visualizing microarray dataweb.math.ku.dk/~richard/courses/bioconductor2009/...1 elements of data...
TRANSCRIPT
Visualizing microarray data
Laurent Gautier
August 18th, 2009
Loading and installing more packages
> l ibrary ( ”<package>”)> # if no such package
> source ( ”http : //www.bioconductor.org/b iocL i t e .R ”)> b i o cL i t e ( ”<package>”)> l ibrary ( ”<package>”)
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1 Elements of data visualization
1.1 Shapes and screen resolution
Screen resolution
� only one dot displayed per pixel
� there a relatively small number of different geomtric shapes a viewer candistinguish on a plot
�
Overplotting Plain plot
~300, 000 data points are represented on a 500× 500 image.
Overplotting Alpha blending
2
Sampling
30, 000 points are sampled out of the 300, 000.
Binning
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m[,
2]
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Counts
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Smooth scatter plot
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m[, 1]
m[,
2]
1.2 Colors
� If only two colors, avoid the two color-blind people can distinguish (redand green are one notorious example)
� Most of the people can only keep track of around a dozen different colors(or tone differences) on one plot
4
ColorBrewer’s palettes
BrBGPiYG
PRGnPuOrRdBuRdGyRdYlBu
RdYlGnSpectral
AccentDark2Paired
Pastel1Pastel2
Set1Set2Set3
BluesBuGnBuPuGnBu
GreensGreys
OrangesOrRdPuBu
PuBuGnPuRd
PurplesRdPuRedsYlGn
YlGnBuYlOrBrYlOrRd
� People generally compare better lengths than areas
Blueberry
Cherry
Apple
Boston Cream
Other
Vanilla Cream
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pie chart vs barplot
Blueberry
Cherry
Apple
Boston Cream
Other
Vanilla Cream
Blueberry Cherry Apple Boston Cream Other Vanilla Cream
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1.3 basic R objects
R objects (some of the)
1.4 basic R plots
plot
> x ← rnorm(50)> plot ( x )
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Index
x
Histogram
> hist ( x )
Histogram of x
x
Fre
quen
cy
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Density estimate
> plot (density ( x ) )
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density.default(x = x)
N = 50 Bandwidth = 0.367
Den
sity
> y ← 2*x + rnorm(50 , sd=0.3 )> plot (x , y )
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> mycolors ← rep ( ”black ” , length ( x ) )> mycolors [ x < 0 | y < 0 ] ← ”red ”> plot (x , y , col = mycolors )
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1.5 Lattice plots
FormulaeFormulae are used to describe a model, generally for the purpose of fitting
or plotting.
y ~ xExample:weight ~ age
y ~ x + zExample:weight ~ age + gender
y ~ x | zExample:weight ~ age | gender
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Storing data in data.frame
> data ( chickwts )> head( chickwts )
weight f e ed1 179 horsebean2 160 horsebean3 136 horsebean4 227 horsebean5 217 horsebean6 168 horsebean
> hist ( chickwts$weight )
Histogram of chickwts$weight
chickwts$weight
Fre
quen
cy
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1015
Using lattice
> l ibrary ( l a t t i c e )> p ← histogram ( ∼ weight ,+ data = chickwts )> print (p)
weight
Per
cent
of T
otal
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> p ← histogram ( ∼ weight | feed ,+ data = chickwts )> print (p)
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weight
Per
cent
of T
otal
0
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50
100 200 300 400
casein horsebean
100 200 300 400
linseed
meatmeal
100 200 300 400
soybean
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sunflower
weight
Per
cent
of T
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0
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40
50
100 200 300 400
casein horsebean
100 200 300 400
linseed
meatmeal
100 200 300 400
