Canadian Bioinformatics Workshops · Module bioinformatics.ca •Data Communication / Presentation...

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Canadian Bioinformatics Workshops www.bioinformatics.ca

Transcript of Canadian Bioinformatics Workshops · Module bioinformatics.ca •Data Communication / Presentation...

Page 1: Canadian Bioinformatics Workshops · Module bioinformatics.ca •Data Communication / Presentation –Show some result or finding –Figures in papers or public reports (static) –Videos

CanadianBioinformaticsWorkshops

www.bioinformatics.ca

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Module bioinformatics.ca2Module #: Title of Module

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Module7DataVisualization

AnamariaCrisan

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LearningObjectivesofModule

• Bytheendofthislectureyoushouldbeableto:– Understanddatavisualizationdesignatahigh-level– Breakdownandevaluatedatavisualizations– Beawareofgenomicepidemiologydatavisualizationtoolsandothertools

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• Wewon’twatchthiswholevideo,butveryworthspendingtimetowatchonyourownhttps://www.youtube.com/watch?v=hVimVzgtD6w

So- whydatavisualization?

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Module bioinformatics.ca

https://www.youtube.com/watch?v=j4Ut4krp8GQ

So- whydatavisualization?

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Datavisualizationisn’tjust anartproject

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Design Art

Ideastakenfrom@rachelbinx’s 2016OpenVistalkAndhttp://featureguru.com/art-vs-design.html

DataVisualization(Iarguedatavisualizationismuchmoreaboutdesign)

Artvs.design

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Function&form

Form(appearance)follows function(howitworks)

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• DataCommunication/Presentation– Showsomeresultorfinding– Figuresinpapersorpublicreports(static)– Videoswithpapers(animated,butnotinteractive)– Example:Nextstrain.org;microreact.org (interactive)

• DataExploration– Findtrends,patterns,orclusters(amongotherthings)– Example:R(Shiny);Excel;Tableau(withsomelimits);D3

• HypothesisAssessment– Identifywhetherdatacontainspre-conceivedtrend,pattern,orcluster

Howdoweusedatavisualization?

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Module bioinformatics.ca

• PartI:DataVisualizationDesign&Analysis– Principles&methodsforthinkingsystematicallyaboutvisualizationdesign&evaluation

• PartII:Tools&Examples– Overviewofsomeexistingtools– We’lldiveintosomeofthemmoredeeplyinthetutorial

Overview

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Module bioinformatics.ca

Part1:DataVisualizationDesign&Analysis

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Layersofadatavisualization

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Why?Whydoyouneedtovisualizedata?

What?Whatkindofdataisbeingvisualized?

How?Howisdatabeingvisualized?Howdoothersinteractwiththedata?

(Motivation)

(Data)

(Visualand InteractionDesign)

Datavisualizationinthreequestions

Layersofadatavisualization

Peopletendtojumptothislevelandignorewhyandwhat

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Why?

What?

How?

Design EvaluationDoesthevisualizationsolvearelevantproblem?

Areyouusingtherightdata,orderiving therightdata?

Arethevisualandinteractivedesignchoicesappropriate?

LayersofadatavisualizationDatavisualizationinthreequestions

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DomainProblem

Data+Tasks

Visual+InteractionDesignChoices

Algorithm

WHY WHAT HOW

T.Munzner (2009)- Anestedmodelforvisualizationdesignandvalidation

Introductiontothenestedmodel

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DomainProblem

Data+Tasks

Visual+InteractionDesignChoices

Algorithm

DESIGN

EVALUATION

Useaniterative process

Introductiontothenestedmodel

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Aniterativeapproachtodevelopmentallowsustogetfeedbackbeforecommittingtoineffectivedesignchoices

Operationalizingthenestedmodel

Useaniterative agile process

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DomainProblem

Data+Tasks

Visual+InteractionDesignChoices

Algorithm

DESIGN

ADesignStudyMethodology(Paraphrased)

M.Sedlmair etal.(2012)- Adesignstudymethodology:lessonsfromthetrenches

1. Partnerwithagroupofstakeholdersthathaveaproblem

Operationalizingthenestedmodel

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DomainProblem

Data+Tasks

Visual+InteractionDesignChoices

Algorithm

DESIGN

2. Askwhatdata stakeholdersuse(isitavailable)?

3. Askwhatstakeholdersdowiththedata[tasks]

ADesignStudyMethodology(Paraphrased)

Operationalizingthenestedmodel

M.Sedlmair etal.(2012)- Adesignstudymethodology:lessonsfromthetrenches

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Note!Don’tjustuserawdata

Operationalizingthenestedmodel

T.Munzner (2014)– VisualizationDesignandAnalysis

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Note!Don’tjustuserawdata

Operationalizingthenestedmodel

https://xkcd.com/1138/

Example:Using(raw)absolutecounts,whenyouactuallyneedtoderiverates

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DomainProblem

Data+Tasks

Visual+InteractionDesignChoices

Algorithm

DESIGN

4. Exploreifothervisualizationshaveaddressedthisproblem andsetoftasks&data

5. Implementyourownsolution

ADesignStudyMethodology(Paraphrased)

