Data Visualization to Enhance our Understanding of the Cancer Genome

Data Visualization to Enhance our Understanding of the Cancer Genome

HARVARD MEDICAL SCHOOL DEPARTMENT OF BIOMEDICAL INFORMATICS

NILS GEHLENBORG

@nils_gehlenborg http://gehlenborglab.org

http://gehlenborglab.org

SAMUEL GRATZL JOHANNES KEPLER UNIVERSITY LINZ

ALEXANDER LEX UNIVERSITY OF UTAH

MARC STREIT JOHANNES KEPLER UNIVERSITY LINZ

ROLE OF VISUALIZATION

experiment

DATA

INSIGHT HYPOTHESIS

interpretation

hypothesis generation

PUBLICATION

experiment

DATA

INSIGHT HYPOTHESIS

interpretation


PUBLICATION

experiment

DATA

INSIGHT HYPOTHESIS

interpretation


PRESENTATION“Storytelling”

experiment

DATA

INSIGHT HYPOTHESIS

interpretation


EXPLORATION“Pattern Discovery”

experiment

DATA

INSIGHT HYPOTHESIS

interpretation

HYPOTHESIS


EXPLORATION

HYPOTHESIS-DRIVEN DISCOVERY

“Pattern Discovery”

experiment

DATA

INSIGHT HYPOTHESIS

interpretation

DATA


EXPLORATION

DATA-DRIVEN DISCOVERY


The Cancer Genome Atlas

10,000+

genomes + clinical data + molecular data

CANCER SUBTYPES

mRNA expression microRNA expression

DNA methylation

protein expression

copy number variants mutation calls

clinical parameters

mRNA expression - clustering

C4C3C2C1

mRNA expression - clustering

C4C3C2C1

mRNA expression

copy number variants

- clustering

- gene X

C4C3C2C1

mRNA expression


DEL NORMAL AMP

- clustering

- gene X

DEL NORMAL AMP

C4C3C2C1

mRNA expression


mutation calls

- clustering

- gene X

- gene Y

DEL NORMAL AMP

C4C3C2C1

mRNA expression


mutation calls

WILDTYPEMUT

- clustering

- gene X

- gene Y

ALGORITHMIC APPROACHES

VISUALIZATION APPROACHES

unsupervised clustering of multivariate data

integrated clustering across data types (e.g., Mo et al., PNAS, 2013)

correlation testing

integrative heatmaps

network-based stratification (Hofree et al., Nat Methods, 2014)

Verhaak et al., Cancer Cell, 2010

Matrix Visualization: Publication Figure

ALGORITHMIC APPROACHES

VISUALIZATION APPROACHES

unsupervised clustering of multivariate data

integrated clustering across data types (e.g., Mo et al., PNAS, 2013)

correlation testing

integrative heatmaps

network-based stratification (Hofree et al., Nat Methods, 2014)

genome browsers (UCSC Cancer Genomics Browser, IGV)

Robinson et al., Nat Biotech, 2011

Genome-Centric Visualization: IGV

PROBLEM 1

Visualize overlap of patient sets across two or more stratifications.

PROBLEM 2

Visualize characteristics of patient sets within a stratification of interest.

A Lex, M Streit, H-J Schulz, C Partl, D Schmalstieg, PJ Park, N Gehlenborg, Comput Graph Forum, 2012 M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, PJ Park, N Gehlenborg, Nat Methods, 2014

Divide & Conquer Visualization: StratomeX

PROBLEM 1

Visualize overlap of patient sets across two or more stratifications.

PROBLEM 2

Visualize characteristics of patient sets within a stratification of interest.

PROBLEM 3

Identify relevant stratifications, pathways, and clinical variables.

Is there a mutation that overlaps with this mRNA cluster?

Is there a CNV that affects survival?

Is there a pathway that is enriched in this cluster?

Is there a mutually exclusive mutation?

Query

Stratifications Clinical Params

Pathways

GUIDED EXPLORATION

M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, PJ Park, N Gehlenborg, Nat Methods, 2014

Query

Rank

VisualizeStratifications

Clinical Params Pathways

GUIDED EXPLORATION

M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, PJ Park, N Gehlenborg, Nat Methods, 2014

AND NOW WHAT?


experiment

DATA

INSIGHT HYPOTHESIS

interpretation

DATA


EXPLORATION“Pattern Discovery”


PUBLICATION

experiment

DATA

INSIGHT HYPOTHESIS

interpretation

DATA


EXPLORATION

PRESENTATION“Storytelling”



DATA-DRIVEN COMMUNICATION

finding figure/videoAuthoringExploration Presentation




Current Model



finding figure/videoAuthoringExploration PresentationWhat we show.




What we tell.




What we did.



track provenance

annotate observations

make sense of observations

tell the story



Capture

Label

Understand

Explain

track provenance

annotate observations

make sense of observations

tell the story

C

L

U

E

CLUE

vistories

Authoring

Exploration Presentation



CLUE Model

Exploration

Authoring

Presentation

VISTORY = visual story + historyDo collaborative data analysis.

Use during peer-review.

Publish with a paper.

Embed in a presentation.



http://vistories.orgDemos and prototypes built with

We are hiring postdocs & developers!

HARVARD MEDICAL SCHOOL DEPARTMENT OF BIOMEDICAL INFORMATICS

See http://gehlenborglab.org or http://dbmi.med.harvard.edu for details.

Data visualization, analysis, and management for: • genomic structural variants • dynamics of the 3D genome • cancer subtypes in patient cohorts • exploration tools for data repositories • provenance graphs

Data Visualization to Enhance our Understanding of the Cancer Genome

Science

Transcript of Data Visualization to Enhance our Understanding of the Cancer Genome