soybean
0
10
20
30
40
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sunflower
> p ← den s i t yp l o t ( ∼ weight , groups = feed ,+ data = chickwts ,+ auto .key = TRUE)> print (p)
weight
Den
sity
0.000
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0.010
0.015
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caseinhorsebeanlinseedmeatmealsoybeansunflower
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1.6 ggplot2
Using ggplot2
> l ibrary ( ggp lot2 )> p ← ggp lot ( chickwts ) ++ aes (x = weight , col=feed ) ++ geom density ( )> print (p)
weight
dens
ity
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
150 200 250 300 350 400
feed
casein
horsebean
linseed
meatmeal
soybean
sunflower
Density estimates
> p ← ggp lot ( chickwts ) ++ aes (x = weight ) ++ geom density ( ) ++ facet wrap ( ∼ f e ed )> print (p)
weight
dens
ity
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.000
0.002
0.004
0.006
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0.010
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casein
meatmeal
150 200 250 300 350 400
horsebean
soybean
150 200 250 300 350 400
linseed
sunflower
150 200 250 300 350 400
Boxplot
> data ( sleep )> p ← ggp lot ( sleep ) ++ aes (x = factor ( group ) , y = extra ) ++ geom boxplot ( )> print (p)
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factor(group)
extr
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1 2
ExpressionSet objects
eset[i, j] subset the matrix with its associated data.frames
> l ibrary ( go lubEsets )> data ( Golub Merge )> e s e t ← Golub Merge> p ← ggp lot ( pData ( e s e t ) ) ++ aes (x = PS) ++ geom histogram ( ) ++ facet wrap ( ∼ Gender , ncol = 3)> print (p)
PS
coun
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F
0.2 0.4 0.6 0.8 1.0
M
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NA
0.2 0.4 0.6 0.8 1.0
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> exprs ( e s e t ) [ exprs ( e s e t ) ≤ 0 ] ←+ min( exprs ( e s e t ) [ exprs ( e s e t ) > 0 ] )> l ibrary ( limma )> model ← model.matrix ( ∼ pData ( e s e t )$ALL.AML )> f i t ← lmFit ( log2 ( exprs ( e s e t ) ) , model)> t f i t ← t r e a t ( f i t , l f c =1)> t t ← topTreat ( t f i t , coef=2, number=50)> head( t t )
ID logFC AveExpr t P.Valuead j .P .Va l2288 M84526 at 9 .819500 4 .245026 11 .641353 2 .030486e−18 1 .447533e−141882 M27891 at 7 .449327 7 .545176 7 .692992 3 .117432e−11 1 .111209e−073252 U46499 at 4 .664273 7 .019749 7 .224403 2 .283971e−10 5 .427476e−07760 D88422 at 3 .998790 7 .925139 6 .578039 3 .467697e−09 5 .671084e−061834 M23197 at 2 .258431 8 .058312 6 .545160 3 .977475e−09 5 .671084e−066378 M83667 rna1 s at 6 .876969 4 .061439 6 .463448 5 .590043e−09 6 .641903e−06
> plot ( exprs ( e s e t ) [ 1 , ] )
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Index
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Having data in data.frame
data.frame as a workhorse
� Have it one variable per column
� Split tell-it-all names into separate variables
� Really, no variable value such as mutant drugA 231209 grumpy (insteadhave 4 columns strain, treatment, experiment date, experimentalist mood
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Converting a wide data structure into a long one
> l ibrary ( reshape )> myids ← t t$ID [ 1 : 1 0 ]> data f ← melt ( exprs ( e s e t ) [ myids , ] ,+ varnames = c ( ”symbol ” , ”sample ” ) )> data f ← merge( dataf , pData ( Golub Merge ) ,+ by.x = ”sample ” , by.y = 0)> str ( data f )
' data . f rame ' : 720 obs . o f 14 v a r i a b l e s :$ sample : i n t 1 1 1 1 1 1 1 1 1 1 . . .$ symbol : Factor w/ 10 levels ”D88422 at ” , ”M11722 at ” , . . : 8 5 10 1 4 7 9 3 2 6 . . .$ value : num 1 303 44 161 261 . . .$ Samples : i n t 1 1 1 1 1 1 1 1 1 1 . . .$ ALL.AML : Factor w/ 2 levels ”ALL” , ”AML” : 1 1 1 1 1 1 1 1 1 1 . . .$ BM.PB : Factor w/ 2 levels ”BM” , ”PB” : 1 1 1 1 1 1 1 1 1 1 . . .
