Talk presented at the Simons Foundation Biotech Symposium "Complex Data Visualization: Approach and Application" (12 September 2014) http://www.simonsfoundation.org/event/complex-data-visualization-approach-and-application/ In this talk I describe how we integrated a sophisticated computational framework directly into the StratomeX visualization technique to enable rapid exploration of tens of thousands of stratifications in cancer genomics data, creating a unique and powerful tool for the identification and characterization of tumor subtypes. The tool can handle a wide range of genomic and clinical data types for cohorts with hundreds of patients. StratomeX also provides direct access to comprehensive data sets generated by The Cancer Genome Atlas Firehose analysis pipeline. http://stratomex.caleydo.org

1. Visual Exploration of
Clinical and Genomic Data for
Patient Stratification
NILS GEHLENBORG
!
@nils_gehlenborg・http://www.gehlenborg.com
Broad Institute of MIT and Harvard
Cancer Program
Harvard Medical School
Center for Biomedical Informatics

2. Team
Alexander Lex Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA
Marc Streit Johannes Kepler University, Linz, Austria
Christian Partl Graz University of Technology, Graz, Austria
Sam Gratzl Johannes Kepler University, Linz, Austria
Dieter Schmalstieg Graz University of Technology, Graz, Austria
!
Hanspeter Pfister Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA
Peter J Park Harvard Medical School, Boston, MA, USA
!
Nils Gehlenborg Harvard Medical School, Boston, MA, USA & Broad Institute, Cambridge, MA
!!
!
!
Special thanks to
Broad Institute TCGA Genome Data Analysis Center Team
in particular Michael S Noble, Lynda Chin & Gaddy Getz

3. Funding
Peter J Park NIH/NCI The Cancer Genome Atlas
!
Nils Gehlenborg NIH/NHGRI K99/R00 Pathway to Independence Award
!!

4. ?

10. TCGA
The Cancer Genome Atlas

11. 20+ cancer types
×
500 patients

12. 10,000+ patients

14. mRNA expression
microRNA expression
DNA methylation
protein expression
copy number variants
mutation calls
clinical parameters

16. Stratome

17. Anthony92931 / Wikimedia Commons

20. Correlation with clusters based on other data types?
Different outcomes?
Mutations or copy number variants associated with clusters?
Demographic differences?

21. Challenges
How can we explore overlap of patient sets across stratifications?
How can we compare properties of patient sets within a stratification?
How can we discover “interesting” stratifications and pathways to consider
How can we handle terabytes of clinical and genomic data in visualization tools?

22. Problem 1
!
Comparing Patient Sets
across Stratifications

23. Patients
Stratifications

24. mRNA Copy Number
gene X
Mutation
gene Y
del
normal
amp
mut
normal
#1
#2
#3
#4

25. mRNA Copy Number
gene X
Mutation
gene Y
del
normal
amp
mut
normal
#1
#2
#3
#4

26. mRNA Copy Number
gene X
Mutation
gene Y
del
normal
amp
mut
normal
#1
#2
#3
#4

27. mRNA Copy Number
gene X
Mutation
gene Y
del
normal
amp
mut
normal
#1
#2
#3
#4

28. StratomeX
(short for Stratome Explorer)

29. mRNA Copy Number Mutation
del
normal
amp
mut
normal
#1
#2
#3
#4

31. Select band

32. Select block

33. Compare clusterings: consensus NMF and hierarchical

34. Park columns

35. Compare clusterings: left cluster split

36. Compare clusterings: right cluster split

37. Compare clusterings: left cluster contained in right cluster

38. Problem 2
!
Comparing Patient Sets
within Stratifications

40. Block Visualizations: Patient Properties
Numerical Data
Matrix
Vector
Matrix + (Pathway) Maps
Categorical Data
Scalar

41. Add KEGG glioma pathway and map mRNA transcript levels

42. Modify color mapping on the fly

43. View pathway detail (cluster 2)

44. Zoom into pathway detail (cluster 2): EGFR down-regulated

46. View pathway detail (cluster 3)

47. Zoom into pathway detail (cluster 3): EGFR up-regulated

49. Add copy number for EGFR

50. Add copy number for EGFR

51. Add survival stratified by TP53 mutation status

52. View detail of Kaplan-Meier plot based on TP53

54. ?

