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    Di 2011 houston Di 2011 houston Presentation Transcript

    • 21 22 1 20 50M 50M 0M 0M 100M 0M M 50M 150 19 M 200 0M M 50 0M 0M 18 2 M 50 M 50 0M ggbio 10 0M 0M 15 l 2 17 0M 20 M 50 l l 0M l strand 0M l + 16 M 50M 50 − 3 l M 0M 100 l 100M l 150 M rearrangements 115 50M l l interchromosomal l l 0M l l 0M intrachromosomal 50M 100M tumreads 48245000 48250000 48255000 48260000 48265000 48270000 4 l 414 l 100M hg19::chrX 50M l 6 Extending the 0M 150M l l 8 l 100M 0M l 10 13 l l 12 50M 50M 0M l 100 5 M l 15 0M 10 l 0 M 50 l 12 0M M 0M 50 M l 10 0M 10 6 Grammar of Graphics 0M 15 50 0M M 11 0M 0M 50 10 M 100 0M 50M 7 150 M 0M 0M 10 M 50M 100M 100M 50M 0M 9 8 to Genomic Data 1 Tengfei Yin, Di Cook 2 3 4 5 Stepping 6 Iowa State University 7 8 9 gieStain 10 acen Michael Lawrence gneg 11 gpos100 12 gpos25 13 gpos50 gpos75 14 gvar 15 15 stalk Genentech 16 17 Coverage 18 10 19 20 21 5 Interface 2012, Rice University 22 X 0 Y0.0e+00 5.0e+07 1.0e+08 1.5e+08 2.0e+08 0 100 200 300
    • MotivationLots of tools exist for displayinggenomic dataMany different packages, manystandalone, many different datastandardsggbio - Genomic Data Vis - Interface 2012, Rice University 2 /31
    • Motivationggbio - Genomic Data Vis - Interface 2012, Rice University 3 /31
    • Motivation Circosggbio - Genomic Data Vis - Interface 2012, Rice University 3 /31
    • Motivation Circosggbio - Genomic Data Vis - Interface 2012, Rice University 3 /31
    • Motivation Circosggbio - Genomic Data Vis - Interface 2012, Rice University 3 /31
    • Motivation Circosggbio - Genomic Data Vis - Interface 2012, Rice University 3 /31
    • Motivation Circosggbio - Genomic Data Vis - Interface 2012, Rice University 3 /31
    • Motivation Circosggbio - Genomic Data Vis - Interface 2012, Rice University 3 /31
    • Motivation Circos Need construct a central and many otherconfiguration files from scratch, learningcurve is very high Adding legend not easy Cannot map aesthetics to certainvariables  ggbio - Genomic Data Vis - Interface 2012, Rice University 3 /31
    • Motivationggbio - Genomic Data Vis - Interface 2012, Rice University 4 /31
    • Motivation UCSC Genome Browserggbio - Genomic Data Vis - Interface 2012, Rice University 4 /31
    • Motivation UCSC Genome Browser Karyogram view, with associated data plottedggbio - Genomic Data Vis - Interface 2012, Rice University 4 /31
    • Motivation UCSC Genome Browser Karyogram view, with associated data plottedggbio - Genomic Data Vis - Interface 2012, Rice University 4 /31
    • Motivation UCSC Genome Browserggbio - Genomic Data Vis - Interface 2012, Rice University 4 /31
    • Motivation UCSC Genome Browserggbio - Genomic Data Vis - Interface 2012, Rice University 4 /31
    • Motivation UCSC Genome Browser Logical zoom, all we know about this genetic codeggbio - Genomic Data Vis - Interface 2012, Rice University 4 /31
    • Motivation UCSC Genome Browser Logical zoom, all we know about this genetic codeggbio - Genomic Data Vis - Interface 2012, Rice University 4 /31
    • Motivation UCSC Genome Browserggbio - Genomic Data Vis - Interface 2012, Rice University 4 /31
    • Motivation UCSC Genome Browser Very commonly used, very popular Gives broadly applicable, generic, butnarrow selection of plot choices No operations on genomic ranges views tofacilitate perception of structure ggbio - Genomic Data Vis - Interface 2012, Rice University 4 /31
