SupplementaryMaterials

SmartR:Anopen-sourceplatformforinteractivevisualanalyticsfortranslationalresearchdataSaschaHerzinger,WeiGu,VenkataSatagopam,SergeEifes,KavitaRege,AdrianoBarbosaDaSilva,ReinhardSchneider

HereweprovideafewexampleimplementationsofvisualanalyticsusingtheSmartRframework.Alltoolsshowninthefigurescanbeaccessedviathepublicserverathttp://smartR.lcsb.uni.lu

1)DynamicHeatMap:Foradetailedlistofthefeaturesforthisworkflow,pleaserefertotheassociatedpaper.Thisfigureisincludedforcompletenessandincludesalinktoavideothatdemonstratesthedynamicnatureoftheheatmap.

SFig.1Dynamicheatmap.Theshownheatmapisfullysortableandcontainsmanyinteractiveelementsthatarelistedindetailinthe

associatedpaper.VideoURL:https://youtu.be/kLRSOMbKuns

2)CorrelationAnalysis:Thisworkflowconsistsofascatterplot,histogramsfortherespectiveaxes,andalegendwithstatistics,suchascorrelationcoefficientandp-value.Thescatterplotallowsuserinteractionbyareaselection,whichtriggersthere-computationofthecorrelationstatistics,theregressionlineposition,andthehistograms.Thecontextmenu,availableviaright-click,revealstheoptionstozoom,resetorexcludetheselectedarea.

SFig.2CorrelationAnalysis.Shownisascatterplotwithhistogramsfortherespectiveaxes.

Statisticsandotherplotelementsadaptdynamicallytocertainuser-triggeredevents.VideoURL:https://youtu.be/IP7vBSQsaFA

3)LineGraph:Themainpurposeofthelinegraphistovisualizelongitudinaldata.Mainfeaturesarethemanuallysortablex-axis,whichisimportantincasethedataarenottime-annotated(unordered),hover-eventsthathighlightcertaindataorrevealdetailedinformation(s.SFig.3),andthepossibilitytoswitchinstantlybetweenseveraldisplaymodes(e.g.meanwithSEMerrorbars,medianwithSDerrorbars,orrawdata).

SFig.3Linegraph.Shownisaplotthatvisualizesmachinegeneratedtimeseriesdata.Besidesmanyvisualhelpersthataretriggeredby

mouse-overevents,thisvisualizationhasamanualsortablex-axisanddifferentmethodsfordefiningtheshowntimelines.VideoURL:https://youtu.be/GFCmJysbTyM

4)Boxplots:Thisworkflowfeaturesboxplotswithadiamond-likeshapethatrepresentsmeanwithstandarddeviation.Besidestheoptiontolog-transformyourdatabeforevisualization,itisworthmentioningthatthepossibilityexiststoselectandtreatsinglerowsofmicro-arraydataasnormalnumericalinputforthisworkflow.

SFig.4Boxplots.CurrentlythisistheonlyvisualizationthatisusingPlotly(PlotlyTechnologiesInc.Collaborativedatascience.Montréal,QC,2015.https://plot.ly.)asavisualizationlibrary.Thisisincludedforcompletenessandtodemonstratethattheframeworkisnotlimitedto

certainvisualizationlibraries.

5)VolcanoPlot:Thisworkflowhelpstoidentifymicro-arrayfeaturesthathavebothahighlog-foldchangeandasignificantp-value.Besidesatooltipthatshowsthestatisticsforeachdatapoint,thereisatablethatlistsalldatapointswithanabsolutelog-foldchangegreaterthan0.5andap-valuelowerthan0.05.Thesevaluesarerepresentedbylimitersinthescatterplot,whicharedrag-enabledandinstantlyupdatethetableonchange.SimilartothedendrogramfeatureintheSmartRheatmap,wecanlaunchaKEGGpathwayenrichmentanalysisforgenesofinterest.

SFig.5Volcanoplot.Shownisthewidelyknownvolcanoplotthathasbeenenhancedbysomedynamicelements.Thelimitersaredrag-

enabledandtriggeranupdatefortheright-handtabledisplayingthemostsignificantgenes.VideoURL:https://youtu.be/OxEgDs8vebY

ShowCase–GuidedAnalysisofParkinsonDisease(PD)dataset

InthefollowingwewilldemonstratethecapabilitiesoftheSmartRHeatMapinashow-casewiththeGEOstudyGSE7621,whichcontains"ExpressiondataofsubstantianigrafrompostmortemhumanbrainofParkinson'sdiseasepatients(PD)”1.Thestudyalsocontainspatientswithoutthedisease,servingasacontrolgroup.

1LesnickTG,PapapetropoulosS,MashDC,Ffrench-MullenJetal.Agenomicpathwayapproachtoacomplexdisease:axonguidanceandParkinsondisease.PLoSGenet2007Jun;3(6):e98.PMID:17571925

SFig.5TheComparisonTab.Thisisourfirstviewwhenladingthewebpage.Ontheleftside,ourdataarerepresentedinatree-likestructure.Ontherightsidewecan,basedonthisdata,definecaseandcontrolgroup.

