Data Visualization in Biomedical Sciences: More than Meets the Eye

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In science, data visualization serves two primary purposes. The first is to explore data sets interactively and the second is to communicate discoveries. However, the requirements for visualizations employed in these activities are very different. Therefore, the software tools used for these purposes are typically disconnected, creating significant challenges for reproducibility and effective communication of discoveries in data-driven biomedical science. In this presentation, I will address how a new approach to creating data visualization tools can connect data analysts and other stakeholders inside and outside the scientific community. I will introduce and demonstrate the "Vistories" approach that was motivated by these question. Presented at the 5th Cancer Research UK Big Data Analytics Conference on Data Visualization.

Wissenschaft

Data Visualization in Biomedical Sciences:
More than Meets the Eye
Nils Gehlenborg, PhD
Department of Biomedical Informatics
Harvard Medical School
http://gehlenborglab.org @ngehlenborghttp://gehlenborglab.org

http://gehlenborglab.org
Research Data Clinical Data

http://gehlenborglab.org
Research Data Clinical Data
Casual Users
Expert Users

http://gehlenborglab.org
Clinical Data
Casual Users
Expert Users
Research Data

http://higlass.io http://hipiler.higlass.io
http://caleydo.org/tools/stratomex http://halyos.gehlenborglab.org
https://vcg.github.io/upset
OncoThreads
http://gehlenborglab.org

REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
hypothesis
generation

REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
hypothesis
generation
EXPLANATION
“Storytelling”

REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
hypothesis
generation
EXPLORATION
EXPLANATION
“Storytelling”
“Pattern Discovery”

REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
HYPOTHESIS
hypothesis
generation
EXPLORATION
EXPLANATION
“Storytelling”
“Pattern Discovery”
HYPOTHESIS-DRIVEN DISCOVERY

REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
DATA
hypothesis
generation
EXPLORATION
EXPLANATION
“Storytelling”
“Pattern Discovery”
DATA-DRIVEN DISCOVERY

Adobe IllustratorCytoscape
Insight
EXPLORATION EXPLANATION

Researchers Researchers
Decision Makers
Policy Makers
Citizens
…
Insight
EXPLORATION EXPLANATION
Decision Makers
Policy Makers
Citizens
…

Nature asked 1,576 researchers if there
is a reproducibility crisis in science.
M Baker, Nature 533, 452-454, 2016

0% 100%
No crisis (3%)
Don’t know (7%)
Slight crisis (38%)
M Baker, Nature 533, 452-454, 2016
Signiﬁcant crisis (52%)
Nature asked 1,576 researchers if there
is a reproducibility crisis in science.

Intentional?
Inability to capture everything?
Inability to communicate everything?
M Baker, Nature 533, 452-454, 2016

Intentional?
Inability to capture everything?
Inability to communicate everything?
SOCIAL ISSUE
TECHNICAL ISSUES
M Baker, Nature 533, 452-454, 2016

HOW IS THIS RELATED TO
DATA VISUALIZATION?

Tumor Subtypes
PROBLEM 1
Visualize overlap of patient sets across two or more stratifications.
PROBLEM 2
Visualize characteristics of patient sets within a stratification of interest.

M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014)
StratomeX

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, P Park, N Gehlenborg , Nature Methods (2014)

Query
Rank
Visualize
Stratifications
Clinical Params
Pathways
Guided
Exploration
M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014)

DATA-DRIVEN DISCOVERY
REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
DATA
hypothesis
generation
EXPLORATION
EXPLANATION
“Storytelling”
“Pattern Discovery”

DATA-DRIVEN COMMUNICATION
DATA-DRIVEN DISCOVERY

ﬁnding ﬁgure/videoAuthoringExploration Presentation
DATA-DRIVEN COMMUNICATION
ﬁnding ﬁgure/videoAuthoringExploration Presentation
Current Model
DATA-DRIVEN DISCOVERY

DATA-DRIVEN COMMUNICATION
ﬁnding ﬁgure/videoAuthoring Presentation
What we show.
DATA-DRIVEN DISCOVERY

