バイオメトリックス第5回 03aiwata/biomet/...genechip êpm æmm& • p ê1hd é 7 6 Ð....
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060310391&0560565&
ȫȇȋȹȣȿȟȏȘ&ģ5[&ĎĂťÎ&
2014/5/15&&&8:40-10:10&@1Pƺ-ģ4ŵŀz�
&
1&
ŝĂbǦƘ�qǪƤǩǫ…&
ɑ�
ŝĂb&
Śĸƥ &DNAȶɆȌɆ&Ƙ�q&
Ă_ǥǫDžǀ1ƚǪ�ĭǶǁǩǹ&ƈǿǾǻnjǩǨǡǤǑǤNjǾ&
&
�=ǪİŦƘ�sĐȺȢȿɄȐ&ǥǫȰȾȟȏȴȟȏȘǦǘǤǑǞ�
2&
ʼǪȎɆɃɆȤ&• ȣȾɄȘȏȿȱȣɆȸťÎ&• ȶȇȏɂȅɁȇ&• Ʒ��Džžƭ&• ƨ�ĐȏȾȘȜȿɄȐDžƱƨ�ĐȏȾȘȜȿɄȐ&– Qĝƨ�ĐȏȾȘȜȿɄȐ¦îDžk-meansîDžő�ĭĻDȶȟȱɈSOMɉ&
• àÕsŁDž²��ǑsŁDž²�ǨǘsŁ&– ȕȵɆȣȲȏȜɆȶȖɄɈSVMɉ&
3&
ȚɄȣȾɀȤȐȶǦȋɆȸťÎ&
DNA&
RNA&
ȜɄȬȏź&
Ȓȩȸ&
ȣȾɄȘȏȿȱȣɆȸ&
ȱɂȡȋɆȸ&
Ɓ3&
łũ&
şŞ&
4&
ȣȾɄȘȏȿȱȣɆȸťÎ&
• Ą�Ĭň1ǩǎǔǾƘ�qĿǪĎĂĀíɈƘ�qǪƁ3ąþǥNJǾmRNAƟɉȂĴľĐǩôvɅťÎǚǾĘğ&
5&
h>p://www.scq.ubc.ca/spot-your-genes-an-overview-of-the-microarray/&&&&&&&&&&&&&Art&by&Jiang&Long&
ȘȵȟȣbȶȇȏɂȅɁȇ&
cDNAȂȱɂɆȰǦǘǤƛĽǚǾ&
2ĝƷǪċǨǡǞĬňɈØ�ɉȂSÀǩ6ÎǚǾ&
ř-ŕīǥÜŶ&ɈÜŶȜɆȒȟȣɉ&
2ĝǪÜŶȜɆȒȟȣȂĢRĐǩȪȇȰȿȝȇșǗǜǾ&
6&
cDNAȅɁȇǪȘȎȻɄĈ)Ǫ�&
ĎĂƟDŽ=DŽȕɄȱɀǪř-��DŽ/DŽ&ȓɄȣɂɆɀǪř-��&
h>p://www.promega.com/enotes/applicaRons/ap0066.htmǻǽ&
7&
GeneChip¨Ŝ&ȜɆȒȟȣǦǘǤ&1ĝƷǪĬňɈØ�ɉȂĆ£ǚǾ&
mRNA&&→&cDNA&&→&cRNA&
→&¸üDcRNA&
GeneChipȅɁȇǫ&20�25hdǪ&ȋȿȔȧȏɁȋȞȤǐnµƛĽǗǿǤ
NjǾ&
Ƙ�qåǩ10�20ĝƷǪȓȮɆǐƛĽǗǿǾ&
QȓȮɆǫ&Perfect&Match&(PM)ǦMismatch&
(MM)ǪȱɂɆȰǪȚȟȣǏǼ¥Ǿdž&MMǫPMǪ1hdȂĽ±ǘǤNJǾ&
h>p://www.scq.ubc.ca/spot-your-genes-an-overview-of-the-microarray/&&&&&&&&&&&&&Art&by&Jiang&Long&8&
GeneChipǪPMǦMM&
• pǪ1hdǩȷȘȶȟȞǐ.ǿǼǿǤNjǾ&• ĎĂƟǫDžPMǦMMǪř-��Ǫ�ɈǵǞǫæɉǩdǣNjǤŦĥǗǿǾ&
Schadt&et&al.&(2000)&J&Cell&Biochem&80:&192&
9&
Perfect&MatchɈPMɉǦMismatchɈMMɉ�
ȶȇȏɂȅɁȇȢɆȜťÎǩǎǔǾŴYƶ&
• ŏoǨȢɆȜ&ɈȕɄȱɀµǩædzǤƘ�qµǐ^$ĐǩnNjɈµG�µ�ɉɉ&– ȢɆȜǪŠĩDžţŤD&– nƞØvYƶ&– ôȺȢɀǩǎǔǾƑ�Ǩ�ǤǫǸ&
&• ȩȇș&– ȢɆȜǪâŢD&
• ǵǟǵǟƼ�&– yƻŦĈî&
