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Brad Windle, Ph.D.

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... significance to the big picture or to mankind's health. ... 9 Types of Tissues/Tumors. Breast. CNS. Colon. Leukemia. Lung. Melanoma. Ovarian. Prostate ... – PowerPoint PPT presentation

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Title: Brad Windle, Ph.D.


1
Unsupervised Learning and Microarrays
Brad Windle, Ph.D. 628-1956 bwindle_at_hsc.vcu.edu
Web Site http//www.people.vcu.edu/bwindle Link
to Courses and then lecture for this class
2
Gene Expression Profiling Unsupervised
Learning Cluster Analysis and Applications
Good review of microarray data analysis
is Computational analysis of microarray
data. Quackenbush J. Nat Rev Genet 2001
Jun2(6)418-427
3
Reductionism versus Systems Approach
Why generate global analyses?
as opposed to picking a gene/protein and hoping
you get lucky and it has great significance to
the big picture or to mankinds health.
4
Normalizing Data
Northern blot
For normalizing samples, you would divide
experimental values by the mean of the values
thought to be constant through the samples
5
Sample values are typically normalized by
dividing by the mean of the reference values or
mean of all values
6
What about normalizing gene values across all the
samples?
100
10
Rationale for normalizing samples does not apply
to genes
One strategy is to subtract the mean (mean
centering).
7
Log transformation
.01 1

10 100
//
-2
0
2
8
Gene to Gene Variability
9
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10
Cluster Analysis
Goal - puts items (genes) together in clusters
based on similarity of expression across various
conditions, either similarity of absolute
expression levels or overall similarity in
pattern
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Hierarchical Clustering
18
Clustering Methods
Divisive
Agglomerative (Aggregative)
19
Cluster Linkage Methods
Nearest Neighbor or Single Linkage
Furthest Neighbor or Complete Linkage
Average Neighbors or Average Linkage
20
X Y Z
21
K-Means Clustering and its relative
Self-Organizing Maps (SOM)
22
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23
Ranking Order Clustering
24
Cluster Playground 3
25
Applications of Gene Expression Profiling
and Cluster Analysis
Tissue or Tumor Classification
Gene Classification
Drug Classification
Drug Target Identification
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27
B-Cell Lymphoma NATURE 403, 503-511, 2000
Indistinguishable by histology
Yet half responded well to therapy and half did
not
Where there differences in gene expression that
correlate with drug response?
Gene expression profiles showed half the
lymphomas were of GC B-Cell lineage and the other
of Activated B-Cell lineage
A subset of genes predicts therapeutic outcome
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31
Gene Expression Profiling of Yeast Mutants and
Drugs Cell 102, 109126, 2000
Mutants
Drugs
M1
M3
M4
M5
M6
M2
D1
D2
D3
D4
D5
D6
M7
M8
M9
M10
M11
M12
D7
D8
D9
D100
D11
D12
M15
M17
M13
M14
M16
M18
D13
D14
D15
D16
D17
D18
32
Validation of cdc28 Kinase Target
Inhibition SCIENCE 281, 533-538, 1998
Nucleotide analogs that block cdc28p
D1 and D2
cdc28-

Cdc28-regulated genes
D1
D2
33
COMPARE Clustering Drugs Based on Cell Line
Sensitivities Nature Genetics 24 236-244, 2000
Cells
A B C D E
Drug
1
-2 -1 0 -1 .01
2 3 4 5
1 -1.5 2 0 -.5
.4 0 1 1 .2
0 .7 2 1 .9
1 0 -.5 .5 -.8
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35
Profiling
36
Clustering NCI 60 Cancer Cell Lines Nature
Genetics 24 227-238
6165 Genes
37
Filtering Data
Filter out data with the program Cluster, based
on SD cuts
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