    • Motivation Chromosome X 65.93 mb 65.95 mb 65.97 mb 65.94 mb 65.96 mb7060504030 4 2 0−2 ggbio - Genomic Data Vis - Interface 2012, Rice University 5 /315 Composite plots for multiple chromosomes
    • Motivation Chromosome X Gviz (Hahne et al) 65.93 mb 65.95 mb 65.97 mb 65.94 mb 65.96 mb7060504030 4 2 0−2 ggbio - Genomic Data Vis - Interface 2012, Rice University 5 /315 Composite plots for multiple chromosomes
    • Motivation Gviz (Hahne et al)ggbio - Genomic Data Vis - Interface 2012, Rice University 5 /31
    • Motivation Gviz (Hahne et al) Pretty good! Incorporated with R, and R datastructures Uses grid (low level) graphics, veryflexible, but not leveraging tools likeggplot2 ggbio - Genomic Data Vis - Interface 2012, Rice University 5 /31
    • OutlineWhat is the grammar of graphics?How it is extended for genomic data.ExamplesNext steps: interactive graphicsggbio - Genomic Data Vis - Interface 2012, Rice University 6 /31
    • GrammarGrammar forms the foundation of alanguage. It is a set of structuralrules that govern composition.For graphics, it provides a way toconstruct a plot in a common form,and enables clarification ofsimilarities and differences betweenplots.ggbio - Genomic Data Vis - Interface 2012, Rice University 7 /31
    • Grammar (ggplot2) 80Bar chart 60 day Thuggplot(data=tips, count 40 Fri Sat aes(x=day, fill=day)) + 20 Sun geom_bar(width=1) 0 Thu Fri Sat Sun day ggbio - Genomic Data Vis - Interface 2012, Rice University 8 /31
    • Grammar (ggplot2) 80Bar chart 60 day Thuggplot(data=tips, count 40 Fri Sat aes(x=day, fill=day)) + 20 Sun geom_bar(width=1) 0 Thu Fri Sat Sun dayPie chartggplot(data=tips, aes(x=day, fill=day)) + geom_bar(width=1) + coord_polar() ggbio - Genomic Data Vis - Interface 2012, Rice University 8 /31
    • Grammar (ggplot2) 80Bar chart 60 day Thuggplot(data=tips, count 40 Fri Sat aes(x=day, fill=day)) + 20 Sun geom_bar(width=1) 0 Thu Fri Sat Sun dayPie chart 80 Sun Thu 60ggplot(data=tips, 40 20 day Thu count 0 Fri aes(x=day, fill=day)) + Sat Sun Sat Fri geom_bar(width=1) + coord_polar() day ggbio - Genomic Data Vis - Interface 2012, Rice University 8 /31
    • Grammar (ggplot2) 80Bar chart 60 day Thuggplot(data=tips, count 40 Fri Sat aes(x=day, fill=day)) + 20 Sun geom_bar(width=1) 0 Thu Fri Sat Sun dayPie chart 80 Sun Thu 60ggplot(data=tips, 40 20 day Thu count 0 Fri aes(x=day, fill=day)) + Sat Sun Sat Fri geom_bar(width=1) + coord_polar() day ggbio - Genomic Data Vis - Interface 2012, Rice University 8 /31
    • Grammar (ggplot2) 80Bar chart 60 day Thuggplot(data=tips, count 40 Fri Sat aes(x=day, fill=day)) + 20 Sun geom_bar(width=1) 0 Thu Fri Sat Sun dayPie chart 80 Sun Thu 60ggplot(data=tips, 40 20 day Thu count 0 Fri aes(x=day, fill=day)) + Sat Sun Sat Fri geom_bar(width=1) + coord_polar() day ggbio - Genomic Data Vis - Interface 2012, Rice University 8 /31
    • Grammar (ggplot2) 80Bar chart 60 day Thuggplot(data=tips, count 40 Fri Sat aes(x=day, fill=day)) + 20 Sun geom_bar(width=1) 0 Thu Fri Sat Sun day 80 Sun Thu 60 day 40 20 Thu count 0 Fri Sat Sun Sat Fri day ggbio - Genomic Data Vis - Interface 2012, Rice University 8 /31
    • Grammar (ggplot2) 80Bar chart 60 day Thuggplot(data=tips, count 40 Fri Sat aes(x=day, fill=day)) + 20 Sun geom_bar(width=1) 0 Thu Fri Sat Sun dayRose plot/Coxcomb 80 Sun Thu 60ggplot(data=tips, 40 20 day Thu count 0 Fri aes(x=day, fill=day)) + Sat Sun Sat Fri geom_bar(width=1) + coord_polar() day ggbio - Genomic Data Vis - Interface 2012, Rice University 8 /31