����H?�.3�
10&
ĎĂȬȜɆɄǩdǣǓ&Ƙ�qǪ6Ʒ&
• ȏȾȘȜɆťÎ&– ƨ�ĐȏȾȘȜȿɄȐɈhierarchical&clusteringɉ&– Ʊƨ�ĐȏȾȘȜȿɄȐɈnon-hierarchical&clustering)&
ĒĐɍLjƘ�qSkǪĎĂȬȜɆɄǪƷ� Ȃ½ǼǏǩǚǾlj&
– Ʒ�ǘǞĎĂȬȜɆɄȂęǚƘ�qĿǫDžǝǿǼǐĄ�Ĭň1ǥàʼnĐǩƷ�ǘǤNjǾǏDžǹǘǓǫƥ ǐðNjǹǪǥNJǾǖǦǐÇ�ǗǿǾ&
→&àʼnǐÉĖǪƘ�qǩǢNjǤDžSǙȐɀɆȱǩ�ǚǾƘ�qĿǪ»ĖàʼnǩƷ�ǘǞàʼnȂǹǢǪǥǫǨNjǏǦNjnj¯ôǐNʼnǦǨǾ&
&&
11&
ĎĂȬȜɆɄǪƷ��Ǧžƭ&ȕɄȱɀ1$ ȕɄȱɀ2$ ȕɄȱɀ3$ ȕɄȱɀ4$
Ƙ�q1& 1.53& 2.38& 2.80& 0.60&
Ƙ�q2& 1.03& 2.54& 3.29& 0.80&
Ƙ�q3& 0.85& 0.21& 0.34& 3.02&
Ƙ�q4& 1.03& 0.82& 0.94& 1.20&
ȕɄȱɀƤǪƔNj:&�ǍǬDžyƻÌ�DžĭĻDžĄņȘȡɆȗDžÀƤǨǧǐċǨǾ&
1.0& 0.95& -0.92& -0.88&
0.95& 1.0& -0.74& -0.77&
-0.92& -0.74& 1.0& 0.93&
-0.88& -0.77& 0.93& 1.0&
Ĕƥ& ĔƥǪı}%&1.0& 0.95& 0.92& 0.88&
0.95& 1.0& 0.74& 0.77&
0.92& 0.74& 1.0& 0.93&
0.88& 0.77& 0.93& 1.0&
0.0& 0.74& 4.13& 2.55&
0.74& 0.0& 4.36& 0.77&
4.13& 4.36& 0.0& 2.02&
2.55& 2.94& 2.02& 0.0&
ȽɆȏȿȟȤžƭ&
(ƱƷ��ɉ&
r =(xi − x
i=1
n
∑ )(yi − y )
(xi − xi=1
n
∑ )2 (yi − yi=1
n
∑ )2
L<�XEP%��$���>W%���#��������AC�$��"��$�
Ĕƥ µɍ�
12&
13&
1
1
1
1
1.0 1.5 2.0 2.5 3.0 3.5 4.0
0.5
1.0
1.5
2.0
2.5
3.0
Sample ID
Exp
ress
ion
leve
l
2
2
2
23
3 3
3
44 4
4
ƨ�ĐȏȾȘȜȿɄȐ&• }ŷƤǪžƭǩdǣNjǤDžžƭǪƆNjǹǪǧnjǘȂ1ǢǪȐɀɆȱɈȏȾȘȜɉǩƵáǵǦǸǤNjǓºîdžĄ¥ǗǿǞȏȾȘȜǐǗǼǩ��ǪȏȾȘȜǩǵǦǸǼǿǤNjǓǞǸDž6ƷįÏǫƨ�ÚƍȂǹǢǹǪǦǨǾ&
• ƨ�ÚƍǫDždendrogramɈÞ�]ɉǦǻǬǿǾ]ǩǻǡǤţŤDǗǿǾ&
• ȏȾȘȜƤǪžƭǪvŀǪ�ºǐċǨǾDžşµǪºîǐNJǾ&
• ºîǪƔNjDžžƭǪ��ǪƔNjǩǻǡǤįÏǐċǨǾ&
ĨİÞǥǨNjǖǦǩï£ɇɇ�
14&
ÛLJǨžƭǪvŀ�
d(xi,x j ) = (xi1 − x j1)2 ++ (xip − x jp )
2
xi = (xi1,…, xip ), x j = (x j1,…, x jp )
ȽɆȏȿȟȤžƭ&(Euclidean&distance)�
d(xi,x j ) = xi1 − x j1 ++ xip − x jp
ȷɄȓȯȘȎɆžƭ&(Minkowski&distance)�
ȽɆȏȿȟȤžƭǪ�œD�
d(xi,x j ) =max xi1 − x j1 ,…, xip − x jp{ }Äožƭ&(Maximum&distance)�
Q¥6ǪÄo%�
d(xi ,x j ) = xi1 − x j1
p++ xip − x jp
p1/p
ÔqĀǪƒǩìǡǤĜBǘǞÀǪžƭ�
ȶɄȪȟȜɄžƭ&(Manha>an&distance)�
15&
p&=&1ǪǦǑDžȶɄȪȟȜɄžƭ&p&=&2ǪǦǑDžȽɆȏȿȟȤžƭ&p&→&∞ǪǦǑDžÄožƭ�
d(xi,x j ) =xi1 − x j1xi1 + x j1
++xip − x jpxip + x jp
Kø�ƆǪƔNjȂ�ŲǚǾǻnjǩȶɄȪȟȜɄžƭȂ"â�
ȎȻɄȲȾžƭ&(Canberra&distance)�
ƨ�ĐȏȾȘȜȿɄȐǪȅɀȔȿșȸ&
1. /Ƙ�qƤǥžƭȂŦĥǚǾ&2. �ĊžƭǪƆNjȳȅɈƘ�qǵǞǫȏȾȘȜɉȂǵǦǸǤ1ǢǪȏȾȘȜǦǚǾ&
3. ¹ǞǩĄ¥ǗǿǞȏȾȘȜǦ�ǪŠīɈƘ�qǵǞǫȏȾȘȜɉƤǪžƭȂŦĥǚǾ&
4. /ǤǪƘ�qǐ1ǢǪȏȾȘȜǩǵǦǵǾǵǥ2-3ȂļǽƇǚ&
16&
0 1 2 3 4 5
01
23
45
Expression level in Exp.1
Expre
ssio
n level in
Exp.2
ƨ�ĐȏȾȘȜȿɄȐ&–&]ť&
Ƙ�q1&
Ƙ�q2&
Ƙ�q3&Ƙ�q4&
Ƙ�q5&ȏȾȘȜ1&
ȏȾȘȜ2&
ȏȾȘȜ3&
ȏȾȘȜ4&
ȏȾȘȜ1&
gene 1
gene 2
gene 5
gene 3
gene 4
0.5
1.0
1.5
2.0
2.5
3.0
Dendrogram
hclust (*, "average")
Distance
ȏȾȘȜ2&
ȏȾȘȜ3&
ȏȾȘȜ4&
�ǍǬDžǖǪžƭȂƦ%ǦǚǾǖǦǥ2Ŀǩ6ǔǾǖǦǐǥǑǾ&17&
ȏȾȘȜƤǪžƭǪvŀ(1)&1. ÄƆƪîɈnearest&neighbor&methodɉ&&
a.k.a.&ÄėžƭîDžIƎįɈsingle&linkageɉî&
2.&&&ÄƕƪîɈfurthest&neighbor&methodɉ&&a.k.a.&ÄơžƭîDžt/ƎįɈcomplete&linkageɉî&
ȏȾȘȜA&ȏȾȘȜB&
ÄǹƆNj}ŷƤǪžƭ&
ÄǹƕNj}ŷƤǪžƭ&
18&
ȏȾȘȜƤǪžƭǪvŀ(2)&3.&&&&Ŀ�aîɈgroup&average&methodɉƿ&
2.&&&ƞ�îɈcentroid&methodɉƿ&
×&×&
/ĭRǜƤǪ�ažƭ&
/ĭRǜǪ�aȂǦǾ&
QȏȾȘȜǪƞ�ƤǪžƭ&&ƞ�ȂéǸǾƩǩǫȏȾȘȜǩ&TǵǿǾ}ŷǪµǐL¾ǗǿǾǻ&njǩDž}ŷǪµȂƞǶǦǘǤĆNjǾ&
19&
ȏȾȘȜƤǪžƭǪvŀ(3)&4.&&&&ȹȢȆȅɄîɈmedian&methodɉƿ&
5.&&&ȈȊɆȤîɈWard’s&methodɉƿ&
×&×&
ƞ�îǪlî&&žƭȂéǸǾƩǩDž}ŷǪµǥ&ƞǶȂ�ǔǨNj&
ƙ16³ǦĿƤ6³ǪæȂ&ÄoDǗǜǾdöǥȏȾȘȜɆȂ&�¥ǚǾ&
×&
×&
×&
ȹȢȆȅɄî&
ƞ�î&
×&×&
20&d(A,B)(=(E(A(�(B)(+(E(A)(+(E(B)�
¦îǩǻǾƔNj&
0 1 2 3 4 5
01
23
45
Expression level in Exp.1
Exp
ress
ion
leve
l in
Exp
.2
ge
ne
3
ge
ne
4
ge
ne
5
ge
ne
1
ge
ne
20.5
1.0
1.5
2.0
2.5
3.0