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$ T .B . c e l l : Factor w/ 2 levels ”B−cell ” , ”T−cell ” : 1 1 1 1 1 1 1 1 1 1 . . .$ FAB : Factor w/ 4 levels ”M1” , ”M2” , ”M4” , . . : NA NA NA NA NA NA NA NA NA NA . . .$ Date : Factor w/ 27 levels ”” , ”1/24/1984 ” , . . : 26 26 26 26 26 26 26 26 26 26 . . .$ Gender : Factor w/ 2 levels ”F” , ”M” : 2 2 2 2 2 2 2 2 2 2 . . .$ pc tB la s t s : i n t NA NA NA NA NA NA NA NA NA NA . . .$ Treatment : Factor w/ 2 levels ”Fa i l u r e ” , ”Success ” : NA NA NA NA NA NA NA NA NA NA . . .$ PS : num 1 1 1 1 1 1 1 1 1 1 . . .$ Source : Factor w/ 4 levels ”CALGB” , ”CCG” , . . : 3 3 3 3 3 3 3 3 3 3 . . .
ggplot2
> l ibrary ( ggp lot2 )
New school plots
> p ← ggp lot ( data f ) ++ aes (x=ALL.AML, y=log2 ( va lue ) ) ++ geom point ( ) ++ facet wrap (∼symbol)> print (p)
ALL.AML
log2
(val
ue)
02468
101214
02468
101214
02468
101214
D88422_at
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ALL AML
> p ← ggp lot ( data f ) ++ aes (x=ALL.AML, y=log2 ( va lue ) , col=Source ) ++ geom point ( ) ++ facet wrap (∼symbol)> print (p)
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ALL.AML
log2
(val
ue)
02468
101214
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D88422_at
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Source
● CALGB
● CCG
● DFCI
● St−Jude
16
Anatomy of an heatmap
� central matrix of array values
� hierarchical clustering on row values
� hierarchical clustering on column values
Heatmap Iconic graphics for expression data
� A heatmap is not mandatory for all and every analysis of microarray data
� Every single pattern in a heatmap is not gold.
� Staring at heatmap diagrams can cause serious apophenia.
� Assessing the validity of clusters otherwise than visually is a good idea.
� Yet heatmaps can be useful.
?heatmap
About distance measures
� Dissimilarities a generalization of distances
� Measure how dissimilar entities are
� Hierarchical clustering depends on dissimilarity measure
Two dissimilarity measures, two outcomes
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> l ibrary ( b ioDi s t )
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� heatmap (stats)
� heatmap.2 (gplots)
� heatmap_2, heatmap_plus (Heatplus)
� circularmap, heatmapl (NeatMap)
Express ionSet ( storageMode : lockedEnvironment )assayData : 7129 f ea tu r e s , 72 samples
element names : exprsphenoData
sampleNames : 39 , 40 , . . . , 33 (72 t o t a l )varLabe l s and varMetadata description :
Samples : Sample indexALL.AML: Factor , i n d i c a t i n g ALL or AML. . . : . . .Source : Source o f sample(11 t o t a l )
featureDatafeatureNames : AFFX−BioB−5 at , AFFX−BioB−M at, . . . , Z78285 f at
(7129 t o t a l )f va rLabe l s and fvarMetadata description : none
experimentData : use ' experimentData ( ob j e c t ) '
pubMedIds : 10521349Annotation : hu6800
' data . f rame ' : 72 obs . o f 11 v a r i a b l e s :$ Samples : i n t 39 40 42 47 48 49 41 43 44 45 . . .$ ALL.AML : Factor w/ 2 levels ”ALL” , ”AML” : 1 1 1 1 1 1 1 1 1 1 . . .$ BM.PB : Factor w/ 2 levels ”BM” , ”PB” : 1 1 1 1 1 1 1 1 1 1 . . .$ T .B . c e l l : Factor w/ 2 levels ”B−cell ” , ”T−cell ” : 1 1 1 1 1 1 1 1 1 1 . . .$ FAB : Factor w/ 4 levels ”M1” , ”M2” , ”M4” , . . : NA NA NA NA NA NA NA NA NA NA . . .$ Date : Factor w/ 27 levels ”” , ”1/24/1984 ” , . . : 1 13 NA 27 9 NA NA NA 4 4 . . .$ Gender : Factor w/ 2 levels ”F” , ”M” : 1 1 1 2 1 2 1 1 1 2 . . .$ pc tB la s t s : i n t NA NA NA NA NA NA NA NA NA NA . . .$ Treatment : Factor w/ 2 levels ”Fa i l u r e ” , ”Success ” : NA NA NA NA NA NA NA NA NA NA . . .$ PS : num 0 .78 0 .68 0 .42 0 .81 0 .94 0 .84 0 .99 0 .66 0 .97 0 .88 . . .$ Source : Factor w/ 4 levels ”CALGB” , ”CCG” , . . : 3 3 3 3 3 3 3 3 3 3 . . .