    • Grammar (ggplot2) 80Bar chart 60 day Thuggplot(data=tips, count 40 Fri Sat aes(x=day, fill=day)) + 20 Sun geom_bar(width=1) 0 Thu Fri Sat Sun day 80 Sun Thu 60 day 40 20 Thu count 0 Fri Sat Sun Sat Fri day ggbio - Genomic Data Vis - Interface 2012, Rice University 8 /31
    • Grammar (ggplot2)Stacked bar chart 200ggplot(data=tips, 150 day Thu count aes(x=””, fill=day)) + 100 Fri Sat Sun geom_bar(width=1) 50 0 "" ggbio - Genomic Data Vis - Interface 2012, Rice University 9 /31
    • Grammar (ggplot2)Stacked bar chart 200ggplot(data=tips, 150 day Thu count aes(x=””, fill=day)) + 100 Fri Sat Sun geom_bar(width=1) 50 0 ""Pie chartggplot(data=tips, aes(x=””, fill=day)) + geom_bar(width=1) + coord_polar(theta="y") ggbio - Genomic Data Vis - Interface 2012, Rice University 9 /31
    • Grammar (ggplot2)Stacked bar chart 200ggplot(data=tips, 150 day Thu count aes(x=””, fill=day)) + 100 Fri Sat Sun geom_bar(width=1) 50 0 ""Pie chart 0ggplot(data=tips, 200 50 day Thu count Fri aes(x=””, fill=day)) + Sat Sun geom_bar(width=1) + 150 100 coord_polar(theta="y") "" ggbio - Genomic Data Vis - Interface 2012, Rice University 9 /31
    • Grammar ElementsDATA: What is to be plottedSTAT: Statistical operations to make ondata, like binning.GEOM: Geometric object, elements to useto displays aspects of the dataSCALE: Map data to aesthetics to geomCOORD: Coordinate system to use, egCartesian(FACET): subset and display ggbio - Genomic Data Vis - Interface 2012, Rice University 10 /31
    • Example: MA plotggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plot DATA=expression data frame,x=average - Genomic Data Vis - Interfacechange University ggbio intensity, y=fold 2012, Rice 11 /31
    • Example: MA plotggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plot GEOM=pointggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plotggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plotSCALE=x is logged ggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plotggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plot SCALE=color is mapped to statistical significanceggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plotggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plot COORD=default, Cartesianggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plotggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plot FACET=noneggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plotggbio - Genomic Data Vis - Interface 2012, Rice University 11 /31
    • Example: MA plotqplot(baseMean, log2FoldChange, data = res, geom = "point", xlab = "Normalized mean", ylab = "log2 fold change", xlim = c(0, 10000), color = group) + scale_x_log10() + scale_color_manual( values = c("black", "red")) ggbio - Genomic Data Vis - Interface 2012, Rice University 12 /31
    • What’s different?Genomic data has interval contextSeveral common geoms used instandard plots, not in currentgrammarAdditional transformations commonLining up of multiple data plots,especially against genomeggbio - Genomic Data Vis - Interface 2012, Rice University 13 /31
    • What’s different? No seqnames ranges strand tx id exon id 1 chrX [48242968, 48243005] + 35775 132624 2 chrX [48243475, 48243563] + 35775 132625 3 chrX [48244003, 48244117] + 35775 132626 4 chrX [48244794, 48244889] + 35775 132627 5 chrX [48246753, 48246802] + 35775 132628 ... ... ... ... ... ... ... 26 chrX [48270193, 48270307] - 35778 132637 27 chrX [48269421, 48269516] - 35778 132636 28 chrX [48267508, 48267557] - 35778 132635 29 chrX [48262894, 48262998] - 35778 132633 30 chrX [48261524, 48262111] - 35778 132632able 2: Typical biological data coerced into a data frame: A GRanges table representing gene SSX4 an DATA: Genomic rangesSX4B. One row represents one exon, seqnames indicates the chromosome name, ranges indicates the interva exons, strand is the direction, tx id and exon id are the internal id’s used for mapping cross database. ggbio - Genomic Data Vis - Interface 2012, Rice University 14 /31
