Download Genomics: Understanding the Blueprint of Life

Genomics: Understanding the
Blueprint of Life
Sujay Datta
Statistical Center for HIV/AIDS Research & Prevention
(Vaccines & Infectious Diseases Institute)
Fred Hutchinson Cancer Research Center
Gregor Mendel
Genes: The Unit of
Inheritance
Figure 10.1
The Structure of DNA
The person who made a silent
contribution to this great discovery
The DNA Double Helix:
The Double Helix
Figure 10.10a & b
Watson-Crick Base Pairs
Figure 10.10a & b
The DNA Chains Are AntiParallel
“It has not escaped our notice that the specific pairing we have
postulated immediately suggests a possible copying mechanism
for the genetic material.” Watson and Crick, Nature 171, 737738 (1953)
Central Dogma of Molecular Biology
A single aa change in hemoglobin
causes sickle cell anemia
Levels of chromosomal
organization
A busy person reading a very, very
long newspaper column!
Information Flow in the Cell
Figure 11.2
Transcription of DNA to RNA
Genes can be expressed at different
levels in different tissues or organisms
Intervening Sequences
Figure 11.23
RNA splicing removes introns from the
pre-mRNA and converts it to mRNA
Figure 11.23
Translation of spliced mRNAs to
proteins occurs at the ribosomes
The Genetic Code
Figure 11.41
Measuring gene expression
• So the expression levels of genes in different tissues,
organs or individuals can be measured by quantifying
the amounts of mRNAs they produce. The totality of
all mRNAs produced from an organism’s genome is its
transcriptome
• Expression levels of genes in different tissues, organs
or individuals can also be measured by measuring the
amounts of proteins they code for (the totality of all
proteins coded for by an organism’s genome is its
proteome)
• The 1st method is a bit indirect but more manageable,
as the proteome is much larger than the transcriptome
Microarrays: A revolution in gene
expression profiling
• Until the 1990s, could only measure
the expression levels of a few genes
at a time---at great expenses
• In came microarrays that, for the
first time, enabled us to measure the
expression of thousands of genes at
once (the current versions of them
can handle entire genomes)
• Types of microarrays: Short oligonucleotide (Affymetrix), long oligonucleotide (Agilent), nylon bead
arrays (Illumina), cDNA arrays
• Other technologies: SAGE, MPSS
Source: Affymetrix website
Raw Microarray Image
Custom software: getting representative value of a probe cell
Microarray data: Promises & Problems
• Data: log_2 (fluorescence intensity)
• Which genes are significantly
differentially expressed between 2
individuals or conditions (at a
particular time-instant)?
• Which genes show significant
changes in expression over time?
• Which genes have expression
levels that are correlated with some
external variable?
• For a given pathway, which of the
genes in our collection are most
likely to be involved?
• For a diffuse disease, which genes
are associated with different
outcomes?
Problems: Many sources
of noise and variation
• Spatial artifacts due to
manufacturing defects
• Contamination by RNA
and other substances
from unwanted sources
• Batch-to-batch variation
• Experimenter-to-experimenter variation
• Day-to-day variation
Problem: Cost. They have
become cheaper over
the years. Still difficult to
afford > 250-300
Steps to extract meaningful
information from microarrays
– preprocessing of the
images
– normalization of the data
from the images
– Probe-level modeling to
extract expression level
data
– gene-filtering
– clustering
– relating to biologic data
from other sources such
as a pathway database or
an annotation database
• Need replicates of each
gene-individual combination
to ensure a good estimate of
the random error (i.e., the
variation that still remains
after getting rid of all known
sources of variation)
• Need good models to take
into account all systematic
sources of variation
• Avoid confounding between
the different experimental
conditions (treatment/control
or cancer/normal) with the
systematic sources of var.
Steps in image
analysis
1. Addressing. Estimate
location of spot centers.
2. Segmentation. Classify pixels
as foreground (signal) or
background.
3. Information extraction. For
each spot on the array and each
dye
• signal intensities;
• background intensities;
• quality measures.
Segmentation
Adaptive segmentation, SRG
Fixed circle segmentation
Spots usually vary in size and shape.
Normalization is needed to minimize nonbiological variation between arrays. It basically
means an ‘intensity alignment’ between arrays
Probe-level model for the normalized
and background-corrected data
yijkl    i   j   k  *  ij  [   ik     jk   ijkl
• The LHS is the normalized log expression value
for the i-th probe, j-th array, k-th dye (in the
case of a 2-color array) and l-th replicate.
• The fit from this model is the final summary output of the preprocessing steps. It is a huge
matrix and is called an expression-set
Finding “interesting” genes
• Now, for each array, we have obtained expressionlevel data as columns of a huge matrix (expression-set)
• The next step is to select those genes that have
“interesting” expression levels.
• “Interesting” is interpreted in many different ways:
– high levels of expression in a subgroup of interest
– lack of expression in a subgroup of interest
– pattern of expression that correlates well with
experimental conditions or certain covariates
– pattern of expression that correlates well with time
Methods to detect “interesting” genes
1. Fold-changes – ratio of expression levels between
two groups (biologists are most comfortable with it)
2. t-tests – now statistical variation comes in to play
3. Other statistical models: ANOVA, Cox Model, etc.
4. For large enough samples we can tailor the test to
the distribution (which may be different in the 2
groups)
5. Whenever inference is being drawn about many,
many genes simultaneously, a multiplicity
adjustment to the p-values must be made
Why need multiplicity-adjusted tests?
• Let A,B and C be events with P(A)=a, P(B)=b, P(C)=
c. Then P(A* and B* and C*) >= 1-[P(A)+P(B)+P(C)],
where A*, B* and C* are the complements of A,B,C
• If we are testing 3 hypotheses, each at level .05, let
A={type-I error is made in hypo. #1}, B={type-I error
made in hypo. # 2}, C={type I error made in hypo# 3}
• Then all we can say is: P(All 3 decisions are correct)
is at least 1-(.05+.05+.05) or .85 (i.e., the chances of
at least one type-I error can be as high as 15%)
• Now imagine testing 5000 or 10000 hypotheses!
• That’s why the cut-off values for the tests should be
adjusted so that P(1 or more type-I error) <= .05
Filtering genes
• A filter is a mechanism for removing a gene from
further consideration
• Want to reduce the number of genes under
consideration so that we can concentrate on those
that are more interesting (it is a waste of resources
to study genes that are not likely to be of interest)
Non-specific filtering:
• at least k (or a proportion p) of the samples must
have expression values larger than some specified
amount, A.
• the gene should show sufficient variation to be
interesting:
– either a gap of size A in the central portion of the data
– or a interquartile range of at least B
Gene-set enrichment analysis
• Suppose you have a short-list of genes that have
significant p-values from a statistical test (say, for
differential expression or temporal change in profile)
• Now you want to see if a given set of genes (that are
known to be of interest to biologists or belong to a
crucial pathway, etc.) is over-represented in this list
• Ex: In a list of ~ 5000 significantly differentially expressed genes between acutely HIV-infected and
healthy individuals, are the NK-cell KIR genes overrepresented (compared to those not diff. exp.)?
• 2X2 contingency table (diff. exp. or not, NK-cell KIR or not)
• Fisher’s exact test (or hypergeometric test)