18
Histogram of exprs(Golub_Merge)
exprs(Golub_Merge)
Fre
quen
cy
−20000 0 20000 40000 60000
010
0000
2500
00
package stats
> m ← exprs ( Golub Merge )> dim(m)
[ 1 ] 7129 72
> s p l i ← order (apply (m, 1 , var ) ) [ 1 : 3 0 0 ]> m ← m[ s p l i , ]> heatmap(m, labRow=””)
54 58 64 8 17 27 44 45 47 56 39 49 24 3 53 33 1 43 62 65 59 23 57 21 34 36 60 35 40 7 4 6 51 55 52 12 29 19 67 22 66 41 70 46 38 50 30 37 61 9 63 28 2 68 14 16 20 15 69 71 13 5 32 31 42 25 26 18 11 72 10 48
package stats
> exprs brk ←+ c ( quantile (m[m < 0 ] , seq (0 , 1 , length=5)) ,+ 0 ,+ quantile (m[m > 0 ] , seq (0 , 1 , length=5)))> a l l am l ← as.integer ( pData ( Golub Merge )$ALL.AML)> mycol ← brewer .pa l (2 , ”Set1 ” ) [ a l l am l ]
19
> heatmap(m, labRow = ”” ,+ col = brewer .pa l (10 , ”RdBu”) ,+ breaks = exprs brk ,+ ColS ideColors = mycol )
54 58 64 8 17 27 44 45 47 56 39 49 24 3 53 33 1 43 62 65 59 23 57 21 34 36 60 35 40 7 4 6 51 55 52 12 29 19 67 22 66 41 70 46 38 50 30 37 61 9 63 28 2 68 14 16 20 15 69 71 13 5 32 31 42 25 26 18 11 72 10 48
54 58 64 8 17 27 44 45 47 56 39 49 24 3 53 33 1 43 62 65 59 23 57 21 34 36 60 35 40 7 4 6 51 55 52 12 29 19 67 22 66 41 70 46 38 50 30 37 61 9 63 28 2 68 14 16 20 15 69 71 13 5 32 31 42 25 26 18 11 72 10 48
package gplots
> l ibrary ( gp l o t s )> heatmap.2 (m, labRow=”” ,+ symbreaks = TRUE,+ col = brewer .pa l (10 , ”RdBu”) ,+ trace = ”none ” ,+ ColS ideColors = mycol )
20
54 58 64 8 17 27 44 45 47 56 39 49 24 3 53 33 1 43 62 65 59 23 57 21 34 36 60 35 40 7 4 6 51 55 52 12 29 19 67 22 66 41 70 46 38 50 30 37 61 9 63 28 2 68 14 16 20 15 69 71 13 5 32 31 42 25 26 18 11 72 10 48
−200 0 200Value
0Color Key
and HistogramC
ount
package Heatplus
> l ibrary ( Heatplus )> addvar ← pData ( Golub Merge ) [ c ( ”Gender ” , ”ALL.AML” ) ]> heatmap plus (m,+ breaks = exprs brk ,+ col=brewer .pa l (10 , ”RdBu”) ,+ addvar = addvar )
54 58 64 8 17 27 44 45 47 56 39 49 24 3 53 33 1 43 62 65 59 23 57 21 34 36 60 35 40 7 4 6 51 55 52 12 29 19 67 22 66 41 70 46 38 50 30 37 61 9 63 28 2 68 14 16 20 15 69 71 13 5 32 31 42 25 26 18 11 72 10 48
M58569_s_atX13589_atL17325_atX95425_s_atU18991_atS62907_s_atU18297_s_atAB005535_s_atM10950_cds2_atX86564_atU92459_atX82835_atY08564_atU12535_atX94629_atHG2743−HT3926_s_atL27624_s_atU70981_atL14542_atU31973_s_atU44105_atS62027_s_atD63882_s_atU20860_atX83929_s_atL09234_atZ78285_f_atM64554_rna1_atU24577_atHG4245−HT4515_atD64108_atK02402_atL05597_atU39231_atU62015_atL32140_atX95654_atU68031_atD13264_atU13913_s_atX83107_atU79304_atU49516_atY00317_atY10202_