Complete linkage
hclust (*, "complete")
Distancegene1&
gene2&gene5&
gene3&gene4&
gene 1
gene 2
gene 5
gene 3
gene 4
0.8
1.0
1.2
1.4
1.6
1.8
Single linkage
hclust (*, "single")
Distance
¦îǪƔNjǩǻǡǤDžÐơǟǔǥǨǓDžgRǩǻǡǤǫȣȵɂȗɆɈ®IJƥ ɉǹċǨǾȢɄȤɂȐȾȸɈdendrogram&Þ�]ɉ
ǐ�ǼǿǾgRǐNJǾDŽ→DŽĿ6ǔǐċǨǡǤǓǾ& 21&
žƭǪvŀǩǻǾƔNj�
• ȢɆȜǪ źǩ�ǙǤƖx�Nj6ǔǾ�ŠǐNJǾ�
gene3
gene4
gene5
gene1
gene2
0.8
1.2
1.6
Euclidean distance
hclust (*, "single")dist(c, method = "euclidian")
Height
gene3
gene4
gene5
gene1
gene2
0.6
1.0
1.4
Maximum distance
hclust (*, "single")dist(c, method = "maximum")
Height
gene3
gene4
gene5
gene1
gene2
1.2
1.6
2.0
Manhattan distance
hclust (*, "single")dist(c, method = "manhattan")
Height
gene1
gene2
gene5
gene3
gene40.15
0.25
0.35
Canberra distance
hclust (*, "single")dist(c, method = "canberra")
Height
22&
Qžƭ��Ǫÿ�ȂăťǘǤ&;ĆǚǾǖǦǐƞŠ&
• ȽɆȏȿȟȤžƭǫDžĎĂƟǪlBǪoǑǨƘ�qǩoǑǨƞǶǐǍǼǿǾ&→DŽYƶǐNJǾgRǫâŢDǚǾ&
• Ĕƥ µǩdǣǓžƭɈ1-rijǵǞǫ1-|rij|ɉǫDžQƘ�qǪĎĂƟǪlBǪo~ǩ�ǡ�ǼǿǾǖǦǫǨNjǐDžmǿ%ǩ}ǚǾ¤M ǐƼǓDžÿǩDžȕɄȱɀǐ�ǨNjgRǩ�ƳǐoǑNj&
23&
ƖĆ�&(1)&
• cƹǗǿŚòǩǻǾ<÷ȂǍǼǿǞȭȣǪCđĺijŗĬňǩǎǔǾƘ�qĎĂ&
• 4ă�0,& 15,& 306Dž1,& 2,& 3,&4,&8,&12,&16,&20,&24ÀƤ�ǩŦô&
• 06ÀǪȢɆȜǩ}ǚǾĔ}ĐǨĎĂƟȂŪ�&
• »ĖǪƘ�qǐnǓTǵǿǾȏȾȘȜǹNJǡǞǐDžSǙȏȾȘȜǩÿ ǐ6ǏǡǤNjǨNjƘ�qǹTǵǿǤNjǞ&
Eisen&et&al.&(1998)&PNAS&95:&14863&Cholesterol&biosynthesis&
Cell&cycle&(ĬňUÇ)&
Immediate-early&response&
Signaling&&&angiogenesis&&(ŚĦ�¥)&
Wound&healing&&&Tissue&remodeling&
→DŽÉĖǪƘ�qǪàʼnȂ¯ôNʼn� 24&
SǙȢɆȜǥǹšºȂlǍǾǦ…&ɈȢɆȜȂƁĽǚǾǦ…ɉ�
Ƙ�q1$ Ƙ�q2$ Ƙ�q3$ Ƙ�q4$
ȕɄȱɀ1& 1.53& 1.03& 0.85& 1.03&
ȕɄȱɀ2& 2.38& 2.54& 0.21& 0.82&
ȕɄȱɀ3& 2.80& 3.29& 0.34& 0.94&
ȕɄȱɀ4& 0.60& 0.80& 3.02& 1.20&