DATA-DRIVEN COMMUNICATION
ﬁnding ﬁgure/videoAuthoring Presentation
What we tell.
DATA-DRIVEN DISCOVERY

DATA-DRIVEN COMMUNICATION
ﬁnding ﬁgure/videoAuthoring Presentation
What we did.
DATA-DRIVEN DISCOVERY

DATA-DRIVEN COMMUNICATION
track provenance
annotate observations
make sense of observations
tell the story
DATA-DRIVEN DISCOVERY

DATA-DRIVEN COMMUNICATION
Capture
Label
Understand
Explain
track provenance
annotate observations
make sense of observations
tell the story
C
L
U
E
DATA-DRIVEN DISCOVERY

CLUE
vistories
Authoring
Exploration Presentation
CLUE Model
DATA-DRIVEN COMMUNICATION
DATA-DRIVEN DISCOVERY

VISTORY = visualization + story + history
Do collaborative data analysis.
Use during peer-review.
Publish with a paper.
Embed in a presentation.

DATA-DRIVEN DISCOVERY
DATA-DRIVEN COMMUNICATION

DATA-DRIVEN DISCOVERY
DATA-DRIVEN COMMUNICATION
http://vistories.org

DATA-DRIVEN DISCOVERY
DATA-DRIVEN COMMUNICATION
http://vistories.org
SAMUEL GRATZL
ALEXANDER LEX
MARC STREIT

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We explore different scenarios to explain the chemical difference found in the remarkable giant-giant binary system HD138202+CD−3012303. For the first time, we suggest how to distinguish these scenarios by taking advantage of the extensive convective envelopes of giant stars. Methods. We carried out a high-precision determination of stellar parameters and abundances by applying a full line-by-line differential analysis on GHOST high-resolution spectra. We used the FUNDPAR program with ATLAS12 model atmospheres and specific opacities calculated for an arbitrary composition through a doubly iterated method. Physical parameters were estimated with the isochrones package and evolutionary tracks were calculated via MIST models. Results. We found a significant chemical difference between the two stars (∆[Fe/H]∼0.08dex), which is largely unexpected considering the insensitivity of giant stars to planetary ingestion and diffusion effects. We tested the possibility of engulfment events by using several different combinations of stellar mass, ingested mass, metallicity of the engulfed object and different convective envelopes. However, the planetary ingestion scenario does not seem to explain the observed differences. For the first time, we distinguished the source of chemical differences using a giant-giant binary system. By ruling out other possible scenarios such as planet formation and evolutionary effects between the two stars, we suggest that primordial inhomogeneities might explain the observed differences. This remarkable result implies that the metallicity differences that were observed in at least some main-sequence binary systems might be related to primordial inhomogeneities rather than engulfment events. We also discuss the important implications of finding primordial inhomogeneities, which affect chemical tagging and other fields such as planet formation. We strongly encourage the use of giantgiant pairs. They are a relevant complement to main-sequence pairs for determining the origin of the observed chemical differences in multiple systems.

Disentangling the origin of chemical differences using GHOST

Sérgio Sacani

Mustard, as a crop, is susceptible to a variety of pests that can affect its growth and yield. Here’s a rundown of some common pests that target mustard plants: Aphids: These small, sap-sucking insects can cause significant damage by feeding on the leaves and stems. Aphids also excrete a sticky substance known as honeydew, which can lead to the growth of sooty mold on the plants. Flea Beetles: These tiny beetles jump like fleas when disturbed and chew small holes in the leaves. They are particularly damaging in the early growth stages of the plant. Cabbage Loopers: The larvae of a type of moth, these caterpillars are known for their distinctive looping movement. They chew large holes in the leaves and can defoliate plants if present in large numbers. Diamondback Moth Larvae: Another caterpillar pest, these larvae chew small holes in the leaves and can cause extensive damage, especially when infestations are heavy. Whiteflies: These are tiny, winged insects that feed on plant sap and can quickly become a problem in greenhouse or close planting conditions. Like aphids, they also secrete honeydew. Cutworms: These are the larvae of certain types of moths and are known for cutting young plants at the stem base at ground level. They are most destructive during the night. Root Maggots: The larvae of root maggot flies, these pests attack the roots of mustard plants, causing wilting and potentially killing young plants. Harlequin Bugs: These are colorful stink bugs that suck the sap from mustard plant stems and leaves, causing the leaves to become stippled, wilt, and eventually die if the infestation is severe. Mustard Sawfly: The larvae of the mustard sawfly can cause considerable defoliation, as they feed voraciously on the leaves. Clubroot: Caused by a fungus-like organism, clubroot affects the roots, causing them to swell and distort. While technically a disease, it is often associated with pest management because controlling it involves similar preventative strategies. Control Measures: Managing pests in mustard involves a combination of cultural, biological, and chemical methods. Crop rotation, resistant varieties, timely sowing, maintaining plant health, and using natural predators like ladybugs and parasitic wasps can help keep pest populations under control. Chemical pesticides should be used as a last resort due to their potential impact on the environment and non-target species.