atU73330_atU37529_atZ83800_atU62434_atHG2564−HT2660_s_atU95626_rna3_atX82629_atX58987_atZ70723_atAC000062_atS76853_s_atL36642_atX54925_atU49379_atD87458_atX66360_atU88871_atL41349_atU28015_atD38462_atJ00209_f_atV00551_f_atL14430_atX14894_atU07856_atU56102_atU38810_atX15943_atL76627_atJ00219_s_atX56088_s_atZ78290_atJ04156_atX78578_atU17033_atL27559_s_atD64053_atU65002_atX97675_rna1_atU92458_atX64877_s_atZ48510_atS75313_atU87309_atX76342_atU46116_atHG3431−HT3616_s_atU35407_atU87460_atZ83806_atX95239_atM14648_atM63896_atL34081_atD38503_atX71661_atU89012_atY10510_atM38180_rna1_atU22322_s_atX75958_atU17034_atU14407_atM55419_atX55330_atZ46629_atD14822_atD14497_atM16474_s_atL41913_atM14123_xpt1_atM73239_s_atU47007_atL40396_atY10517_atX89426_atM61916_atU68727_atU62432_atX98330_atM19154_atX51405_atU63332_atX65663_atAC000066_atD10922_s_atM14306_atU65437_rna1_atX52001_atL11573_atU13896_atD37965_atJ04970_atU68133_atU39196_atX82018_atD42072_atU35376_atU17032_atU30245_atHG2160−HT2230_atHG2007−HT2056_s_atU91521_atD26561_cds3_atM26167_rna1_atX51730_atD86980_atU79300_atU43328_atX51823_s_atL49218_f_atU58033_atU22815_atU79249_atX81895_atX95237_atM18533_atL75847_atU19495_s_atX76534_atU11821_s_atD26561_cds1_atX00949_atU52155_atL26953_atX76383_atS79862_s_atU79246_atX16901_atY07596_atL22650_atM30773_atX65233_atD13168_atU42359_atM82919_atS78693_f_atJ04513_atU57093_atU69108_atS73205_atX62429_s_atD00408_atM16801_atU66561_atL07077_atS81661_s_atM37981_atM91556_s_atD82347_atM81882_atL08485_atM33478_atX54150_atY08136_atU16129_atS57887_atU68135_s_atU00001_s_atAFFX−LysX−5_atM31423_s_atL32163_atL25286_s_atHG3513−HT3707_atM86808_atD13644_atX97671_atX06290_atX71125_atX06661_atU93091_atL19778_atL07949_atX84003_atHG2841−HT2970_atU26712_atU18985_atU21128_atL07615_atX64643_atU12778_atY08319_atL47726_atL24470_atHG429−HT429_atU12897_atX63597_atX15422_atX77922_s_atV00503_atZ95624_atU54617_atU33267_atX78926_atM60828_atX82153_atX02956_atX58723_atL12468_atM16714_atM60503_atZ28339_atM84605_atX66087_atM55418_atZ75330_atM63623_atX07820_atZ24725_atU03886_atD31784_atX78686_atU19906_atX98266_cds2_atU59914_atM93119_atM31241_s_atS52028_s_atY09615_atD38122_atY10571_atU10886_atU66726_s_atL21934_atJ03810_atU66497_atS58544_atX59710_atJ05096_rna1_atU33632_atM25393_atM19301_atZ83802_atX00540_atU22233_atL40157_atV00571_rna1_atX98337_s_atL25441_atM65290_atL46353_atD76435_atM54927_atZ48570_atX64810_atL29306_s_atM62424_atX84195_atS82592_atU96136_atY07512_atU01828_atS66896_atX05608_atU09279_at
Gender
ALL.AML
21
2 Visualizing data on the genome
