StratomeX Visual Analysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization

Transcription

1 StratomeX Visual Analysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization Alexander Lex 1, Marc Streit 2, Hans-Jörg Schulz 3, Christian Partl 1, Dieter Schmalstieg 1, Peter J. Park 4, Nils Gehlenborg 4,5 1 Graz University of Technology, Austria 2 Johannes Kepler University Linz, Austria 3 University of Rostock, Germany 4 Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA 5 Cancer Program, Broad Institute, Cambridge, MA, USA 1

2 Cancer Subtypes Cancer Subtypes have different histology different molecular alterations Subtypes have serious implications different treatment for subtypes prognosis varies between subtypes 2

3 Cancer Subtype Analysis Modern cancer subtype analysis based on biomolecular data Our Goal: Support cancer subtype characterization through integrative visual analysis of multiple relevant datasets. 3

4 4

5 How Subtypes are Visualized Patients 5

6 Challenges Challenge 1: Visualize complex interdependencies between multiple datasets 6

7 StratomeX: Interdependencies between Stratifications of Datasets 7

8 Challenges Challenge 1: Visualize complex interdependencies between multiple datasets Challenge 2: Manage complex setup of multiple datasets, multiple stratifications and multiple views 8

9 Data View Integrator 9

10 THE DATA 10

11 TCGA Large-scale project to catalogue genetic mutations responsible for cancer 20 tumor types 500 patient samples each Extensive molecular profiling for each patient 11

12 expression TCGA Data Gene Protein sequencing data microrna expression clinical parameters mrna expression pathways methylation levels copy number status mutation status 12

13 THE TECHNIQUE 13

14 Patients Tabular Stratification Data Subtypes are identified by stratifying datasets, e.g., based on an expression pattern a mutation status a copy number alteration a combination of these Brick Various Stuff Column 14

15 Tasks T1 Evaluate whether stratifications support each other T2 Refine stratifications T3 Review effect of stratifications on clinical outcomes on pathways T4 Show expression patterns in subtypes Elicited in expert interviews and literature review 15

16 Stratification of a Single Dataset 16

17 Stratification of Multiple Datasets Cluster A1 B1 Cluster A2 B2 T1 Evaluate whether stratifications support each other Cluster A3 Tabular Data Categorical Data 17

18 VisBricks Parallel Sets [Lex, InfoVis 2011] [Kosara, InfoVis 2006] 18

19 Stratification of Multiple Datasets Cluster A1 B1 Cluster A2 B2 Cluster A3 Tabular Data Categorical Data 19

20 Stratification of Multiple Datasets Cluster A1 B1 B2.1 Cluster A2 B2.2 T2 Refine stratifications Cluster A3 Tabular Data Categorical Data 20

21 Stratification of Multiple Datasets Cluster A1 B1 Dep. C1 B2.1 Cluster A2 Dep. C2.1 B2.2 T3 Review effect of stratification Dep. C2.2 Cluster A3 Tabular Data Categorical Data Dependent Data, e.g. clinical, pathways 21

22 Column Classes T4 Show expression patterns in subtypes Table Categorical Dependent 22

23 Live Demo Glioblastoma Multiforme 23

24 Conducted with two domain experts from Broad Institute of MIT and Harvard Datasets Glioblastoma Multiforme Breast Invasive Carcinoma Report on findings Methylation subtypes Effects of clustering Effects on Pathways Case Studies 24

25 Implementation Part of Caleydo Caleydo is now open source! Release weeks ago Includes Glioblastoma dataset 25

26 ? Marc Streit Hans-Jörg Schulz Christian Partl Dieter Schmalstieg Peter J. Park Nils Gehlenborg StratomeX Visual Analysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization 26

27 Case Studies Glioblastoma Multiforme 27

28 Effects on Pathways 28

29 Effects on Pathways 29

30 Methylation Subtypes 30

31 Clustering Effects 31