    • Extensions data source(s) I/O packages in bioconductor abstract data (formal model) meta dataautoplot geom stat scale grammar of graphics with coord facet layout extensions plots tracks ggbio - Genomic Data Vis - Interface 2012, Rice University 15 /31
    • Extensionscurrent software. Development of new visualization tools should beindependently factorized into components of the grammar. Table 1describes the extensions developed in this work. Comp name usage icon geom geom rect rectangle geom segment segment aut geom chevron chevron geom arrow arrow geom arch arches geom bar bar geom alignment alignment (gene) stat stat coverage coverage (of reads) Figu ggbio - Genomic Data Vis -mismatch pileup forRice University Interface 2012, tion. 16 /31 stat mismatch mod
    • geom arrow arrow Extensions geom arch arches geom bar bar geom alignment alignment (gene)stat stat coverage coverage (of reads) Figure tion. It stat mismatch mismatch pileup for model, alignments map d new co stat aggregate aggregate in sliding body o window ggbio. stat stepping avoid overplotting stat gene consider gene struc- ture Sev In ggp stat table tabulate ranges ple bin resenti stat identity no change comm coveracoord linear ggplot2 linear butggbio - Genomic Data Vis - Interface 2012, Rice University facet by chromo- 17 31al read / Add
    • stat gene consider gene struc- ture Several Extensions In ggplot2 stat table tabulate ranges ple binning resenting a stat identity no change commonly coverage, coord linear ggplot2 linear but facet by chromo- read alignm some Additio faceting m genome put everything on are listed in genominc coordi- examples. nates Let’s an truncate gaps compact view by Figure 3, t shrinking gaps In this plo by using th layout track stacked tracks attributes s system. It chr1 chr2 karyogram karyogram display ing genom chr3 50 100 150 200 250 300 start circle circular the differe the gramm faceting formula facet by formula once one g discover th ranges facet by ranges componen scale not extended ggplot2default in the desigggbio - Genomic Data Vis - Interface 2012, Rice University / 18 31 The foll
    • Extensionsautoplot Tries, and does a jolly good job, of recognizing the data object to be plotted, and how it should be displayed. ggbio - Genomic Data Vis - Interface 2012, Rice University 19 /31
    • Example 2 strand + − 1 48245000 48250000 48255000 48260000 48265000 48270000 hg19::chrXggbio - Genomic Data Vis - Interface 2012, Rice University 20 /31
    • Example 2 strand + − 1 48245000 48250000 48255000 48260000 48265000 48270000 hg19::chrX DATA=GRangesList Objectggbio - Genomic Data Vis - Interface 2012, Rice University 20 /31
    • Example 2 strand + − 1 48245000 48250000 48255000 48260000 48265000 48270000 hg19::chrXggbio - Genomic Data Vis - Interface 2012, Rice University 20 /31
    • Example GEOM=alignment, chevron 2 strand + − 1 48245000 48250000 48255000 48260000 48265000 48270000 hg19::chrXggbio - Genomic Data Vis - Interface 2012, Rice University 20 /31
    • Example 2 strand + − 1 48245000 48250000 48255000 48260000 48265000 48270000 hg19::chrXggbio - Genomic Data Vis - Interface 2012, Rice University 20 /31