� contextual information
� measured entities have respective positions on the genome
� the reference genome is an abstraction
2.1 Idiogram
> l ibrary ( idiogram )
> l ibrary ( go lubEsets )> data ( Golub Train )> human chr ← buildChromLocation ( ”hu6800 ”)> expr vec to r ←+ assayData ( Golub Train ) [ [ ”exprs ” ] ] [ , 1 ]> id iogram ( expr vector , human chr , chr=”1 ”)
●●● ●●●● ● ●●●● ●● ●●●●●● ● ●● ●●●●● ●●●●●●●●● ●●●● ●●●●●●● ●●●●●● ●●●● ●●●● ●●●●●● ● ●●●●● ● ●●●● ●●●●● ●●● ●●●●● ●● ●● ●●● ● ●●●●●●●● ●●●● ●●●● ●●●● ●● ●● ●●●● ●● ● ●●●●●●●●●●● ●●● ●●●●●●●●● ●● ●●●●● ●●●●● ●●● ●● ● ●●●●●●● ●●●●●●●●●●● ●●●●●●●●● ●●●●●●●●● ●● ●●●●●●●●● ●●●●●●● ●● ●●● ●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●● ●●●●●●●●●●●● ●● ●●●●●●●●●● ●●●● ●●● ●●●●●●●●●●●●●●●●●
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1
0 5000 10000 15000 20000
q44q43q42q41q32q31q25q24q23q22q21q12q11p11p12p13p21p22p31p32p33p34p35p36
chromLocation objects
> buildChromLocation ( ”<package name>”)
> prob2chro ← new.env ( )> assign ( ”probe a1 ” , ”1 ” , prob2chro )> assign ( ”probe a2 ” , ”1 ” , prob2chro )> assign ( ”probe b ” , ”X” , prob2chro )> assign ( ”probe c ” , ”X” , prob2chro )> prob2symbol ← new.env ( )> assign ( ”probe a1 ” , ”a ” , prob2symbol )> assign ( ”probe a2 ” , ”a ” , prob2chro )> assign ( ”probe b ” , ”b” , prob2symbol )> assign ( ”probe c ” , ”c ” , prob2symbol )
> f o oba r ch r l o c ← new( ”chromLocation ” ,+ organism = ”foobar ” , # name
+ dataSource = ”Dr. Moreau ' s lab ” ,+ chromLocs = l i s t ( gene a=c ( ) , gene b=c ( ) ) ,+ probesToChrom = c (1000 , 1200 , 200 , 200) ,+ chromInfo = c ( ”1 ”=10000 , ”X”=500 , ”Y”=600) ,+ geneSymbols = prob2symbol )
22
> l ibrary ( RColorBrewer )> c o l i nd ex ← cut ( expr vector , 9)> my col ← brewer .pa l (9 , ”BuGn” ) [ c o l i nd ex ]> id iogram ( expr vector , human chr , chr=”1 ” ,+ col = my col )
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0 5000 10000 15000 20000
q44q43q42q41q32q31q25q24q23q22q21q12q11p11p12p13p21p22p31p32p33p34p35p36
> c o l i nd ex ← cut (rank ( expr vec to r ) , 9)> my col ← brewer .pa l (9 , ”BuGn” ) [ c o l i nd ex ]> id iogram ( expr vector , human chr , chr=”1 ” ,+ col = my col , pch = 16)
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1
0 5000 10000 15000 20000
q44q43q42q41q32q31q25q24q23q22q21q12q11p11p12p13p21p22p31p32p33p34p35p36
2.2 Genome Atlases
Genome Atlas
23
> l ibrary ( e c o l i t k )
http://tinyurl.com/6zqsc5
Using more of the available space
24
KEMISK MÅNEDSBLAD / NUMMER 4 APRIL 2009 / 90. ÅRGANG ISSN 0011-6335KEMISK FORENING KEMIINGENIØRGRUPPEN