Pests of mustard_Identification_Management_Dr.UPR.pdf

PirithiRaju

Cotton crops are vulnerable to a variety of sucking pests, which can severely impact plant health, yield, and fiber quality. These pests primarily feed on plant sap, extracting nutrients directly from the plant's vascular system. Here's a breakdown of some of the most significant sucking pests in cotton cultivation: Aphids: Cotton aphids or melon aphids can cause direct damage by sucking sap and indirect damage by secreting honeydew, which encourages sooty mold growth. This can interfere with photosynthesis and weaken the plant. Aphids can also transmit viral diseases. Whiteflies: Two species, the silverleaf whitefly and the bandedwinged whitefly, are particularly troublesome. They not only suck sap from the underside of leaves, causing yellowing and leaf drop, but their honeydew excretion promotes sooty mold and they can transmit several plant viruses. Thrips: While thrips can chew on plants, their primary damage to cotton is through sucking. They attack the cotton plant during its seedling stage, which can stunt growth and reduce vigor. Thrips are also capable of transmitting the Cotton Bud disease. Spider Mites: These are not insects but arachnids. Spider mites, such as the two-spotted spider mite, suck cell contents from the leaves, leading to speckled discoloration and potentially significant leaf loss if infestations are severe. Leafhoppers: Including various species, leafhoppers can cause direct damage through feeding, which results in leaf curling and stunted growth. They can also be vectors for plant diseases. Mealybugs: These pests are less common but can be problematic, especially in clustered planting conditions. They suck sap and secrete honeydew, which leads to sooty mold. Mealybugs can also spread viruses. Stink Bugs: Although primarily known for their chewing mouthparts, certain stink bugs can cause damage similar to sucking pests by injecting saliva into the plant and sucking out nutrients, leading to boll damage and stained lint. Management Strategies: Cultural Controls: This includes practices such as crop rotation, using resistant varieties, and managing planting and harvesting times to avoid peak pest populations. Biological Controls: Beneficial insects like lady beetles, lacewings, and predatory mites can naturally control sucking pest populations. Parasitic wasps also play a role in controlling aphid and whitefly populations. Chemical Controls: Insecticides may be used but should be chosen carefully to minimize resistance development and preserve beneficial insects. Systemic insecticides can be particularly effective against sucking pests. Integrated Pest Management (IPM): Combining multiple control strategies based on monitoring and thresholds to apply the most effective and environmentally sensitive approach. Effective management of sucking pests in cotton requires a thorough understanding of the pest species present, their life cycles, and the ecological balance of the field environment.

