1. Boolean Analysis of High-Throughput Biological Datasets
Debashis Sahoo
PhD Candidate, Electrical Engineering, Stanford University
Integrative Cancer Biology Program, Stanford University
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2. ICBP, Stanford University
Outline
Introduction
Research Contributions
StepMiner
BooleanNet
Conclusion
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3. ICBP, Stanford University
Introduction
George Boole ( )
Two values
High, Low
1, 0
Boolean operations
AND, OR, NOT
Implication
x  y
Add High/Low – maybe remove True/False
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4. ICBP, Stanford University
Molecular Biology
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5. ICBP, Stanford University
Microarrays
High throughput gene expression measurement
Gene expression for all genes are assigned a real valued number.
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6. ICBP, Stanford University
Microarray Analysis
Pearson’s correlation
Hierarchical clustering
Significance analysis of microarrays (SAM) …
This slide does not add a lot of value.
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7. ICBP, Stanford University
Research Direction
What can you learn by studying Boolean relationships between genes?
Are there any fundamental Boolean relationship on gene expressions that are conserved?
Is it possible to gain new insight into human diseases?
Can we generate hypotheses to test in a model organism?
Dele
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8. ICBP, Stanford University
Outline
Introduction
Research Contributions
StepMiner
BooleanNet
Conclusion
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9. Research Contributions
New method of analyzing timecourse microarray dataset
Developed a statistical test for fitting step functions.
Developed a tool called “StepMiner”. [Sahoo et al. NAR 2007]
StepMiner is applied to biology
Mouse osteosarcoma [Wu et al. PLoS Genetics 08]
Mouse lymphoma [Shachaf et al. Cancer Research 08]
Prostate cancer [In preparation]
New computational analysis of time courses.
Method for extracting(?) Boolean relationships.
Developed technique
- implemented a tool
- Used it to discover stuff
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10. Research Contributions
New method for analyzing large microarray dataset
Developed a statistical test for discovering Boolean relationships between pairs of genes.
Developed a tool called “BooleanNet”. [Sahoo et al. RECOMB Sat 2007]
Developed a web interface for BooleanNet
BooleanNet is applied to biology
Predicts developmentally regulated genes. [In preparation]
Predicts highly conserved regulatory relationships associated with DREAM complex. [In preparation]
New computational analysis of time courses.
Method for extracting(?) Boolean relationships.
Developed technique
- implemented a tool
- Used it to discover stuff
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11. ICBP, Stanford University
Outline
Introduction
Research Contributions
StepMiner
BooleanNet
Conclusion
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12. ICBP, Stanford University
Timecourse Analysis
Timecourse microarray data
Temporal progression of transcriptional behavior
Characteristics of the data
Voluminous, small number of time points, full of noise
Current analysis techniques
Hierarchical clustering [Eisen et al. 1998]
Significance analysis – SAM [Storey et al. 2003]
Clustering for timecourse data [Ernst et al. 2005, Tavazoie et al. 1999]
Model based [Storey et al. 2005]
Motivational slide: Time courses to see genetic response to stimulus
Problems: Too many genes
Biologists would like simple answers to most basic question: What genes change, what direction do they go, and when do they change?
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13. ICBP, Stanford University
StepMiner
Directly answer:
Which genes turn “on”/”off”?
When do they change?
Algorithm:
Fit step functions using adaptive regression
Group genes that change at the same time and direction.
Gene expression
[Sahoo et al. 07]
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14. ICBP, Stanford University
StepMiner Algorithm
Step Error
3 7.87
4 3.72
5 7.61
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15. ICBP, Stanford University
StepMiner Algorithm
Step Error
3 7.87
4 3.72
5 7.61
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16. ICBP, Stanford University
Regression Statistic
Compute the degrees of freedom
Adaptive one-step – 3
Adaptive two-step – 4
Compute F-statistic using the adjusted degrees of freedom m
F = (SSTOT – SSE)/(m – 1) * (n-m)/SSE
Compute p-values
F-distribution with (m–1, n–m) degrees of freedom.
Compute FDR
Random permutations of timepoints
Ratio of the expected to the observed significant genes
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17. ICBP, Stanford University
Outline
Introduction
Research Contributions
StepMiner
BooleanNet
Conclusion
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18. StepMiner on Mouse Osteosarcoma
Experiment overview:
Specific biological question:
What MYC target genes are responsible for tumor maintenance?