Tema: Plast fra træ Holdbar mælk Humleudtræk
– kemi på højniveau
2.3 Hilbervis
Hilbert’s curve
� fractal curve
� covers as much of the plane as possible
Iteration level = 1
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Iteration level = 4
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Visualizing large vectors on screens
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Comparing cartesian plots with Hilbert curve
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2.4 GenomeGraphs
GenomeGraphs
� Genomic information is traditionnally represented in tracks
� Tracks can be:
– ORF, promoters, genes on the genome
– Sequence characteristics (GC-content, functional domains, complex-ity)
– Experimental signal associated with those sequences (RNA levels,methylation signals, copy-numbers)
> l ibrary (GenomeGraphs )
> ehs mart ← useMart ( ”ensembl ” ,+ ”hsap iens gene ensembl ”)> minbase ← 180300000#180292097> maxbase ← 180500000#180491933> genesp lus ← makeGeneRegion ( start = minbase ,+ end = maxbase ,+ strand = ”+” ,+ chromosome = ”3 ” ,
28
+ biomart = ehs mart )> genesmin ← makeGeneRegion ( start = minbase ,+ end = maxbase ,+ strand = ”−” ,+ chromosome = ”3 ” ,+ biomart = ehs mart )> a x i s t r k← makeGenomeAxis ( add53 = TRUE,+ add35=TRUE,+ l i t t l e T i c k s = TRUE)> p ← gdPlot ( l i s t ( genesplus , ax i s t r k , genesmin ) ,+ minBase = minbase , maxBase = maxbase )> print (p)
180300000
180350000
180400000
180450000
1805000005' 3'
3' 5'
> i d i o g ← makeIdeogram ( ”3 ”)> p ← gdPlot ( l i s t ( id iog , genesplus ,+ ax i s t r k , genesmin ) ,+ minBase = minbase , maxBase = maxbase )> print (p)
180300000
180350000
180400000
180450000
1805000005' 3'
3' 5'
> probepos beg in ← sort ( runif (200 , min=minbase ,+ max=maxbase ) )> probepos end ← probepos beg in + 200> probepo s s i gna l ←
29
+ sin ( seq (0 , 6 , length=200)) + rnorm(200 , sd=0.05 )> probepo s s i gna l ←+ matrix ( probepos s i gna l ,+ ncol=1)> e xp r e s s i o n t r k ←+ makeGenericArray (+ i n t e n s i t y = probepos s i gna l ,+ probeStart = probepos begin ,+ probeEnd = probepos end ,+ dp = DisplayPars ( c o l o r=”darkblue ” ,+ type=”point ” ) )> gdPlot ( l i s t ( ”+” = genesplus ,+ ax i s t r k , ”−” = genesmin ,+ ”log− rat ion expr e s s i on ” = exp r e s s i o n t r k ) ,+ minBase = minbase ,+ maxBase = maxbase )
+−
log−
ratio
n ex
pres
sion
180300000180350000
180400000180450000
1805000005' 3'3' 5'
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30