# Statistical Methods for Analyzing

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30 Νοε 2013 (πριν από 4 χρόνια και 5 μήνες)

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Statistical Methods for Analyzing
Ordered Gene Expression
Microarray Data

Biostatistics Branch

National Inst. Environmental

Health Sciences (NIH)

Research Triangle Park, NC

An outline

Ordered gene expression data

Common experimental designs

A review of some statistical methods

An example

Demonstration of ORIOGEN

a software for ordered
gene expression data

Some examples of ordered

gene expression data

Comparison of gene expression by:

various stages of cancer

Normal
-

Hyperplasia

Carcinoma

tumor size

New tumor

Middle Size

Large tumor (with necrosis)

dose of a chemical (dose
-
response study)

duration of exposure to a chemical (time
-
course
experiments)

dose & duration

Some commonly used experimental
designs

Experimental unit: Tissues/cells/animals

Single chemical/treatment

Dose response study

Time course study

single dose but responses obtained at multiple time
points after treatment

experimental units are treated at multiple time points
using the same dose.

Dose response x Time course study

Multiple doses at multiple time points

Multi chemicals/treatments

Possible objectives

Investigate changes in gene expression at certain
biologically relevant category.

E.g. Hyperplasia to Adenoma to Carcinoma

E.g. “early time point” to “late time point” since the
exposure to a chemical

Identify/cluster genes with similar expression profiles
over time/dose.

Correlation coefficient based methods

Correlation coefficient based methods match genes with
similar
observed

patterns of expression across dose/time
points.

Gene 1

Gene 2

Correlation coefficient based methods

A number of variations to this general principle
exist in the literature. Here we outline some
prominent ones.

A.
Chu et al. (Science, 1998):

Pre
-
select a set of biologically relevant patterns of
gene expressions over time.

Identify a sample of about 3 to 8 genes for each
pattern.

Compute the correlation coefficient of each candidate
gene in the microarray data with the above pre
-
selected genes.

Cluster each candidate gene into the cluster with
highest correlation coefficient

Correlation coefficient based

methods …

B.
Kerr and Churchill (PNAS, 2001):

They correctly recognized the uncertainty associated with
Chu et al. ‘s clustering algorithm. Hence they proposed a
bootstrap methodology to evaluate Chu et al.’s clusters.

C.

Heyer et al. (Genome Research, 1999):

Rather than using the standard correlation coefficient
between genes, they employ jackknife version which
robustifies against outliers.

Unlike Chu et al.’s strategy, they classify genes on the basis
of pairwise correlation coefficients.

Correlation coefficient based

methods …

Strengths

Familiarity among biologists

Easy to compute and interpret (although it is often
misinterpreted too!)

Weakness

Non
-
linearity in the data can lead to misinterpretation

Outliers and influential observations can affect the numerical
value of the correlation coefficient.

Heterogeneity between genes can also affect the numerical value
of the correlation coefficient.

It is also important to note that correlation coefficient is
typically estimated on the basis of a very small number of
points.

Regression based procedures

Basic assumption among these methods:

The “conditions” are numerical,

e.g. dose or time

Polynomial regression

Liu et al. (BMC Bioinformatics, 2005)

For each gene Liu et al. fitted a quadratic regression model:

They cluster each gene into a particular cluster depending

upon the sign and statistical significance of the regression

parameters.

If for a gene none of the regression coefficients are

significant then such a gene is declared un
-
important.

t
g
g
g
g
t
g
t
t
Y
,
2
2
,
1
,
0
,
,

Polynomial regression

Liu et al. (BMC Bioinformatics, 2005)

Strengths:

Biologists are reasonably familiar with quadratic regression
analysis.

Regression coefficients are easy to interpret.

For small number of doses or time points and for evenly spaced
doses, a quadratic model may be a reasonable approximation.

An easy to use EXCEL based software is available.

Polynomial regression

Liu et al. (BMC Bioinformatics, 2005)

Two major limitations because it is fully parametric:

1. Departure from quadratic model is common:

In such cases the

may not be correct.

2. Normality assumption need not be valid.

Time

“Semi
-
parametric” regression methods

Several authors have tried semi
-
parametric regression

approach to gene expression data.

E.g.

deHoon et al. (Bioinformatics, 2002)

Bar
-
Joseph et al. (PNAS, 2003, Bioinformatics, 2004)

Luan and Li et al. (Bioinformatics, 2003)

Storey et al. (PNAS, 2005)

Storey et al. (2005)

Basic idea:

For each gene, they fit mixed effects model with a B
-
spline
basis. This methodology is largely based on Brumback and
Rice (JASA, 1998).