Pests of cotton_Sucking_Pests_Dr.UPR.pdf

PirithiRaju

Context. Determining the size distribution of asteroids is key to understanding the collisional history and evolution of the inner Solar System. Aims. We aim to improve our knowledge of the size distribution of small asteroids in the main belt by determining the parallaxes of newly detected asteroids in the Hubble Space Telescope (HST) archive and subsequently their absolute magnitudes and sizes. Methods. Asteroids appear as curved trails in HST images because of the parallax induced by the fast orbital motion of the spacecraft. Taking into account the trajectory of this latter, the parallax effect can be computed to obtain the distance to the asteroids by fitting simulated trajectories to the observed trails. Using distance, we can obtain the absolute magnitude of an object and an estimation of its size assuming an albedo value, along with some boundaries for its orbital parameters. Results. In this work, we analyse a set of 632 serendipitously imaged asteroids found in the ESA HST archive. Images were captured with the ACS/WFC and WFC3/UVIS instruments. A machine learning algorithm (trained with the results of a citizen science project) was used to detect objects in these images as part of a previous study. Our raw data consist of 1031 asteroid trails from unknown objects, not matching any entries in the Minor Planet Center (MPC) database using their coordinates and imaging time. We also found 670 trails from known objects (objects featuring matching entries in the MPC). After an accuracy assessment and filtering process, our analysed HST asteroid set consists of 454 unknown objects and 178 known objects. We obtain a sample dominated by potential main belt objects featuring absolute magnitudes (H) mostly between 15 and 22 mag. The absolute magnitude cumulative distribution logN(H > H0) ∝ αlog(H0) confirms the previously reported slope change for 15 < H < 18, from α ≈ 0.56 to α ≈ 0.26, maintained in our case down to absolute magnitudes of around H ≈ 20, and therefore expanding the previous result by approximately two magnitudes. Conclusions. HST archival observations can be used as an asteroid survey because the telescope pointings are statistically randomly oriented in the sky and cover long periods of time. They allow us to expand the current best samples of astronomical objects at no extra cost in regard to telescope time.

Hubble Asteroid Hunter III. Physical properties of newly found asteroids

Sérgio Sacani

Seismic Method Estimate velocity from seismic data.pptx

AlMamun560346

Jupiter’s moon Io hosts extensive volcanism, driven by tidal heating. The isotopic composition of Io's inventory of volatile chemical elements, including sulfur and chlorine, reflects its outgassing and mass loss history, and thus records information about its evolution. We used millimeter observations of Io’s atmosphere to measure sulfur isotopes in gaseous SO2 and SO, and chlorine isotopes in gaseous NaCl and KCl. We find 34S/32S = 0.0595 ± 0.0038 (equivalent to δ34S = +347 ± 86‰), which is highly enriched compared to average Solar System values and indicates that Io has lost 94 to 99% of its available sulfur. Our measurement of 37Cl/35Cl = 0.403 ± 0.028 (δ37Cl = +263 ± 88‰) shows that chlorine is similarly enriched. These results indicate that Io has been volcanically active for most (or all) of its history, with potentially higher outgassing and mass-loss rates at earlier times.

Isotopic evidence of long-lived volcanism on Io

Sérgio Sacani

Recombination DNA Technology (Nucleic Acid Hybridization )

aarthirajkumar25

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Data Visualization in Biomedical Sciences: More than Meets the Eye

1. Data Visualization in Biomedical Sciences: More than Meets the Eye Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School http://gehlenborglab.org @ngehlenborghttp://gehlenborglab.org

2. datavisyn Co-Founder

3. Thank you!

4. Data Visualization in Biomedical Sciences: More than Meets the Eye Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School http://gehlenborglab.org @ngehlenborghttp://gehlenborglab.org

5. http://gehlenborglab.org

6. http://gehlenborglab.org Research Data Clinical Data

7. http://gehlenborglab.org Research Data Clinical Data Casual Users Expert Users

8. http://gehlenborglab.org Clinical Data Casual Users Expert Users Research Data

9. http://higlass.io http://hipiler.higlass.io http://caleydo.org/tools/stratomex http://halyos.gehlenborglab.org https://vcg.github.io/upset OncoThreads http://gehlenborglab.org

10. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation

11. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation EXPLANATION “Storytelling”

12. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery”

13. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation HYPOTHESIS hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery” HYPOTHESIS-DRIVEN DISCOVERY

14. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery” DATA-DRIVEN DISCOVERY

15. Insight EXPLORATION EXPLANATION

16. Adobe IllustratorCytoscape Insight EXPLORATION EXPLANATION

17. Insight EXPLORATION EXPLANATION

18. Researchers Researchers Decision Makers Policy Makers Citizens … Insight EXPLORATION EXPLANATION Decision Makers Policy Makers Citizens …