    • Example 2SCALE=stepping, strand + color=strand − 1 48245000 48250000 48255000 48260000 48265000 48270000 hg19::chrX ggbio - Genomic Data Vis - Interface 2012, Rice University 20 /31
    • Example 2 strand + − 1 48245000 48250000 48255000 48260000 48265000 48270000 hg19::chrXggbio - Genomic Data Vis - Interface 2012, Rice University 20 /31
    • Example 2 strand + − 1 48245000 48250000 48255000 48260000 hg19::chrX LAYOUT=linear 48265000 48270000ggbio - Genomic Data Vis - Interface 2012, Rice University 20 /31
    • Example 2 strand + − 1 48245000 48250000 48255000 48260000 48265000 48270000 hg19::chrXggbio - Genomic Data Vis - Interface 2012, Rice University 20 /31
    • Examples p1 <- autoplot(gr) p2 <- autoplot(gr, Stepping stat = "coverage") tracks(p1, p2) Examine short 15 reads Stack them (top)Coverage 10 5 Collapse into 0 “density” (bottom) 0 100 200 300 ggbio - Genomic Data Vis - Interface 2012, Rice University 21 /31
    • Examplesuc002dwc.3(226) p1 <- autoplot(txdb,   which = genesymbol["A"])uc010veg.2(226) p2 <- autoplot(txdb,  uc002dwa.4(226) which = genesymbol["A"],uc002dvz.3(226) stat = "reduce")uc002dvw.3(226) tracks(p1, p2, heights = c(4, 1))uc002dvx.3(226) Compare transcriptsuc010bzo.2(226) Reduce all to one 30065000 30070000 30075000 30080000 ggbio - Genomic Data Vis - Interface 2012, Rice University 22 /31
    • Examples uc002dwc.3(226) p1 <- autoplot(txdb,   uc010veg.2(226) uc002dwa.4(226) which = genesymbol["A"])knownGene uc002dvz.3(226) p2 <- autoplot(txdb,   which = genesymbol["A"], uc002dvw.3(226) uc002dvx.3(226) uc010bzo.2(226)   truncate.gaps = TRUE) 30065000 30070000 30075000 30080000 hg19::chr16 tracks(p1, p2, uc002dwc.3(226) uc010veg.2(226) heights = c(4, 4)) uc002dwa.4(226)knownGene uc002dvz.3(226) uc002dvw.3(226) uc002dvx.3(226) uc010bzo.2(226) 30064411 hg19::chr16 30081741 Focus on exons ggbio - Genomic Data Vis - Interface 2012, Rice University 23 /31
    • Examples 3.0 1 2 3 4 5 6 l 7 8 9 10 11 12 13 l 14 15 16 17 18 19 20 21 l 22 X Y p1 <- autoplot(gr.snp, l geom = "point", l l l 2.5 l l l l l l l l l l l l l l aes(y = pvalue)) l l 2.0 l ll l l ll l l l l l l l l l lpvalue l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l ll l l l ll l l l ll l l 1.5 l l l l l ll l l l l l l l l l l l l ll l l ll l ll l ll l l l l ll l l l l l l l l l ll l l l l l l ll ll l ll l ll l l l ll l l l ll l l l l l l l l l l l l l l ll l l l l ll l l l l l l l l l l ll l l l l ll l l l l l l l l l l l l ll l ll l l l l l l l l l l l l ll l l l 1.0 l l lll l l l l l l ll l l l l l l l l l l l l ll ll l l l l ll l l l l l ll l ll ll l l l l l l ll ll l ll l ll l ll ll l l ll l ll l l l l ll ll l ll l l l l l l l l l l l ll ll l l ll ll l l l ll l l l l ll ll l l l l l l ll l lll l ll ll l ll l l l ll l l l l ll l l l l l ll ll l l ll l ll ll l l lll l l ll l l l l l l l lll l l ll l ll l l l llll ll ll l l ll l l l l l l l ll l l l l l l ll l l l l ll ll ll ll l lll l l l l lll ll l l l ll ll l ll ll ll l l l ll l ll l ll l l l l l ll l l l l ll l ll l l ll l l l l l l l l l l l l ll l lll l l l l ll ll ll l l l ll l l ll l l ll l l ll l l ll lll ll l l l ll lll ll ll l l ll l l l ll l l l l ll l l l l l ll l l l l ll ll l l ll ll l ll l l l l l l l ll l l l l ll ll l l l l l ll l ll l l l l l l l l ll l lll lll ll ll l ll ll l ll l l l 0.5 l ll l l l l l lll l ll l l l ll l l l ll ll l ll l lll l ll ll ll l ll lll ll l l ll ll ll l l ll l ll l l l l l l l l l ll ll l lll l l l l l