Download Expression over samples in controls and cases

Subspace Differential Coexpression Analysis
for the Discovery of Disease-related Dysregulations
Gang Fang, Rui Kuang, Gaurav Pandey, Michael Steinbach,
Chad L. Myers and Vipin Kumar
[email protected]
http://www-users.cs.umn.edu/~kumar/dmbio/
Department of Computer Science and Engineering
RECOMB Systems Biology 12/05/2009
Differential Expression (DE)
• Differential Expression (DE)
– Traditional analysis targets the changes of
expression level
controls
cases
cases
genes
Expression level
controls
[Kostka & Spang, 2005]
Expression over samples in controls and cases
[Golub et al., 1999], [Pan 2002], [Cui and Churchill, 2003] etc.
Differential Coexpression (DC)
• Differential Coexpression (DC)
– Targets changes of the coherence of expression
cases
genes
controls
cases interesting,
Question:
Is this gene
i.e. associated w/ the phenotype?
controls
Answer: No, in term of differential
expression (DE).
However, what if there are
another two genes ……?
Matrix of expression values
Yes!& Spang, 2005]
[Kostka
Expression over samples
in controls and cases
Biological interpretations of DC:
Dysregulation of pathways, mutation of transcriptional factors, etc.
[Silva et al., 1995], [Li, 2002], [Kostka & Spang, 2005], [Rosemary et al., 2008], [Cho et al. 2009] etc.
Differential Coexpression (DC)
• Existing work on differential coexpression
– Pairs of genes with differential coexpression
• [Silva et al., 1995], [Li, 2002], [Li et al., 2003], [Lai et al. 2004]
– Clustering based differential coexpression analysis
• [Ihmels et al., 2005], [Watson., 2006]
– Network based analysis of differential coexpression
• [Zhang and Horvath, 2005], [Choi et al., 2005], [Gargalovic et al. 2006],
[Oldham et al. 2006], [Fuller et al., 2007], [Xu et al., 2008]
– Beyond pair-wise (size-k) differential coexpression
• [Kostka and Spang., 2004], [Prieto et al., 2006]
– Gene-pathway differential coexpression
• [Rosemary et al., 2008]
– Pathway-pathway differential coexpression
• [Cho et al., 2009]
Existing DC work is “full-space”
• Full-space differential coexpression
Full-space measures: e.g.
correlation difference
• May have limitations due to the heterogeneity of
– Causes of a disease (e.g. genetic difference)
– Populations affected (e.g. demographic difference)
Motivation:
Such subspace patterns
may be missed by fullspace models
Extension to Subspace Differential Coexpression
• Definition of Subspace Differential Coexpression Pattern
– A set of k genes
–
–
= {g1, g2 ,…, gk}
: Fraction of samples in class A, on which the k genes are coexpressed
: Fraction of samples in class B, on which the k genes are coexpressed
Given n genes, there are 2n
candidates of SDC pattern!
How to effectively handle the
combinatorial search space?
Similar motivation and challenge
as biclustering, but here
differetial biclustering !
as a measure of subspace differential coexpression
Problem: given n genes, find all the subsets of genes, s.t. SDC≥d
Details in [Fang, Kuang, Pandey, Steinbach, Myers and Kumar, PSB 2010]
Direct Mining of Differential Patterns
Refined SDC measure: “direct”
>>
A measure M is antimonotonic
if V A,B: A
B  M(A) >= M(B)
≈
Details in [Fang, Kuang, Pandey, Steinbach, Myers and Kumar, PSB 2010]
[Fang, Pandey, Gupta, Steinbach and Kumar, TR 09-011, CS@UMN]
An Association-analysis Approach
systematic and efficient combinatorial search
Refined SDC measure
null
Disqualified
A
B
C
D
E
A measure M is antimonotonic if
V A,B: A
B  M(A) >= M(B)
AB
AC
AD
AE
BC
BD
BE
CD
CE
DE
ABC
ABD
ABE
ACD
ACE
ADE
BCD
BCE
BDE
CDE
ABCD
ABCE
ABDE
ACDE
ABCDE
Prune all the
supersets
[ Agrawal et al. 1994]
BCDE
Advantages:
1) Systematic & direct
2) Completeness
3) Efficiency
Validation
• Three lung cancer datasets
– [Bhattacharjee et al. 2001], [Stearman et al. 2005], [Su et al. 2007]
• All are from Affymetrix microarrays (first two: HG-U95A, and the
third: HG-U133A)
– Lung cancer samples & normal samples
• Combined dataset
–
–
–
–
More samples
Proper normalizations before combining: (RMA, DWD, XPN)
Lung cancer samples (102)
normal samples (67)
RMA [Irizarry et al., 2003], DWD [Benito et al., 2004], XPN [Shabalin et al., 2008]
Statistical Significance
Phenotype permutation test (n=1000 )
C
B
A
Could Subspace DC patterns have been
discovered in full-space?
Subspace DC measures
88 statistically significant size-3 patterns (stars)
Can NOT be
found in full-space
Can also be found
in full-space
Phenotype permutation
based significant cutoff for
the full-space measure
Full-space DC measures
DC (Differential Coexpression)
A 10-gene Subspace DC Pattern
Enriched with the TNF-α/NFkB signaling pathway
(6/10 overlap with the pathway, P-value: 1.4*10-5)
≈ 10%
Suggests that the dysregulation of TNF-α/NFkB
pathway may be related to lung cancer≈ 60%
www. ingenuity.com: enriched Ingenuity subnetwork
Biological Interpretations
• Specific interpretation
– Enriched cancer-related signaling pathways
• TNF-α/NFkB
• WNT
– Target gene sets of cancer-related microRNA & TFs
• microRNA:
– miR-101 ({PIK3C2B,TSC22D1} + AKAP12)
miR-101 is shown down-regulated in cancer
[Friedman et al 2009]
• Transcriptional factor (TF):
– ATF2 ({ETV4,PTHLH} + CBX5)
Mutations of ATF2 are shown to be related to cancer
[Woo et al. 2002]
Summary & Future Directions
• Summary
– Proposed the problem definition & a systematic approach for subspace DC
– Subspace DC analysis can identify many statistically significant &
biologically relevant patterns that would have been missed in full-space
• Potential Biomedical utility
– Study the demographic and genetic difference within each class
Compare
Compare
– Phenotype classification with subspace DC patterns
• Combine DE and Subspace DC patterns
DE (Differential Expression);
DC (Differential Coexpression)
Acknowledgement
• Co-authors at Dept. Computer Science, Univ. of Minnesota
Data Mining for Biomedical
Informatics Group
Gaurav
Pandey
Michael
Steinbach
Vipin
Kumar
• Conference organizers
Comp. Bio.
Group
Comp. Bio. & Func.
Genomic Group
Rui
Kuang
Chad
Myers
NSF grants
#CRI-0551551
#IIS-0308264
#ITR-0325949
UMR-IBM-Mayo BICB Fellowship
Thanks!
• Paper
– Gang Fang, Rui Kuang, Gaurav Pandey, Michael Steinbach,
Chad L. Myers and Vipin Kumar,
Subspace Differential Coexpression Analysis: Problem Definition
and a General Approach
Proceedings of 15th Pacific Symposium on Biocomputing, 2010
• Source codes: http://vk.cs.umn.edu/SDC
• Questions:
– Gang Fang: [email protected]