Operationalizingthenestedmodel

M.Sedlmair etal.(2012)- Adesignstudymethodology:lessonsfromthetrenches

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DomainProblem

Data+Tasks

Visual+InteractionDesignChoices

Algorithm

DESIGN

4. Exploreifothervisualizationshaveaddressedthisproblem andsetoftasks&data

5. Implementyourownsolution(partorallofthatsolutioncouldbeanewalgorithm)

ADesignStudyMethodology(Paraphrased)

Operationalizingthenestedmodel

M.Sedlmair etal.(2012)- Adesignstudymethodology:lessonsfromthetrenches

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DomainProblem

Data+Tasks

Visual+InteractionDesignChoices

Algorithm

EVALUATION

6. Testmultiplealternatives(includingnewonesyoudevelop)withstakeholders

7. Gatherqualitative&quantitativeevaluationdata

Operationalizingthenestedmodel

M.Sedlmair etal.(2012)- Adesignstudymethodology:lessonsfromthetrenches

ADesignStudyMethodology(Paraphrased)

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DomainProblem

Data+Tasks

Visual+InteractionDesignChoices

Algorithm

DESIGN

EVALUATION

FORMATIVEEVALUATIONS

Manycyclesofformativeevaluations

ADesignStudyMethodology(Paraphrased)

Operationalizingthenestedmodel

M.Sedlmair etal.(2012)- Adesignstudymethodology:lessonsfromthetrenches

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1. Partnerwithagroupofstakeholdersthathaveaproblem

2. Askwhatdata stakeholdersuse(isitavailable)?

3. Askwhatstakeholdersdowiththedata[tasks]

4. Exploreifothervisualizationshaveaddressedthisproblem andsetoftasks&data

5. Implementyourownsolution(visand/oralgorithm)

6. Testmultiplealternatives(includingnewonesyoudevelop)withstakeholders

7. Gatherqualitative&quantitativeevaluationdata

Puttingitalltogether

M.Sedlmair etal.(2012)- Adesignstudymethodology:lessonsfromthetrenches

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Why

What

How

How

Design

Eval.

Puttingitalltogether

QualitativeMethods,DomainKnowledge

Qualitative&QuantitativeMethods

Design&CognitiveScience

ComputerScience

Methodology

T.Munzner (2014)– VisualizationDesignandAnalysis

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• InformationVisualization(infovis)isadisciplinewithincomputersciencethatstudiesdatavisualization

• Infovisisayoung&evolvinginterdisciplinaryarea

• Alternativemethodsforvisualizationdesign&evaluationexist,butarenotpresentedhere

Anoteondatavisualizationmethods

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DomainProblem

Data+Tasks

Visual+InteractionDesignChoices

Algorithm

Divingintohow

WHY WHAT HOW

• Lastbitoftheory– let’stalkmoreaboutthehow• Thiswillbeacherry-picked,high-leveloverview

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Visualizationbasics:display&interaction

T.Munzner (2014)– VisualizationDesignandAnalysis

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Encoding&decoding

R.Kosara (EagerEyes)– https://eagereyes.org/basics/encoding-vs-decoding

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Visualizationbasics:marks&channelsMark:Geometricprimitive(basicbuildingblock)

Channel:Controlstheappearanceofmarks

T.Munzner (2014)– VisualizationDesignandAnalysis

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Visualizationbasics:marks&channels

T.Munzner (2014)– VisualizationDesignandAnalysis

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Colour=Continent

Transparency=Density

Revealdetailonhover

X-axisposition:Expenditure

y-axispo

sition:life

expectancy

Visualizationbasics:marks&channelsExample of these concepts in action

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Differentdataset,butit’susingsomeofthesamechannels(colour,transparency,size)

Visualizationbasics:marks&channelsExample of these concepts in action

Thisisaterriblevisualization

(morebaddatavisat:viz.wtf )

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J.Heer (2010)– CrowdsourcingGraphicalPerception:UsingMechanicalTurktoAssessVisualizationDesign

• Calculated(log)errorratesfordifferenttypesofbasicdatavisualizations

• Cleveland&McGill(1984),firststudy

• CrowdsourcedResults(2010),usingMechanicalTurk

• Reproducibleresults• Differencesinerrorratesfor

differentvisualizations

Visualizationbasics:marks&channels

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Visualizationdesignaffectshowpeoplewillunderstand,interpret,andactonunderlyinginformationMoreonperception:http://bit.ly/2oQZJBC

Encoding&decoding

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Visualizationbasics:display&interaction

T.Munzner (2014)– VisualizationDesignandAnalysis

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Visualizationbasics:display&interaction

T.Munzner (2014)– VisualizationDesignandAnalysis

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Example:Nextstrain.org

(aboveisananimatedgif)

