Guided visual exploration of patient stratifications in cancer genomics

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Talk presented at the "Beyond the Genome 2014: Cancer Genomics" conference (10 October 2014) http://www.beyond-the-genome.com/2014/ Cancer is a heterogeneous disease, and molecular profiling of tumors from large cohorts has enabled characterization of new tumor subtypes. This is a prerequisite for improving personalized treatment and ultimately better patient outcomes. Potential tumor subtypes can be identified with methods such as unsupervised clustering or network-based stratification, which assign patients to sets based on high-dimensional molecular profiles. Detailed characterization of identified sets and their interpretation, however, remain a time-consuming exploratory process. To address these challenges, we have developed StratomeX (http://stratomex.caleydo.org), an interactive visualization tool that complements algorithmic approaches. StratomeX also integrates a computational framework for query-based guided exploration directly into the visualization, enabling discovery of novel relationships between patient sets and efficient generation and refinement of hypotheses about tumor subtypes. StratomeX enables analysts to efficiently compare multiple patient stratifications, to correlate patient sets with clinical information or genomic alterations, and to view the differences between molecular profiles across patient sets.

Science

Guided visual exploration
of patient stratiﬁcations in
cancer genomics
Nils Gehlenborg
Harvard Medical School 
Center for Biomedical Informatics
nils_gehlenborg

INTERPRETATION
GENERATIONCOMPUTATION
Machine
Human
Data

“In every chain of reasoning, the evidence of the last conclusion can
be no greater than that of the weakest link of the chain, whatever
may be the strength of the rest.”
- Thomas Reid, Essays on the Intellectual Powers of Man (1786)

INTERPRETATION
GENERATIONCOMPUTATION
Machine
Human
Data
Hypotheses
Discoveries
Knowledge
Cognition

x y1 y2
1.00 0.96 0.76
2.00 0.76 -0.14
3.00 -0.14 -0.91
4.00 -0.91 -0.84
5.00 -0.84 0.00
6.00 0.00 0.84
7.00 0.84 0.91
8.00 0.91 0.14
9.00 0.14 -0.76
10.00 -0.76 -0.96
11.00 -0.96 -0.28

INTERPRETATION
GENERATIONCOMPUTATION
Machine
Human
Hypotheses
Discoveries
Knowledge
Data
Cognition

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

StratomeX
A Lex, M Streit, H-J Schulz, C Partl, D Schmalstieg, PJ Park, N Gehlenborg, “StratomeX: Visual Anal-
ysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization“, Computer
Graphics Forum 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)

Comparing Patient Sets
across StratiﬁcationsPROBLEM 1

Comparing Patient Sets
within StratiﬁcationsPROBLEM 2

Finding Relevant
Stratiﬁcations and PathwaysPROBLEM 3

Knowledge-driven Exploration
Data-driven Exploration

Query
Rank
Visualize
Stratiﬁcations
Clinical Params
Pathways
Guided Exploration

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)

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)

MD Anderson Cancer Center
University of Rostock
Peter J Park
Michael S Noble, David Heiman, Firehose Team, Gad Getz
Terrence Wu, Ian Watson, Lynda Chin
Harvard Medical School
Broad Institute of MIT & Harvard
Christian Partl, Dieter SchmalstiegGraz University of Technology
Johannes Kepler University Linz Samuel Gratzl, Marc Streit
Hans-Jörg Schulz
Acknowledgements
Harvard SEAS Alexander Lex, Hanspeter Pﬁster
Funding
NIH/NHGRI K99 HG007583 Pathway to Independence

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We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and 30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1 . Our search finds no candidates at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to infer the properties of the evolving luminosity function without binning in redshift or luminosity that marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results, and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5 from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical models for evolution of the dark matter halo mass function.

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With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.

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As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.

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Since volcanic activity was first discovered on Io from Voyager images in 1979, changes on Io’s surface have been monitored from both spacecraft and ground-based telescopes. Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images show that a plume deposit from a powerful eruption at Pillan Patera has covered part of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive optics at visible wavelengths.

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Guided visual exploration of patient stratifications in cancer genomics

1. Guided visual exploration of patient stratiﬁcations in cancer genomics Nils Gehlenborg Harvard Medical School  Center for Biomedical Informatics nils_gehlenborg

2. Machine Human

3. Machine Human

4. Machine Human Data

5. INTERPRETATION GENERATIONCOMPUTATION Machine Human Data

6. “In every chain of reasoning, the evidence of the last conclusion can be no greater than that of the weakest link of the chain, whatever may be the strength of the rest.” - Thomas Reid, Essays on the Intellectual Powers of Man (1786)

7. INTERPRETATION GENERATIONCOMPUTATION Machine Human Data Hypotheses Discoveries Knowledge Cognition

8. x y1 y2 1.00 0.96 0.76 2.00 0.76 -0.14 3.00 -0.14 -0.91 4.00 -0.91 -0.84 5.00 -0.84 0.00 6.00 0.00 0.84 7.00 0.84 0.91 8.00 0.91 0.14 9.00 0.14 -0.76 10.00 -0.76 -0.96 11.00 -0.96 -0.28

9. -1 -0.5 0 0.5 1 1 2 3 4 5 6 7 8 9 10 11

10. INTERPRETATION GENERATIONCOMPUTATION Machine Human Hypotheses Discoveries Knowledge Data Cognition

11. INTERPRETATION GENERATIONCOMPUTATION Machine Human Hypotheses Discoveries Knowledge Data| Cognition

12. ?

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14. TCGAThe Cancer Genome Atlas

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

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22. Stratome

23. Anthony92931 / Wikimedia Commons

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26. StratomeX A Lex, M Streit, H-J Schulz, C Partl, D Schmalstieg, PJ Park, N Gehlenborg, “StratomeX: Visual Anal- ysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization“, Computer Graphics Forum 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)

27. Comparing Patient Sets across StratiﬁcationsPROBLEM 1

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37. Comparing Patient Sets within StratiﬁcationsPROBLEM 2

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49. Finding Relevant Stratiﬁcations and PathwaysPROBLEM 3

50. ?

51. Knowledge-driven Exploration Data-driven Exploration

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53. Query Rank Visualize Stratiﬁcations Clinical Params Pathways Guided Exploration

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55. 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)

56. Query Rank Visualize Stratiﬁcations Clinical Params Pathways Guided Exploration

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59. INTERPRETATION GENERATIONCOMPUTATION Machine Human Hypotheses Discoveries Knowledge Data| Cognition

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61. www.caleydo.org

62. www.caleydo.org

63. www.caleydo.org

64. 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)

65. MD Anderson Cancer Center University of Rostock Peter J Park Michael S Noble, David Heiman, Firehose Team, Gad Getz Terrence Wu, Ian Watson, Lynda Chin Harvard Medical School Broad Institute of MIT & Harvard Christian Partl, Dieter SchmalstiegGraz University of Technology Johannes Kepler University Linz Samuel Gratzl, Marc Streit Hans-Jörg Schulz Acknowledgements Harvard SEAS Alexander Lex, Hanspeter Pﬁster Funding NIH/NHGRI K99 HG007583 Pathway to Independence

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