SFig.6aThei2b2Tree.Forthisshowcase,weexpandthedatatreetoexposecaseandcontrolgroupoftheGSE7621studyasshowninthefigure.

SFig.7Definingcaseandcontrolgroup.Bydraggingtherespectivenodesintothetwosubsetwindows,wedefineourcohortsforthefollowinganalysis.Inourcase,wewanttocomparePDsubjectswiththehealthycontrolgroup.

SFig.8TheSmartRTab.OnceourgroupsaredefinedweswitchtotheSmartRtabandselectthe‘HeatmapWorkflow’asshown.

SFig.9aTheFetchTab.Afterselectingaworkflow,wearepresentedwithseveralboxes.Theseboxescanbeusedtoassignrolestoourdatanodesfromthetreeontheleft.Inthisshowcaseweobviouslymustdefinetheexpressiondatawewishtoanalyze,butarefreetoincludeclinicaldata,aswewillseelater.

SFig.6bThei2b2Tree.Wefurtherexpandthetreetoshowthesubjectgenderandtheexpressiondata.

SFig.9bTheFetchTab.Again,byusingdrag-and-drop,wedefinewhatdatawewishtoincludeinouranalysis.Expressiondataaredraggedtotheleft-mostboxandthetwocategoricalgendernodesaredraggedtotheright-most.Aswewillseeinamoment,thiswillallowustorelatesubjectstotheirrespectivegender.

SFig.9cTheFetchTab.Nowwepressthebutton“FetchData”andwaitamomentuntilalldataareloadedandcanseeasmallsummaryofstatistics.Inthebackgroundthisstepfetchesthejustdefinednodesfromthedatabaseandassignsthesubjectstotheirrespectivecohortthatwedefinedearlier.

SFig.10TheRunAnalysisTab.Nowwecanswitchtothe“RunAnalysis”tab,whereitispossibletosetseveralparameterstotweaktheanalysisscriptbeforecreatingtheheatmap.Forthisshowcase,thedefaultparametersareagoodchoice.

SFig.11aTheHeatMap.Oncetheanalysisisfinishedthedynamicheatmapiscreated.Visiblearethetopgenesaccordingtoadifferentialexpressionanalysisexecutedbasedontheparametersofthepreviousfigure.Featuresoftheheatmapareintroducedasneededinthefurthercourseofthisshowcase.

SFig.11bTheHeatMap.Inthelowerrightcornerofthescreenwecanfindseveraloptionsthatwillmodifyourheatmapon-the-fly.Fornow,wewanttoapplyahierarchicalclusteringtoseeifwecanclearlyseparatecaseandcontrolgroup.Theoption“Hierarch.-Eucl.-Avg.”willapplysuchaclusteringwith‘Euclidean’distancemeasureand‘Average’linking.

SFig.12TheSubjectDendrogram.Abovetheheatmapwecanseeourcohorts,encodedinyellowandorangecolor,andthepreviouslyaddedgendernodes.Wecandeducetwofactsfromthisimage:1.Theclusteringcouldclearlyseparateourcohortsintotwogroups.2.Thesubjectgenderseemstohavenocorrelationastohowthedataaregrouped.

SFig.13TheRowDendrogram.Inasimilarfashionthegenesarewellseparatedintotwosubgroups.Forfurtheranalysis,wemightwanttoattempttoassociatethesegeneswithaKEGGpathway.Byclickingthemarkeddendrogramnodethecontainedleafs/genesaresendtoanexternalservicetohandlethistask(bioCompendium.Thehigh-throughputexperimentaldataanalysisplatform(2016).Retrievedfromhttp://biocompendium.embl.de/).

SFig.14BioCompendium.Sureenough,weseeanassociationofthesegeneswiththePDPathway.ClickingtheredflagontheleftsidewillbringustotheKEGGwebservice.

SFig.15KEGGPathway.Associatedgenesarehighlightedinanimageofthepathwayforfurtherinterpretationoranalysis.

SFig.16Statisticsrepresentation.Backintheheatmap,wehavealookattheredbarsontheleftside.Basedonouranalysisparameters,thesebarsinitiallyindicatetheadjustedp-valuefortherespectivegene.Onegeneshowsanextraordinarylowp-value,representedbyalongbar.

SFig.17aManualSorting.Byclickingthebuttonsaroundtheheatmap,wecanmanuallysortbycolumnsorrows.Sortingthecolumnsbasedontheexpressionvaluesforthissinglegeneseemstohavealmostnoimpact.

SFig.17bManualSorting.Sureenough,thepreviousobservationisconfirmedbythenearlyperfectsplitbetweencaseandcontrolgroup.Thismakesthegeneagoodcandidateforfurtheranalysis.Ifonewishestoproceedfromhere,clickingthenameofthegenewillredirecttheanalysttoanotherservice,wherefurthergenespecificdetailsarelisted.

SFig.18StatisticsTable.Finally,mostofthedataaredisplayedbelowtheheatmapintableform.Thetableautomaticallyadaptstothecurrentsortingoftheheatmaprowstoincreasevisibility.Thesedataarealsoretrievablebyclickingoneofthebuttonsnearthe“CreatePlot”button.