MYC
Tet-O
c-MYC
MYC ON
No Doxycycline
tTA
MYC OFF
Tet-O
c-MYC
DOX
Plus Doxycycline
tTA
Proliferation
Microarray arrays – 7 arrays
MYC is a famous oncogene that cause Human lymphoma…a type of deadly Blood cancer.
There are MYC target genes that responsible for cancer.
[The Felsher Lab, Wu et al. PLoS Genetics 08]
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19. Tumor Maintenance Genes
Hypothesis
Expressions of tumor maintenance genes follow the cell proliferation phenotype.
Time Course
(hours)
Permanently Repressed (PR)
Permanently Induced (PI)
[The Felsher Lab, Wu et al. PLoS Genetics 08]
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20. StepMiner on Mouse OsteosarcomaPI PR
MYC
Audience ought to understand here that StepMiner solves the problem of interpreting the data.
Describe colors/row/column.
[The Felsher Lab, Wu et al. PLoS Genetics 08]
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21. Tumor Maintenance Genes
MYC
[The Felsher Lab, Wu et al. PLoS Genetics 08]
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22. Permanent Changes in Ribosomal Proteins
[The Felsher Lab, Wu et al. PLoS Genetics 08]
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23. MYC, Master of Ribosome Synthesis
Sci. STKE, 8 March 2005
Vol. 2005, Issue 274, p. tw89
Grewal et al. 2005
Grandori et al. 2005
Oskarsson and Trumpp 2005
Arabi et al. 2005
Be careful about what you say. “Master regulator” may not be well defined.
Expression of dMyc is both necessary and sufficient to control rRNA synthesis and ribosome biogenesis during larval development.
Expression of c-Myc correlates with increased synthesis of rRNAs and their precursors, as indicated by labelling c-Myc-overexpressing primary human fibroblasts.
RNA interference was used to determine whether or not pre-rRNA synthesis is sensitive to the level of endogenous c-Myc. RT-PCR showed that pre-rRNA expression is decreased significantly by c-Myc depletion from HeLa cells using two different small interfering RNAs (siRNAs; Fig. 1f,g).
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24. Permanent Changes in Ribosomal Proteins
[The Felsher Lab, Wu et al. PLoS Genetics 08]
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25. ICBP, Stanford University
Outline
Introduction
Research Contributions
StepMiner
BooleanNet
Conclusion
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26. ICBP, Stanford University
Background
Analysis of large gene expression datasets
Clustering [Eisen et al. 1998]
Co-expression [Arkin and Ross 1995; Allocco et al. 2004; Day et al. 2007; Jordan et al. 2004; Lee et al. 2004; Tavazoie et al. 1999]
Bayesian analysis [Friedman et al. 2000; Lee et al. 2006; Pe'er et al. 2001; Segal et al. 2001]
Mutual information [Basso et al. 2005; Butte and Kohane 2000; Margolin et al. 2006; Wang et al ]
Problem and motivation.
Huge datasets, difficult to extract knowledge from them.
Lots of existing techniques.
Why Boolean might be useful.
Maybe start off with L-shaped scatterplot, argue that other techniques miss it.
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Limitations
Current approaches
Explore symmetric relationships only
Hard to extend across species
Do not scale to very large datasets
Not easy to interpret
[Basso et al. 2005]
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BooleanNet
Get data
GEO
[Edgar et al. 02]
Normalize
RMA
[Irizarry et al. 03]
Determine thresholds
Discover Boolean relationships
This is an overview of the Boolean analysis.
- brain image
Make it more professional.
Flow chart
Biological interpretation
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29. ICBP, Stanford University
Determine threshold
A threshold is determined for each gene.
The arrays are sorted by gene expression
StepMiner is used to determine the threshold
High
CDH expression
Intermediate
Threshold
Low
Say about linear shape.
Labels in the graph bigger.
Put forbidden zone
threshold. Labels.
Sorted arrays
[Sahoo et al. 07]
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30. Discovering Boolean Relationships
Analyze pairs of genes.
Analyze the four different quadrants.
Identify sparse quadrants.
Record the Boolean relationships.
ACPP high  GABRB1 low
GABRB1 high  ACPP low 2 4
GABRB1
If -> then
Describe x and y axis.
Describe a point.