Statistical significance of each gene is evaluated using an F
like test statistic with P
-
value (q
-
value) determined by
bootstrap.

Storey et al. (2005)

Strengths:

It is semi
-
parametric

A user friendly software called EDGE is available

Limitations:

It does not perform well for “threshold” patterns of gene
expression

The “conditions” should be numerical

Unequal dose or time spacing can have an impact on the
performance of the procedure

O
rder
R
estricted
I
nference for
O
rdered
G
ene
E
xpressio
N

(ORIOGEN)

Bioinformatics
, 2003, 2005)

Bioinformation
, 2007)

Temporal Profile /Dose Response

Pattern of the
(unknown)

mean expression of a gene

over time (dose) is known as the
temporal profile

(
dose
response
) of a gene.

ORIOGEN: uses mathematical (in)equalities to describe a
profile.

)
(

Some Examples

Null profile:

6
5
4
3
2
1

Examples Continued …

Up
-
down profile with maximum at 3 hours

6
5
4
3
2
1

Examples Continued …

Non
-
increasing profile

Cyclical profile

6
5
4
3
2
1

6
5
4
3
2
1

ORIOGEN

Step 1 (Profile specification)
:

Pre
-
specify the
shapes

of profiles of interest.

Some Examples Of Pre
-
specified
Profiles

ORIOGEN …

Step 2 (profile fitting)
: Fit each pre
-
specified profile
to each gene using the estimation procedure
described in:

Ann. of Stat.
)

A Brief Description Of The Estimation
Procedure …

Definitions

Two parameters are said to be linked if
the inequality between them is known
a priori
.

Nodal parameter:

A parameter is said to be nodal if it is
linked to all parameters in the graph.

For any given profile, the estimation always starts at the
nodal parameter.

(PAVA)

Hypothesis:

Observed data

Isotonized data (PAVA)

Estimation: The General Idea

1

2

4

5

3

1

2

5

4

3

3 is the only nodal parameter

Estimation Continued …

From this sub
-
graph we estimate 1 and 2.

1

2

3

Step 3:

Determine the norm of a gene corresponding

to each temporal profile.

This is defined as the maximum (studentized) difference
between estimates corresponding to linked parameters.

Biometrics
).

A Measure of “Goodness
-
of
-
fit”
Norm

l

l
An Example

Observed data:

1, 1.5, 2, 2.5, 1.5, 2.25

Two pre
-
specified temporal profiles:

(a)

(b)

Example Continued …

Fit under profile (a)

1, 1.5, 2.25, 2.25, 1.875, 1.875

Fit under profile (b)

1, 1.5, 2, 2.5, 1.875, 1.875

Example Continued …

norm for profile (a) is:

2.25
-

1 = 1.25

norm for profile (b) is:

2.5
-

1 = 1.5

l

l
“Best Fitting” Profile

Step 4:

Identify the profile with the largest norm.

In the example, profile (b) has larger norm than profile (a) .

Hence profile (b) is a better fit than (a).

Statistical Significance

Step 5:
Statistical significance:

P
-
value

for statistical significance is obtained using the
bootstrap methodology:

Illustration …

MCF
-
7 breast cancer cell treated with
17
-
2002,
Mol. Endocrin
.).

Gene expressions were measured after:

1hr, 4hrs, 12hrs, 24hrs, 36hrs and 48hrs

of treatment.

# of genes on each chip = 1900.

# of samples at each time point = 8

Available softwares

Linear Regression Method (Liu et al., 2005)

EDGE (Storey et al., 2005)

EPIG (Chao et al., 2008)

Concluding remarks

Methodology

Freely available
software

Applicable
to ordinal
“conditions”

Repeated
measures
and
correlated
data

Model
assumptions

Linear Regression

Yes

No

No

Linear
regression

EPIG

Yes

No

?

No

EDGE

Yes

No

Yes

No

ORIOGEN

Yes

Yes

Yes

No

Some open problems

ORIOGEN is potentially subject to Type III error. How do
we control FDR & Type III error.

How to deal with

Dependent samples?

Covariates?

Order restricted inference in the context of mixed effects
linear models.

Acknowledgments

Leping Li

David Umbach

Clare Weinberg

Ed Lobenhofer

Cynthia Afshari

Software developers at
Constella Group

(late) John Zajd

Shawn Harris