Transcript lecture1-287 - UCLA Statistics

Study of Gene Expression:
Statistics, Biology, and
Microarrays
Ker-Chau Li
Statistics Department
UCLA
[email protected]
PART I. Cellular Biology
Macromolecules: DNA, mRNA,
protein
Why Biology?
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
• Gene number, exact locations, and functions
• Gene regulation
• DNA sequence organization
• Chromosomal structure and organization
• Noncoding DNA types, amount, distribution, information content, and functions
• Coordination of gene expression, protein synthesis, and post-translational events
• Interaction of proteins in complex molecular machines
• Predicted vs experimentally determined gene function
• 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
Molecular Medicine
• improved diagnosis of disease
• earlier detection of genetic predispositions to disease
• rational drug design
• gene therapy and control systems for drugs
• pharmacogenomics "custom drugs"
Microbial Genomics
• rapid detection and treatment of pathogens (disease-causing microbes) in medicine
• new energy sources (biofuels)
• environmental monitoring to detect pollutants
• protection from biological and chemical warfare
• safe, efficient toxic waste cleanup
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
Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001
What is a gene ?
SNP and Genetic Disease
Mitochondrial ATP Synthase E. coli ATP Synthase
These images depicting models of ATP Synthase subunit
structure were provided by John Walker. Some equivalent
subunits from different organisms have different names.
PART II. Microarray
Genome-wide expression profiling
Differential Gene expression:
tissues, organs
Next Step in Genomics
• Transcriptomics involves large-scale analysis of messenger RNAs (molecules that
are transcribed from active genes) to follow when, where, and under what conditions
genes are expressed.
• Proteomics—the study of protein expression and function—can bring researchers
closer than gene expression studies to what’s actually happening in the cell.
• Structural genomics initiatives are being launched worldwide to generate the 3-D
structures of one or more proteins from each protein family, thus offering clues to
function and biological targets for drug design.
• Knockout studies are one experimental method for understanding the function of
DNA sequences and the proteins they encode. Researchers inactivate genes in living
organisms and monitor any changes that could reveal the function of specific genes.
• Comparative genomics—analyzing DNA sequence patterns of humans and
well-studied model organisms side-by-side—has become one of the most powerful
strategies for identifying human genes and interpreting their function.
Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001
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
– Mutation/Knockout Response
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
Exploring the Metabolic and Genetic Control of
Gene Expression on a Genomic Scale
Joseph L. DeRisi, Vishwanath R. Iyer, Patrick O. Brown*
Time Course:
Expression level
1
0
Time
Change of Condition
Or:
A
B
C
D
E
A
--
2.1
0.8
1.3
0.5
B
0.2
--
-0.5
2.3
0.22
…
-1.2
--
0.3
-1.1
…..
PART III. Statistics
Low-level analysis
Comparative expression
Feature extraction
Classification,clustering
Pearson correlation
Liquid association
Image analysis
• 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
Example 1
Comparative expression
Normal versus cancer cells
ALL versus AML
E.Lander’s group at MIT
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•
•
•
•
•
•
Cancer classification (leukemia)
ALL; AML (arising from lymphoid or myeloid precursors)
Require different treatments
Traditional methods ; nuclear morphology;
Enzyme-based histochemical analysis(1960)
Antibodies (1970)
Genome wide expression comparision
ALL (acute lymphoblastic
leukemia)
AML(acute myeloid
leukemia)
Gene selection
•
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.
• Works ????
• 34 new leukemia samples
• 29 are predicated with 100% accuracy; 5 weak predication
cases