Analysisofsingle-cellRNA-seqdatawithRandBioconductor.

FannyPerraudeau,KellyStreet,DavideRisso

Tasksandpackages

•  DimensionalityreducDonwithzinbwave.– bioconductor.org/packages/zinbwave

•  ClusteranalysiswithclusterExperiment.– bioconductor.org/packages/clusterExperiment

•  Lineageinferenceandtrajectoryanalysiswithslingshot.– github.com/kstreet13/slingshot

Workshopmaterial:github.com/fperraudeau/bioc2017singlecell

Acknowledgements UC Berkeley

Methods development Sandrine Dudoit

Elizabeth Purdom Nir Yosef

Svetlana Gribkova JP Vert

Experiments and analyses Russell Fletcher

Diya Das John Ngai Lab

Core Facilities QB3 Functional Genomics Laboratory

QB3 Genomics Sequencing Laboratory Cancer Research Laboratory

Funding

NIH BRAIN Initiative Cell Census Consortium

Adult stem cells maintain and regenerate tissues

Bond, Ming, Song (2015) Cell Stem Cell

Fletcher et al. (2017) Cell Stem Cell

Usefullinks

•  ZINB-WaVEpreprint:– hYps://doi.org/10.1101/125112

•  Slingshotpreprint:– hYps://doi.org/10.1101/128843

•  Pleasesubmitbugreportsandotherissuesat:– github.com/drisso/zinbwave/issues– github.com/epurdom/clusterExperiment/issues– github.com/kstreet13/slingshot/issues

Contactus!

•  E-mail–  fperraudeau@berkeley.edu– kstreet@berkeley.edu–  risso.davide@gmail.com

•  TwiYer:– @fannyperraudeau– @KStreetBerkeley– @drisso1893

RSEC:Resampling-basedSequenDalEnsembleClustering

•  UseaclusteringrouDnethatfindsalargenumberofsmall,coherentclusters:–  Subsamplingofdatatofindrobustclusters–  SequenDalclusteringàfindagroupofcoherentsamples,removethem,startover

•  PerformthisrouDneovermanydifferentparameters•  Findasingleconsensusovertheclusterings•  Mergetogethernon-differenDalclusters•  FindbiomarkersviadifferenDalexpressionwithtargetedcomparisons

•  ImplementedwithvisualizaDontoolsinclusterExperiment package

Whatmeanbysubsampleclustering?

•  Pickanunderlyingclusteringstrategy(e.g.kmeansorPAMwithparDcularchoiceofK)

•  Repeatthefollowing:– Subsamplethedata,e.g.70%ofsamples– Findclustersonthesubsample

•  CreatecoClusteringmatrixD:%ofsubsampleswheresampleswereinsamecluster

ExamplesofmatrixD

Note,hereforcedthesamplesinordergivenbyPAMAlsousedkmeansinresampling,ratherthanPAM

K=3 K=7

Whatmeanbysubsampleclustering?

•  Pickanunderlyingclusteringstrategy(e.g.kmeansorPAMwithparDcularchoiceofK)

•  Repeatthefollowing:–  Subsamplethedata,e.g.70%ofsamples–  Findclustersonthesubsample

•  CreatecoClusteringmatrixD:%ofsubsampleswheresampleswereinsamecluster

•  ClustermatrixDforfinalclustering–  Couldbewithoriginalclusteringstrategy,ordifferentone–  “Right”KmaynotbetheKusedinoriginalclustering(e.g.kmeans)

–  Weuseamoreflexibleapproachofhierarchicalclusteringandpickingclusterssohaveatleast1-αsimilarity

–  ChangefrompickingKtopickingα,moreintuiDvechoice–  Notallsamplesgetclustered

WhatmeanbysequenDalclustering?

•  OverrangeofstarDngparameters,doclustering•  Theclusterthatstaysatleastβsimilar,idenDfyascluster

andremove•  RepeatunDlnomoresuchclustersfound,ornotenough

sampleslen•  Drawsonideasof“Dghtclustering”ofgenesofTsengand

Wong(2005)

Specifically,•  WerangeoverKinunderlyingPAMinsubsampling•  Wefindclustersbasedonresultsofsubsampleclustering

(somaynotbesameKasinputparameter)

Becauseofsubsampling,changingKismoreaperturbaDon

K=4 K=5

K=6 K=7

Findconsensuscluster

•  Createaco-ClusteringmatrixDofhowmanyDmesco-clustertogetherandclusterD– Likewithsubsampling,onlynowacrossdifferentparameters

– Again,importantthatclustersarelargelyperturbaDons,notradicallydifferentclusters

Bioconductorworkflowforsingle-cellRNA-seqdataanalysis:

dimensionalityreduc<on,clustering,andpseudo<meordering.

FannyPerraudeau,DavideRisso,KellyStreetBioC2017

July28th,2017

Clustereddatainlow-dimensionalspace.Weconsiderdimensionalityreduc<onandclusteringtobeseparateproblems,butgenerallypreferPCAandRSEC.

2

Step0:Input

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3

Step1:ClusterMST

Constructminimumspanningtree(MST)onclusters.Thenodesareclusters,notclustercenters.Requiresadistancemetric.

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4

Step1:ClusterMST

Constructminimumspanningtree(MST)onclusters.Selectstar<ngclusterbasedonmarkergenesorparsimony.

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5

Step1:ClusterMST

Specifyknownterminalclustersforaddi<onalsupervision.Thisresultsintheconstruc<onofaconstrainedMST.

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6

Step1:ClusterMST

Constructminimumspanningtree(MST)onclusters.Selectstar<ngclusterbasedonmarkergenesorparsimony.

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7

Step1.5:PrincipalCurves

Highlystable,nonlineargeneraliza<onofprincipalcomponents.Fitsacurvetothe“middle”ofthedata.Inconsistent,failstoreflectunderlyingbiology(ie.branching).

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8

Step2:SimultaneousPrincipalCurves

S<llhighlystableandnonlinear.Extendstheconceptofprincipalcurvestomul<ple,branchingcurveswithcommonorigin(smoothtreestructures).

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9

Step2:SimultaneousPrincipalCurves

S<llhighlystableandnonlinear.Extendstheconceptofprincipalcurvestomul<ple,branchingcurveswithcommonorigin(smoothtreestructures).

10

Step2:SimultaneousPrincipalCurves

Projectcellsontocurvestoobtainpseudo<mevalues.Likelinearprincipalcomponents,thecurvesseektominimizesquaredprojec<ondistance(subjecttosomeconstraints).

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