Statistical tests for identifying sparse quadrant. 1 3
ACPP
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31. ICBP, Stanford University
Statistical Tests
Compute the expected number of points under the independence model
Compute maximum likelihood estimate of the error rate
a00
a01
a11
a10 A B
statistic =
(expected – observed)
expected √
a00
(a00+ a01)
(a00+ a10) + ( ) 1 2
error rate =
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32. Sparse Quadrant Identification
(Statistic, Error Rate)
(-0.5, 0.57)
(3.7, 0.00) 2 4
GABRB1 1 3
(0.2, 0.92)
ACPP
(-1.3, 0.51)
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33. Sparse Quadrant Identification
(Statistic > 3, Error Rate < 0.1)
(-0.5, 0.57)
(3.7, 0.00) 2 4
ACPP high  GABRB1 low
GABRB1 1 3
(0.2, 0.92)
ACPP
(-1.3, 0.51)
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34. Four Asymmetric Boolean Relationships
A low  B low
A low  B high
A high  B low
A high  B high
PTPRC low  CD19 low
XIST high  RPS4Y1 low
RPS4Y1
CD19
FAM60A low  NUAK1 high
PTPRC
COL3A1 high  SPARC high
XIST
Sparse quadrants are highlighted.
Divide the pictures:
Two slides
First show Asymmetric
Symmetric
SPARC
NUAK1
FAM60A
COL3A1
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35. Two Symmetric Boolean Relationships
Opposite
Equivalent
CCNB2
EED
Sometime they have good correlation.
But boolean relationships is strong in other cases
Divide the pictures:
Two slides
First show Asymmetric
Symmetric
BUB1B
XTP7
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36. Boolean Relationships
There are six possible Boolean relationships
A low  B low
A low  B high
A high  B low
A high  B high
Equivalent
Opposite
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37. Size of The Boolean Networks
lowhigh
highlow
lowlow
highhigh
Equivalent
Opposite
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38. Conserved Boolean Networks
Find orthologs between human, mouse and fly using EUGene database.
Search for orthologous gene pairs that have the same Boolean relationship.
Fly
17M
Human
208M
[Gilbert, 02]
Mouse
336M
41K 4M
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39. ICBP, Stanford University
Web Interface
Easy navigation and visualization of Boolean network
Search for genes
Scatterplot for a pair of genes
Retrieve all Boolean relationships for a gene
Build a subnetwork from a collection of genes
Maybe talk about size of network before this slide.
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41. ICBP, Stanford University

42. ICBP, Stanford University

43. BooleanNet Reveals Known Biology
Gender
Tissue
Development
Differentiation
XIST
ACPP
HOXD3
PTPRC
HOXA13
CD19
RPS4Y1
GABRB1
(UBX)
(Antp)
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44. ICBP, Stanford University
Outline
Introduction
Research Contributions
StepMiner
BooleanNet
Conclusion
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45. Prediction of Developmentally Regulated Genes
HSC – Stem Cell that makes blood cells
B Cell – A type of blood cell that makes antibodies
Goal is to identify genes that are expressed at the intermediate stages of development
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46. Prediction of Developmentally Regulated Genes
Boolean relationships follow known developmental relationships
KIT
Hematopoietic stem cell marker
CD19
Mature B cell marker
Boolean relationships
KIT high  CD19 low
CD19 high  KIT low
Problem statement & motivation
Inferring gene expression during development from gene expression in “other” cell types.
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47. Computational Discovery of Human B Cell Precursors
Boolean Interpolation: KIT high  A low  CD19 low
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48. Interpolation Between KIT and CD19
WASPIP
TRAF3IP3
ZC3HAV1
SEPT6
BACH2
TBC1D1
PTPRCAP
NUP153
ITGB7
CD53
CD72
ZC3H12D
ARHGAP4
LY9
ITGA4
CENTB1
LAT2
SEPT1
IL12RB1
19 Predicted genes
Published recently [Sintes et al. Tissue Antigen 2007]
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49. Experimental Validation (In Mice)
Cell sorting
HSC
MPP fl-
MPP fl+
CLP
Frac A [Pre-ProB]
Frac B [Pro-B]
Frac C [Large Pre-B]
Frac D [small Pre-B]
Frac E [Immature B] T1 T2
Mature B GC
[Deepta Bhattacharya]
qPCR on 14 genes
[Jun Seita]
Different stages of B cell development
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50. ICBP, Stanford University
qPCR Results X X
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51. ICBP, Stanford University
Analysis of qPCR Data
Determine a threshold to call “low” and “high” expression levels.