19. Researchers Researchers Decision Makers Policy Makers Citizens … Insight EXPLORATION EXPLANATION Decision Makers Policy Makers Citizens …

20. Researchers Researchers Decision Makers Policy Makers Citizens … Insight EXPLORATION EXPLANATION Decision Makers Policy Makers Citizens …

21. WHY SHOULD WE CARE?

22. Nature asked 1,576 researchers if there is a reproducibility crisis in science. M Baker, Nature 533, 452-454, 2016

23. 0% 100% No crisis (3%) Don’t know (7%) Slight crisis (38%) M Baker, Nature 533, 452-454, 2016 Signiﬁcant crisis (52%) Nature asked 1,576 researchers if there is a reproducibility crisis in science.

24. M Baker, Nature 533, 452-454, 2016

25. M Baker, Nature 533, 452-454, 2016

26. M Baker, Nature 533, 452-454, 2016

27. Intentional? Inability to capture everything? Inability to communicate everything? M Baker, Nature 533, 452-454, 2016

28. Intentional? Inability to capture everything? Inability to communicate everything? SOCIAL ISSUE TECHNICAL ISSUES M Baker, Nature 533, 452-454, 2016

29. HOW IS THIS RELATED TO DATA VISUALIZATION?

30. Tumor Subtypes PROBLEM 1 Visualize overlap of patient sets across two or more stratifications. PROBLEM 2 Visualize characteristics of patient sets within a stratification of interest.

31. M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014) StratomeX

32.

33. Tumor Subtypes 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.

34. 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, P Park, N Gehlenborg , Nature Methods (2014)

35. Query Rank Visualize Stratifications Clinical Params Pathways Guided Exploration M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014)

36.

37. ?

38.

39.

40. AND NOW WHAT?

41. DATA-DRIVEN DISCOVERY REPORT experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery”

42. DATA-DRIVEN DISCOVERY REPORT experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery”

43. DATA-DRIVEN COMMUNICATION DATA-DRIVEN DISCOVERY

44. finding figure/videoAuthoringExploration Presentation DATA-DRIVEN COMMUNICATION finding figure/videoAuthoringExploration Presentation Current Model DATA-DRIVEN DISCOVERY

45. DATA-DRIVEN COMMUNICATION ﬁnding ﬁgure/videoAuthoring Presentation What we show. DATA-DRIVEN DISCOVERY

46. DATA-DRIVEN COMMUNICATION ﬁnding ﬁgure/videoAuthoring Presentation What we tell. DATA-DRIVEN DISCOVERY

47. DATA-DRIVEN COMMUNICATION ﬁnding ﬁgure/videoAuthoring Presentation What we did. DATA-DRIVEN DISCOVERY

48. DATA-DRIVEN COMMUNICATION DATA-DRIVEN DISCOVERY

49. DATA-DRIVEN COMMUNICATION track provenance annotate observations make sense of observations tell the story DATA-DRIVEN DISCOVERY

50. DATA-DRIVEN COMMUNICATION Capture Label Understand Explain track provenance annotate observations make sense of observations tell the story C L U E DATA-DRIVEN DISCOVERY

51. CLUE vistories Authoring Exploration Presentation CLUE Model DATA-DRIVEN COMMUNICATION DATA-DRIVEN DISCOVERY

52. Exploration Authoring Presentation

53. Exploration Authoring Presentation

54. Exploration Authoring Presentation

55. Exploration Authoring Presentation

56. Exploration Authoring Presentation

57. Exploration Authoring Presentation

58. Exploration Authoring Presentation

59. Exploration Authoring Presentation

60. VISTORY = visualization + story + history Do collaborative data analysis. Use during peer-review. Publish with a paper. Embed in a presentation.

61. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION

62. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION http://vistories.org

63. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION http://vistories.org SAMUEL GRATZL ALEXANDER LEX MARC STREIT

64. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION http://vistories.org

65. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery” DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION

Data Visualization in Biomedical Sciences: More than Meets the Eye

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