FilterChange

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Example:TreeJuxtaposer

T.Munzner (2006)– TreeJuxtaposer:ScalableTreeComparisonusingFocus+Context withGuaranteedVisibilityhttps://www.youtube.com/watch?v=GdaPj8a9QEo

SelectJuxtapose

(aboveisananimatedgif)

ComparingPhylogeneticTrees

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Example:TreeJuxtaposer

T.Munzner (2006)– TreeJuxtaposer:ScalableTreeComparisonusingFocus+Context withGuaranteedVisibilityhttps://www.youtube.com/watch?v=GdaPj8a9QEo

ComparingPhylogeneticTreesNavigateJuxtapose

(aboveisananimatedgif)

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• Datavisualizationisnotjustartisticorgraphicdesign

• Thinkaboutwhy-what-how– Datavisualizationlayers:thenestedmodel– Datavisualizationmethods:designstudymethodology– We’lloperationalizetheseconceptsinthetutorial

• Relationshipbetweenhowdataisencoded andhowothersdecode theinformation– Somevisualizationseasiertounderstandthatothers– Somestrategiesforhow tovisualizedata

Takeawaysfromthispart

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Part2:ExamplesandTools

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Datavisualizationinthewild• Manysourcesofdatavisualizationsacrossdisciplinesandeveninpopularpublications

• ExamplesfrominfoVis community:– Mizbee– Pathfinder:VisualizingPaths– TreeJuxtaposer

• ExamplesfromMicrobialGenomicCommunity:– TheIRIDAplatformintegratesanumberoftools

• PhyloViz- online• IslandViewer

– Microreact

• Generalpurposetools:– R,Python,D3,etc.

We’lltalkmoreaboutthese

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DataVisualizationToolsRecreatingonechartusing24tools:http://bit.ly/2gRGx1J

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DataVisualizationToolsRecreatingonechartusing24tools:http://bit.ly/2gRGx1J

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PhyloVizOnlineWe’regoingtolookattheFasta exampledataset

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PhyloVizOnline

• Let’stalkthroughthedesignchoicesofthistool:• Why-what-how

– Whywasthisvisualizationdeveloped?– Whatdatadoesituse?– Howdidauthorschoosetovisualizethedata?

• Marksandchannels– Howisthedataencoded?

• Interactions– Howcanothersinteractwiththedata?

• Whatdoyouthink?– Doyouagreewiththedecisionstheymade?– Wouldyoudoitdifferently?Ifyes,how?

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IslandViewer(http://bit.ly/1LFRPkV)

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IslandViewer(http://bit.ly/1LFRPkV)

• Let’stalkthroughthedesignchoicesofthistool:• Why-what-how

– Whywasthisvisualizationdeveloped?– Whatdatadoesituse?– Howdidauthorschoosetovisualizethedata?

• Marksandchannels– Howisthedataencoded?

• Interactions– Howcanothersinteractwiththedata?

• Whatdoyouthink?– Doyouagreewiththedecisionstheymade?– Wouldyoudoitdifferently?Ifyes,how?

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Microreact(http://bit.ly/2ooye1f)We’regoingtolookattheEbola example

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Microreact(http://bit.ly/2ooye1f)

• Let’stalkthroughthedesignchoicesofthistool:• Why-what-how

– Whywasthisvisualizationdeveloped?– Whatdatadoesituse?– Howdidauthorschoosetovisualizethedata?

• Marksandchannels– Howisthedataencoded?

• Interactions– Howcanothersinteractwiththedata?

• Whatdoyouthink?– Doyouagreewiththedecisionstheymade?– Wouldyoudoitdifferently?Ifyes,how?

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• Toolstobuildyourownvisualization– Includesstatic&interactivecapabilities

• Toolsthatothershavebuiltforyoutouse– Toolsandlibrariesdifferineaseofuse&flexibility– Manyoptionsavailable,choosewhatworksbestforyou

• Thinkingsystematicallyaboutdatavisualizationstools– Usingwhy-what-howtoassesspros&cons

Takeawaysfromthispart

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Whathavewelearnedtoday?

Image:nounproject

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• Understanddatavisualizationdesignatahigh-level– Thedifferencebetweendesign&art– Layersofadatavisualization(thenestedmodel)– Aflavour ofdatavisualizationmethodologies

• Breakdownandevaluatedatavisualizations– Usingwhy-what-how– Beingawareofencoding&decoding– Usingmarksandchannels– Thinkingthroughdisplay&interaction

• Beawareofgenomicepidemiologydatavisualizationtoolsandothertools– Generalpurposetools– Communitydriventools:infovis,microbialgenomics

Returningtothelearningobjectives

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• We’llimplementashiny(https://shiny.rstudio.com/)applicationinthetutorial

• Thedesignoftheshinyapplicationwillbeinspiredbythetoolswe’veseen

Upnext

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WeareonaCoffeeBreak&NetworkingSession


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