Using HSC and MPP FL- expression levels
Test for
Genes that turn “on” between “MPP FL+” and “Frac C” stages
Genes that are “on” at the mature B cell stage
12/14 genes pass this test (FDR 1%)
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52. ICBP, Stanford University
More B Cell Precursors
AICDA low
AICDA high
Boolean Interpolation: KIT high  A low  AICDA low
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53. Interpolation Between KIT and AICDA
BLK
BTLA
CCR6
CCR7
CD180
CD19
CD22
CD53
CD79A
CD80
CD84
CD86
CENTB1
CLEC2D
DENND1C
DOK3
FAIM3
FCER2
FCRLM1
FLT3LG
GCET2
GPR132
IL21R
IRF4
ITGA4
ITGB7
KIAA0674
KYNU
LAT2
LCK
LNPEP
LY9
MAP4K2
MGC10986
NCOA3
PAX5
PIK3R5
RAB8B
RAC2
RHOF
SLAMF1
SLAMF7
SP100
SP110
SPIB
SYK
TBX21
TRAF1
TREML2
WASPIP
ZC3H12D
ZNFN1A3
52 Predicted genes
Check BCL6
LMO2
BCL2 –ve control
BCL6 – BCL2 are inversely related – DLBCL hypothesis 6 genes
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54. ICBP, Stanford University

55. Analysis of Predicted Genes
Total number of genes predicted: 62
33 genes have been knocked out in mice. [Literature]
18 genes have defects in B cell function and B cell differentiation.
2 genes are known prognostic markers of B cell lymphomas: WASPIP and GCET2.
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56. ICBP, Stanford University
Conclusion
results
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57. ICBP, Stanford University
Conclusion
StepMiner reports important gene activity in timecourse microarray datasets.
BooleanNet discovers both symmetric and asymmetric relationships.
Boolean implication network is a platform for new biological hypothesis
Predicts genes related to B cell development.
Boolean implication is easy to interpret.
Write this carefully to summarize the key points.
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58. ICBP, Stanford University
Conclusion
A High B low
StepMiner
Boolean Analysis
Gene Regulation
Human Diseases
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59. ICBP, Stanford University
Future Work
Application of StepMiner and BooleanNet to other type of datasets
Improvement of the statistical tests
Threshold for BooleanNet
Prediction of genes in less well-characterized developmental pathway
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Grand Challenge
Understand gene regulatory relationships
In normal and disease processes
Build models of cellular processes
Build models of different developmental processes
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61. ICBP, Stanford University
Acknowledgements
David L. Dill
Teresa Meng
Robert Tibshirani
Andrew J. Gentles
Judy Polenta, Maggie Bos
Friends at Stanford
Friends from Undergrad
Parents, brother and sister
Sylvia K. Plevritis
Dean W. Felsher
Irving L. Weissman
The Dill Lab:
Jacob, Eric
The Sylvia Lab:
Pong, Andrew, Ray, Slava
Emily, Stephanie
The Felsher Lab:
Natalie, Cathy
Joseph Lipsick
James D. Brooks
The Fujitsu Lab:
Jawahar, Amit, Subbu
The Lipsick Lab:
Hong, Laura, Wai
The Brooks Lab:
Suvarna
The Weissman Lab:
Deepta, Jun
Same font size.
Funding: ICBP Program (NIH grant: 5U56CA )
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62. ICBP, Stanford University
The END
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63. Adaptive Regression Method
Transition edges are called “knots”
For each possible knot position
Levels of constant segments are means of the relevant measurements.
Compute SSE (sum-of-squares error).
Choose knot position that minimizes SSE.
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64. ICBP, Stanford University
Statistical Tests
Compute the expected number of points under the independence model
Compute maximum likelihood estimate of the error rate
a00
a01
a11
a10 A B
nAlow = (a00+ a01), nBlow = (a00+ a10)
total = a00+ a01+ a10+ a11, observed = a00
expected = (nAlow/ total * nBlow/ total) * total
a00
(a00+ a01)
(a00+ a10) + ( ) 1 2
error rate =
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65. Dynamic Range Consideration
Width of the intermediate region is 1 (2 fold-change)
More than 5% low or high points for each gene.
Number of points in the intermediate region is less than 2/3 of the total number of points.
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