ll l ll l l ll ll l ll l lll l l l ll l l ll l ll ll l l l l l l l ll l ll l ll l l l l l l l ll l l ll ll ll ll l ll l l lll ll l lllll ll lll ll l l l lll l l l ll ll ll l l lll ll l l ll ll l l ll l l l l lll lll ll ll ll ll lll ll ll l l l l l ll l l ll ll l l ll ll ll ll l l ll ll l ll l lll l ll lll ll l l ll l l l ll l l l ll ll lll l ll l l ll l l ll l l ll ll l l ll l l ll l ll l ll l ll l ll ll l l l l ll ll l l ll l ll l ll l l l l l l l l l ll l l l ll ll ll ll ll l ll ll ll ll lll l ll ll ll l l l l l ll l ll l l l ll ll l ll l ll ll l l ll l l l ll l ll l ll ll ll ll l ll ll l ll ll l ll ll l ll l ll ll l l l l l ll l ll llll l ll l ll ll l l ll l l ll l l l l l l ll l l ll l lll ll l l l l l ll l l ll ll l l l lll l l l llll l ll l ll lll ll l ll l ll ll ll ll ll ll ll ll l ll llll ll ll l l lll l ll ll l l ll ll l ll l ll l ll ll l l ll ll l l l l ll l l ll l ll l ll l ll l l l ll ll ll l ll l lll ll ll l lll ll lll ll l ll l ll l l l ll ll l l ll l l l ll l l l l ll l l ll l ll l ll ll l ll lll l ll l ll l l l ll ll l ll ll l lll lll lll l ll ll l ll ll lll l lll ll l ll ll l l l l l ll l l ll l l ll l lll lll ll ll l ll l l ll l ll ll ll l l lll l l ll l llll l ll l l ll l ll l ll l l ll l l l l l ll l ll l l l l l lll llll l ll l l ll l l ll l ll lll ll lllll ll ll ll ll l lll ll l l ll ll ll ll ll l ll l ll l ll ll ll ll ll l ll l ll l l ll l l l l ll l lllll l ll l l lll l ll ll l ll l ll ll l ll l lll l l ll l l l l l l l ll ll l l l l l l ll l ll l ll lll ll ll l ll ll l ll lll ll ll l ll ll l ll ll llll l l l ll ll ll l ll ll ll l l llllll ll l l l l ll l l l l lll l l ll l lll l l llll l l ll ll ll ll l lll l ll l ll l lll l l lll l ll l l ll lll l ll ll ll ll ll ll ll l l l l ll l lll l lll l ll ll l ll l lll lll ll lll ll ll lll l ll ll ll lll ll l ll ll ll l l ll ll ll l l l l l l llll l l l l l ll l l ll l l l l ll lll ll l ll l ll l ll l l l ll ll l llll l l lll ll l ll ll ll ll l l llll l ll l l ll l l ll l ll l lll l l ll l l l l l ll lllll llll l l l llll l l l l l l ll ll l l l ll ll l llll ll ll l ll l ll ll ll l ll lll l l l ll ll l ll ll ll ll ll ll l l ll l ll ll l l ll l l l l ll ll l l l ll ll ll ll l 5.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+07 1.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+08 1.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+08 2.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+08 1,10,11,12,13,14,15,16,17,18,19,2,20,21,22,3,4,5,6,7,8,9,X,Y p2 <- plotGrandLinear( l 3.0 l l l l gr.snp, l l 2.5 l l l l l l l l l l l l l l l l 2.0 l ll l l aes(y = pvalue)) l l l l l l l l l l l l l l l l l l l l ll l l l l ll l l l l l l l l ly l l l l l ll l l l l l l l l l l ll l 1.5 l l l l l l l l l l l l l l l l l ll l l l l l l l l l l l l ll l l l l l l l l l l ll l l l l l l l l l l l l l l l l l l l l ll l l l l l l l l ll l ll l l l l l l l l l l l l l l l l l l l l l l l ll l l l l l l l l l l l l l ll l l l l l l l l l l l l l ll l l l ll l l l l l l l 1.0 l l l ll l l l l l l l l ll l ll l l ll l l l l l l l l ll l l ll l l ll l l ll l l l ll l l ll l l ll ll ll l l l l ll l l l l ll l ll ll l ll l l l l l l l l l l ll l l ll ll ll l l l l l ll l l l l l l ll l l l ll l l ll l ll l ll l l l l l l ll l ll ll l l l l l l ll l ll ll l l l l l ll l l l ll l ll l l l l l ll l l l l l l l l ll l l l l l l l ll ll l l ll l ll l l ll l l l l l ll ll l l l ll l l ll ll l l l l ll l ll l l ll l l ll l l l l ll l l l ll l ll l l l l l ll l l l ll l l l l l ll l l l l l l l l l l l