55. Knowledge-driven Exploration
Data-driven Exploration

56. Problem 3
!
Finding “Interesting”
Stratifications and Pathways

58. Is there a mutation that overlaps with this mRNA cluster?
Is there a mutually exclusive mutation?
Is there a CNV that affects survival?
Is there a pathway that is enriched in this cluster?
Query
Stratifications
Clinical Params
Pathways

59. Guided
Exploration
Query
Retrieve
Visualize
Stratifications
Clinical Params
Pathways

61. LineUp
S Gratzl, A Lex, N Gehlenborg, H Pfister and M Streit, “LineUp: Visual Analysis of Multi-Attribute
Rankings“, IEEE Transactions on Visualization and Computer Graphics 19:2277-2286 (2013)

62. Example: Clear Cell Renal Carcinoma (KIRC)
Main TCGA Paper published in Nature in 2013
!
First goal here: Characterize mRNA clusters

63. View TCGA mRNA subtypes

64. Add MutSig q-values for mutations

65. Invert q-value mapping

66. Add filter to inverted q-value as cut-off

68. Query mutated genes

69. Queries
Retrieve Stratifications
Sets with large overlap: Jaccard Index
Similar stratifications: Adjusted Rand Index
Survival: Log Rank Score (one vs rest)
Retrieve Pathways
Gene Set Enrichtment Score: original or PAGE (one vs rest)

70. Query mutated genes

71. Result of Jaccard Index query: preview PTEN

72. Query mutated genes

73. Query mutated genes

74. Query mutated genes with cluster m2

75. Result of Jaccard Index query: preview MTOR

77. Re-order columns

78. Add TCGA microRNA subtypes (direct insert mode)

79. Add TCGA microRNA subtypes (direct insert mode)

80. Observe large overlap between m1 and mi3

81. Observe large overlap between m3 and mi2

82. Query for copy number variation matching m3

83. Query only tumor suppressor genes (Vogelstein et al.)

84. Query only tumor suppressor genes (Vogelstein et al.)

85. Score only deletions

86. Score only deletions

87. Score only deletions

88. Score only deletions

89. Score only deletions

90. View CDKN2A copy number status and m3 and mi2 overlap

91. Add survival stratified by TCGA microRNA clusters

92. Find gene mutation that affects survival

93. Score only mutations

94. Score only mutations

95. Score only mutations

96. Score only mutations

97. View BAP1 mutation status and survival stratified by BAP1

98. View BAP1 mutation status and survival stratified by BAP1

99. View BAP1 mutation status and survival stratified by BAP1

100. Query for enriched pathway in TCGA mRNA cluster m4

101. Preview KEGG ribosome pathway overexpression in m4

102. Confirm selection

103. Change color mapping

104. View ribosome pathway detail for TCGA mRNA cluster m4

105. ?

106. Problem 4
!
Dealing with Terabytes of
Cancer Genomics Data

107. TCGA
Data Coordination Center
Broad Institute
Genome Data Analysis Center
Standardized Data Sets
Standardized Analyses
Analysis Reports
MSKCC cBio Portal
TCGA Working Groups
StratomeX
...

108. Standardized Data Sets Standardized Analyses Analysis Reports
Data set versioning
Format normalization
Removal of redacted data
. . .
Mutation Analysis
Copy Number Analysis
Clustering
Correlations
Pathway Analysis
. . .

109. 102
Standardized Data Sets Standardized Analyses Analysis Reports
http://gdac.broadinstitute.org
individual downloads and view reports
firehose_get
bulk download

110. 102
Standardized Data Sets Standardized Analyses Analysis Reports
http://gdac.broadinstitute.org
individual downloads and view reports
firehose_get
bulk download

111. Standardized Data Sets Standardized Analyses Analysis Reports
+ = one per
Data Matrices Stratifications
mRNA (array & sequencing)
microRNA (array & sequencing)
methylation
reverse phase protein array
clinical parameters
clustering (CNMF & hierarchical)
gene mutation status (binary)
gene copy number status (5 class)
Data Package
tumor type

114. up to 24 data and result files
from 18 Firehose archives
up to 500 MB (190 MB compressed)
Data Packages

115. Schroeder et al. Genome Medicine 2013, 5:9

116. Challenges
How can we explore overlap of patient sets across stratifications?
How can we compare properties of patient sets within a stratification?
How can we discover “interesting” stratifications and pathways to consider
How can we handle terabytes of clinical and genomic data in visualization tools?

