Clustering Gene Expression Data. (Slides thanks to Dr. Mark Craven)


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1 Clusterng Gene Epresson Data Sldes thanks to Dr. Mark Craven
2 Gene Epresson Proles we ll assume we have a D matr o gene epresson measurements rows represent genes columns represent derent eperments tme ponts ndvduals etc. what we can measured usng one* mcroarray we ll reer to ndvdual rows or columns as proles a row s a prole or a gene * Dependng on the number o genes beng consdered we mght actually use several arrays per eperment tme pont ndvdual.
3 Epresson Prole Eample rows represent genes columns represent people wth leukema
4 Task Denton: Clusterng Gene Epresson Proles gven: epresson proles or a set o genes or eperments/ndvduals/tme ponts whatever columns represent do: organze proles nto clusters such that nstances n the same cluster are hghly smlar to each other nstances rom derent clusters have low smlarty to each other
5 Motvaton or Clusterng eploratory data analyss understandng general characterstcs o data vsualzng data generalzaton ner somethng about an nstance e.g. a gene based on how t relates to other nstances everyone else s dong t
6 The Clusterng Landscape there are many derent clusterng algorthms they der along several dmensons herarchcal vs. parttonal lat hard no uncertanty about whch nstances belong to a cluster vs. sot clusters dsunctve an nstance can belong to multple clusters vs. nondsunctve determnstc same clusters produced every tme or a gven data set vs. stochastc dstance smlarty measure used
7 Dstance/Smlarty Measures many clusterng methods employ a dstance smlarty measure to assess the dstance between a par o nstances a cluster and an nstance a par o clusters gven a dstance value t s straghtorward to convert t nto a smlarty value sm y + dst y not necessarly straghtorward to go the other way we ll descrbe our algorthms n terms o dstances
8 Dstance Metrcs propertes o metrcs dst 0 dst 0 dst dst dst some dstance metrcs dst + k dst k Manhattan Eucldean dst dst e e e e e e e ranges over the ndvdual measurements or and
9 Herarchcal Clusterng: A Dendogram heght o bar ndcates degree o dstance wthn cluster dstance scale 0 leaves represent nstances e.g. genes
10 Herarchcal Clusterng can do topdown dvsve or bottomup agglomeratve n ether case we mantan a matr o dstance or smlarty scores or all pars o nstances clusters ormed so ar nstances and clusters
11 Dstance Between Two Clusters the dstance between two clusters can be determned n several ways sngle lnk: dstance o two most smlar nstances dst c u c v { dst a b a c b c } mn complete lnk: dstance o two least smlar nstances dst c u c v { dst a b a c b c } ma average lnk: average dstance between nstances dst c u c v { dst a b a c b c } avg u u u v v v
12 CompleteLnk vs. SngleLnk Dstances complete lnk cv sngle lnk cv c u c u
13 Updatng Dstances Ecently we ust merged u and v nto we can determne dstance to each other cluster as ollows sngle lnk: dst c c complete lnk: k dst c c k c mn ma c c k { dst c c dst c c } u k { dst c c dst c c } u k c v v k k average lnk: dst c c k c u dst c u c c k u + + c c v v dst c v c k
14 Dendogram or Serum Stmulaton o Fbroblasts sgnalng & angogeness cell cyle cholesterol bosynthess
15 Parttonal Clusterng dvde nstances nto dsont clusters lat vs. tree structure key ssues how many clusters should there be? how should clusters be represented?
16 Parttonal Clusterng Eample
17 Parttonal Clusterng rom a Herarchcal Clusterng we can always generate a parttonal clusterng rom a herarchcal clusterng by cuttng the tree at some level cuttng here results n clusters cuttng here results n 4 clusters
18 KMeans Clusterng assume our nstances are represented by vectors o real values put k cluster centers n same space as nstances each cluster s represented by a vector consder an eample n whch our vectors have dmensons + + nstances + cluster center +
19 KMeans Clusterng each teraton nvolves two steps assgnment o nstances to clusters recomputaton o the means assgnment recomputaton o means
20 KMeans Clusterng: Updatng the Means or a set o nstances that have been assgned to a cluster we recompute the mean o the cluster as ollows µ c c c c
21 KMeans Clusterng gven : a set X {... n} o nstances select k ntal cluster centers... whle stoppng crteron not true do or all clusters c c or all means { dst < dst } µ c l do // determne whch nstances are assgned to ths cluster do // update the cluster center k l
22 Kmeans Clusterng Eample dst dst dst dst dst dst dst dst dst dst dst dst dst dst dst dst Gven the ollowng 4 nstances and clusters ntalzed as shown. Assume the dstance uncton s e e e dst
23 Kmeans Clusterng Eample Contnued assgnments reman the same so the procedure has converged
24 EM Clusterng n kmeans as ust descrbed nstances are assgned to one and only one cluster we can do sot kmeans clusterng va an Epectaton Mamzaton EM algorthm each cluster represented by a dstrbuton e.g. a Gaussan E step: determne how lkely s t that each cluster generated each nstance M step: adust cluster parameters to mamze lkelhood o nstances
25 Representaton o Clusters n the EM approach we ll represent each cluster usng an mdmensonal multvarate Gaussan where Σ Σ ep T m N µ µ π Σ µ s the mean o the Gaussan s the covarance matr ths s a representaton o a Gaussan n a D space
26 EM Clusterng the EM algorthm wll try to set the parameters o the Gaussans Θ to mamze the log lkelhood o the data X log lkelhood X Θ log log n n n Pr k k log N N
27 EM Clusterng the parameters o the model nclude the means the covarance matr and sometmes pror weghts or each Gaussan here we ll assume that the covarance matr and the pror weghts are ed; we ll ocus ust on settng the means Θ
28 EM Clusterng: the Estep z recall that s a hdden varable whch s generated and 0 otherwse n the Estep we compute the epected value o ths hdden varable h h N E z k N l l N assgnment
29 EM Clusterng: the Mstep gven the epected values we reestmate the means o the Gaussans µ n n h h can also reestmate the covarance matr and pror weghts we re varyng them h
30 EM and KMeans Clusterng both wll converge to a local mamum both are senstve to ntal postons means o clusters have to choose value o k or both
31 Evaluatng Clusterng Results gven random data wthout any structure clusterng algorthms wll stll return clusters the gold standard: do clusters correspond to natural categores? do clusters correspond to categores we care about? there are lots o ways to partton the world
32 Evaluatng Clusterng Results some approaches eternal valdaton E.g. do genes clustered together have some common uncton? nternal valdaton How well does clusterng optmze ntracluster smlarty and ntercluster dssmlarty? relatve valdaton How does t compare to other clusterngs usng these crtera? E.g. wth a probablstc method such as EM we can ask: how probable does heldasde data look as we vary the number o clusters.
33 Comments on Clusterng there many derent ways to do clusterng; we ve dscussed ust a ew methods herarchcal clusters may be more normatve but they re more epensve to compute clusterngs are hard to evaluate n many cases
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