l ll l l l l ll l l ll l l l l ll l l ll l l l l l l l l ll ll l l ll l l l l l l l l ll ll ll ll l ll l l l l ll l ll l l ll lll l l l l l l l l l l l ll l l l ll ll ll ll l ll ll l ll l lll ll ll l l l l l l l l l l l l ll ll lll l l l ll ll l l l l l ll ll l l ll l l l l ll l l l ll l l lll l l l ll l l ll l ll ll l l ll l l ll ll l lll l l l l l l l ll l lll l l l l l l ll l ll 0.5 l l lll l l ll l ll l l l l l lll l l l ll l l ll l ll l l ll l l ll ll ll l ll l ll l l ll l ll ll l ll l l ll ll l l l l ll l ll l l l l ll l lll ll l l ll l l l l ll l l l ll l ll ll l l l ll ll l l ll ll l l l l l l l l ll ll ll l l l l l l l ll ll l ll l ll l lll l l l l l ll l l ll l lll l l l l ll l l lll ll l l llll l lll l l l l ll l lll ll ll l l l l l l l l ll l ll ll ll l l l l llll l l ll l l l ll l l l ll ll l l l l l l ll l ll l l ll l l l ll l l l l l l ll l l ll l l l l lll l l l l l ll ll l l l l lll l l lll l lll l l ll ll l l ll l l l l ll l ll l l ll l l l l l lll l l l l l ll l l l l ll l l l l ll l l ll l l l ll ll l l l ll l ll ll l l l l ll l l ll ll ll l l ll l l l l l llll l l l ll l ll l l l l l l l l ll l ll ll l l l ll l ll l l ll l l l lll l ll l l l ll l ll l l l l ll l l ll l l l ll ll l l l ll l lll l l l l l l l ll l l l ll l l ll l l l ll l l l ll ll ll l l l ll l l l l ll l l lll l l l l ll l l l lll ll lllll l lll l l lll l l ll l l ll lll ll l ll lll l lll l l l l ll ll l l l l l ll l l l l lll lll ll l l ll l l l l ll ll l lll ll ll l ll l l ll ll l l ll ll ll lll ll l ll l ll ll l l l l ll l l ll l l l ll l llll ll l ll l l ll ll ll ll ll l l l l ll ll l ll lll l l ll ll lll l l l ll l ll l l lll ll l l l l l ll l ll l l l l l ll l l l l ll l l ll ll l l l l lll l ll ll l l l lll l l l ll l l l l l l ll l l ll l l ll ll l l l ll l l l ll ll l l ll ll llllll l l l l l l l l l l l l l ll l l l l l l l l lllllll l l ll l l l l l l ll l l l l l ll l ll l l l l l llll llll ll l l ll l l ll l l l l l llll l l ll ll l ll l l ll l l l l l ll l lll l ll l l lll l l ll ll l l ll l ll l l l lllll ll l ll l l lll l l llll ll ll l l l lll lll l l l l ll ll l l l ll l ll ll l ll ll l l lll ll l l l ll l ll l l ll ll l lll ll l ll l ll ll ll l l lll l l l lll llll l l l ll lll l l l l ll lllll l lll l l ll ll l l l l l l l l l l l l ll ll l l ll l l ll l l l l l l l ll l l lll ll ll l l ll lllll l l l l l ll l l ll l l l ll l ll l l l l ll ll lll ll ll ll llll l lll l lll lll l l ll llll lll ll l l l l ll lll l lll lll ll l ll ll ll l l l l ll l l l ll l ll ll l l ll l l l l l l l l l l l ll l l l l l l ll l ll l l l l l ll ll ll l l l ll l l l ll l l l l l ll l l l l l l ll l l l llll l ll l l ll l lll ll ll ll lll l l l ll l l ll l l lll l ll ll l l l l l lll l ll ll ll ll l l lll l l l l l l lll l l l l ll l l ll lll ll l lll l l lll lll lll ll l lll l l llll lll l l ll lll l ll l ll lll l l l l llll ll lll l 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y genome Manhattan plot: features plotted against genomic position ggbio - Genomic Data Vis - Interface 2012, Rice University 24 /31
