
X
Compute LA(X,Y|Z)
for all Z
Figu re 2. T he four genes in t he urea cycle are coded by ARG3,
ARG1, ARG4, and CAR1 in S. Cere visiae.
ARG2 enocodes acet yl-glut am at e synt hase, which cat alyzes t he first
step of ornit hine biosynthesis. CP A1 and CP A2 enocode small and
large unit s of carbamoylphosphat e synthetase. CAR2 encodes
ornithine aminot ransferase. T his chart is adapted from KEGG.
Adapted from KEGG
Rank and find
leading genes
Coverage of bioinformatics
by areas | topics
Sequence
analysis
DNA
RNA
Protein
Linkage,
pedigree
Microarray
Evolution
SNP Alternative
splicing
Functional
prediction
Pathway
discovery
Promoter
Motif
Domain
Drug
Protein-protein 3-D structure
Protein -gene
TRANSFAC
EST
System
modeling
Drug -gene protein
Coverage of Bioinformatics
by expertise (hat, not person)
Computer
Statistician/m
scientist
athematician
(raw data provider)
(huge data volume)
(Crude oil)
Oil-refining
(Noise, garbage, or ignorance?)
Make
Data cleaning
Data mining
researcher’s life
Pattern searching (Bio-information distilling/ easier (pipeline)
Biologist
/comparison
Bio-data refining)
Physical/Math/prob/stat
Data base/
models, computer
visualization
optimization
Literature
searching
Web page
browsing
Generalization
Gene Ontology /inference
Math. Modeling : a nightmare
Current
mRNA
Observed mRNA
hidden
mRNA
protein kinase
ATP, GTP, cAMP, etc
Cytoplasm
Nucleus
localization
Mitochondria
Vacuolar
DNA methylation, chromatin structure
Nutrients- carbon, nitrogen sources
Temperature
Water
Next
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Statistical
methods become
useful
Bioinformatics
(knowledge integration center)
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When
Where
Who
What
Why
Cell level
Organ level
Organism level
Species level
Ecology system level
Want to get a quick start ?
Special issue on bioinformatics
Statistica Sinica
2002 January
My paper on liquid association : PNAS 2002,
99, 16875-16880
Genome-wide co-expression dynamics: theory and
application
Classification: Biological Science, Genetics; Physical Science, Statistics
END
Cautionary Notes for
Seriation and row-column sorting
• Hierarchical clustering is popular, but
• Sharp boundaries may be artifacts due to “clever”
permutation
• how to fine-tune user-specified parameters-need some
theoretical guidance
• What is a cluster ? Criteria needed
Popular methods for
clustering/data mining
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Linkage : Eisen et al ,
Alon et al
K-mean : Tavazoein et al
Self-organizing map : Tamayo et al
SVD : Holter et al; Alter, Brown, Botstein
Can statisticians take the lead?
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Difficult
But not impossible
The key :
Willingness to learn more biology
February 2002, Talk at UCLA Biochemistry,
feedback from David Eisenberg;
March 2002, David gave an inspiring review talk
about several of his works (Nature, similarity)