→DŽȕɄȱɀƤǪƷ� ǺžƭǹŦĥǥǑǾ�
25&
ƖĆ�&(2)&
• ȕɄȱɀɈ60ǪcƹĬňÓ&cell&linesɉǩ}ǘǤǹƨ�DȏȾȘȜȿɄȐȂśnj&
• ȕɄȱɀɈǖǖǥǫŌčĬňǐćÍǚǾZuɉƤǪƷ� ȂţŤD&
Ross&et&al.&(2000)&Nat&Genet&24:&227&
26&
ŋŐǐȃ� oōǐȃ�J�ǐȃ� ďŚČ� ƾŕŌ�ÑěĮĨ�
�ǐȃ�=ġŎǐȃ�
Ʊ~ĬňŇǐȃ�
Ʊƨ�ĐȏȾȘȜȿɄȐ&
• ƨ�ÚƍȂǢǓǼǛǩDž«vǘǞµǪȏȾȘȜǩēǩ6ƷǚǾ&
• k-meansȏȾȘȜȿɄȐ&• ő�ĭĻDȶȟȱ&(Self-organizing&mapsɍ&SOMɉ&
27&
k-meansȏȾȘȜȿɄȐǪȅɀȔȿșȸ&
1. k#ǪȏȾȘȜ�ǦǘǤDžk#ǪȕɄȱɀȂú�ùǩƗǮǟǚ&
2. ǚdzǤǪȢɆȜøǦk#ǪȏȾȘȜ�ƤǪžƭȂéǸDžQȢɆȜøȂ�ǐÄǹƆNjȏȾȘȜǩ6ƷǚǾ&
3. �¥ǗǿǞȏȾȘȜǪ�Ȃ¹ǚǾ&4. ȏȾȘȜǪ�ǐlDǘǨǓǨǾǵǥDž2-3ȂļǽƇǚ&
89�*726�&6)5+4���
28&
k-meansȏȾȘȜȿɄȐ&–&]ť&
Bishop&CM&(2006)&Pa>ern&recogniRon&and&machine&learning.&Springer.&ï£ɍǖǖǥǫÄ9ǩȏȾȘȜǪ�ȂȕɄȱɀǏǼƗȃǥNjǨNj� 29&
şµǪȐɀɆȱDzǪ6Ʒ�• ȢɆȜ�ɍȇȨǪƘ�Ÿõ386ĨİDž1311SNPs&• k−�aîǥ5Ŀǩ6Ʒ&• �¥6Ƃ�ǥDž6ƷįÏȂęǚ&
30&
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−20 −10 0 10 20
−10
010
20
Rep 0
PC1
PC2
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−10 −5 0 5 10 15
−10
−50
510
1520
Rep 0
PC3
PC4
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−20 −10 0 10 20
−10
010
20
Rep 1
PC1
PC2
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−10 −5 0 5 10 15
−10
−50
510
1520
Rep 1
PC3
PC4
×DŽǫƞ�Ǫ&�ĽȂęǚ�
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−20 −10 0 10 20
−10
010
20
Rep 2
PC1
PC2
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−10 −5 0 5 10 15
−10
−50
510
1520
Rep 2
PC3
PC4
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−20 −10 0 10 20
−10
010
20
Rep 10
PC1
PC2
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−10
−50
510
1520
Rep 10
PC3
PC4
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