117. CALEYDO
StratomeX is part of the Caleydo Visualization Framework
Implemented in Java, uses OpenGL and
Eclipse Rich Client Platform
Binaries available for Linux, Windows, Mac OS X
Requires Java 1.7 JRE or JDK (on Mac OS X)
Open source licensed under BSD license
Source code on GitHub

118. CALEYDO
StratomeX
http://stratomex.caleydo.org
http://www.github.com/caleydo
A Lex, M Streit, H-J Schulz, C Partl, D Schmalstieg, PJ Park, N
Gehlenborg, “StratomeX: Visual Analysis of Large-Scale Heterogeneous
Genomics Data for Cancer Subtype Characterization”, Computer Graphics
Forum (EuroVis '12), 31:1175-1184 (2012)
M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, PJ Park, N
Gehlenborg, “Guided Visual Exploration of Genomic Stratifications in
Cancer”, Nature Methods 11:884–885 (2014)

119. Plans
!
Where to go from here?

120. Domino
S Gratzl, N Gehlenborg, A Lex, H Pfister and M Streit, “Domino: Extracting, Comparing, and
Manipulating Subsets across Multiple Tabular Datasets“, IEEE Transactions on Visualization and Computer
Graphics (2014)

122. INTEGRATION

123. INTEGRATION INTEGRATION

124. INTEGRATION
Horizontal Integration across Data Types
Biological Insight

125. Vertical Integration across Data Levels
Confirmation &
Troubleshooting
INTEGRATION

128. Refinery Platform
!
! |

129. Refinery Platform
!
! |
Data repository based on ISA-Tab for reproducible research
Workflow execution in Galaxy
Integrated visualization tools with access to provenance
http://www.refinery-platform.org

130. CALEYDO
StratomeX
http://stratomex.caleydo.org
http://www.github.com/caleydo
A Lex, M Streit, H-J Schulz, C Partl, D Schmalstieg, PJ Park, N
Gehlenborg, “StratomeX: Visual Analysis of Large-Scale Heterogeneous
Genomics Data for Cancer Subtype Characterization”, Computer Graphics
Forum (EuroVis '12), 31:1175-1184 (2012)
M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, PJ Park, N
Gehlenborg, “Guided Visual Exploration of Genomic Stratifications in
Cancer”, Nature Methods 11:884–885 (2014)

131. Execute Logrank
Test query
Select displayed
set
Execute Jaccard
Index query
Select displayed
Z[YH[PÄJH[PVU
Execute Adjusted
Rand Index query
6WLU8LY`PaHYK 6WLU8LY`PaHYK
Select pathway
Select displayed
set
Add other data
Execute GSEA
query
Select displayed
Z[YH[PÄJH[PVU
Select displayed
Z[YH[PÄJH[PVU
Select clinical param.
in LineUp view
Manually
Execute Logrank
Test query
Execute PAGE
query
:LSLJ[Z[YH[PÄJH[PVU :LSLJ[Z[YH[PÄJH[PVU :LSLJ[Z[YH[PÄJH[PVU :LSLJ[Z[YH[PÄJH[PVU :LSLJ[Z[YH[PÄJH[PVU
in LineUp view
Select pathway Select pathway Select pathway Select clinical param.
in LineUp view
(KKZ[YH[PÄJH[PVU
Based on Logrank
Test score (survival)
Based on similarity to
KPZWSH`LKZ[YH[PÄJH[PVU
Based on overlap
with displayed set
Add pathway
Stratify with displayed
Z[YH[PÄJH[PVU
Find based on differential
expression in displayed set
Stratify with displayed
Z[YH[PÄJH[PVU
Display
UZ[YH[PÄLK
Add pathway
Based on Logrank
Test score (survival)
Add other data
Add independent
column
Add dependent
column
Add independent
column to existing one
Manually
Based on GSEA Based on PAGE
6WLU8LY`PaHYK
Select clinical param.
in LineUp view
in LineUp view in LineUp view in LineUp view in LineUp view in LineUp view in LineUp view in LineUp view in LineUp view