    • Examples 3.0 1 2 3 4 5 6 l 7 8 9 10 11 12 13 l 14 15 16 17 18 19 20 21 l 22 X Y p1 <- autoplot(gr.snp, l geom = "point", l l l 2.5 l l l l l l l l l l l l l l aes(y = pvalue)) l l 2.0 l ll l l ll l l l l l l l l l lpvalue l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l ll l l l ll l l l ll l l 1.5 l l l l ll l l l l l l Facets by l l l l l l ll l l l l ll l ll l l ll l l l l l l l l l l l l l ll l l l l l l ll ll l ll l ll l l l ll l l l ll l l l l l l l l l l l l l l ll l l l l ll l l l l l l l l l l ll l l l l ll l l l l l l l l l l l l ll l ll l l l l l l l l l l l l ll l l l 1.0 l l lll l l l l l l ll l l l l l l l l l l l l ll ll l l l l ll l l l l l ll l ll ll l l l l l l ll ll l ll l ll l ll ll l l ll l ll l l l l ll ll l ll l l l l l l l l l l l ll ll l l ll ll l l l ll l l l l ll ll l l l l l l ll l lll l ll ll l ll l l l ll l l l l ll l l l l l ll ll l l ll l ll ll l l lll l l l l l l lll l l l ll l l l llll ll l l ll l l l l chromosome # l l ll l ll ll l l l ll l l l l l l ll l l l l ll ll ll ll l lll l l l l lll ll l l l ll ll l ll ll ll l l l ll l ll l ll l l l l l ll l l l l ll l ll l l ll l l l l l l l l l l l l ll l lll l l l l ll ll ll l l l ll l l ll l l ll l l ll l l ll lll ll l l l ll lll ll ll l l ll l l l ll l l l l ll l l l l l ll l l l l ll ll l l ll ll l ll l l l l l l l ll l l l l ll ll l l l l l ll l ll l l l l l l l l ll l lll lll ll ll l ll ll l ll l l l 0.5 l ll l l l l l lll l ll l l l ll l l l ll ll l ll l lll l ll ll ll l ll lll ll l l ll ll ll l l ll l ll l l l l l l l l l ll ll l lll l l l l l ll l ll l l ll ll l ll l lll l l l ll l l ll l ll ll l l l l l l l ll l ll l ll l l l l l l l ll l l ll ll ll ll l ll l l lll ll l lllll ll lll ll l l l lll l l l ll ll ll l l lll ll l l ll ll l l ll l l l l lll lll ll ll ll ll lll ll ll l l l l l ll l l ll ll l l ll ll ll ll l l ll ll l ll l lll l ll lll ll l l ll l l l ll l l l ll ll lll l ll l l ll l l ll l l ll ll l l ll l l ll l ll l ll l ll l ll ll l l l l ll ll l l ll l ll l ll l l l l l l l l l ll l l l ll ll ll ll ll l ll ll ll ll lll l ll ll ll l l l l l ll l ll l l l ll ll l ll l ll ll l l ll l l l ll l ll l ll ll ll ll l ll ll l ll ll l ll ll l ll l ll ll l l l l l ll l ll llll l ll l ll ll l l ll l l ll l l l l l l ll l l ll l lll ll l l l l l ll l l ll ll l l l lll l l l llll l ll l ll lll ll l ll l ll ll ll ll ll ll ll ll l ll llll ll ll l l lll l ll ll l l ll ll l ll l ll l ll ll l l ll ll l l l l ll l l ll l ll l ll l ll l l l ll ll ll l ll l lll ll ll l lll ll lll ll l ll l ll l l l ll ll l l ll l l l ll l l l l ll l l ll l ll l ll ll l ll lll l ll l ll l l l ll ll l ll ll l lll lll lll l ll ll l ll ll lll l lll ll l ll ll l l l l l ll l l ll l l ll l lll lll ll ll l ll l l ll l ll ll ll l l lll l l ll l llll l ll l l ll l ll l ll l l ll l l l l l ll l ll l l l l l lll llll l ll l l ll l l ll l ll lll ll lllll ll ll ll ll l lll ll l l ll ll ll ll ll l ll l ll l ll ll ll ll ll l ll l ll l l ll l l l l ll l lllll l ll l l lll l ll ll l ll l ll ll l ll l lll l l ll l l l l l l l ll ll l l l l l l ll l ll l ll lll ll ll l ll ll l ll lll ll ll l ll ll l ll ll llll l l l ll ll ll l ll ll ll l l llllll ll l l l l ll l l l l lll l l ll l lll l l llll l l ll ll ll ll l lll l ll l ll l lll l l lll l ll l l ll lll l ll ll ll ll ll ll ll l l l l ll l lll l lll l ll ll l ll l lll lll ll lll ll ll lll l ll ll ll lll ll l ll ll ll l l ll ll ll l l l l l l llll l l l l l ll l l ll l l l l ll lll ll l ll l ll l ll l l l ll ll l llll l l lll ll l ll ll ll ll l l llll l ll l l ll l l ll l ll l lll l l ll l l l l l ll lllll llll l l l llll l l l l l l ll ll l l l ll ll l llll ll ll l ll l ll ll ll l ll lll l l l ll ll l ll ll ll ll ll ll l l ll l ll ll l l ll l l l l ll ll l l l ll ll ll ll l 5.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+07 1.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+08 1.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+08 2.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+08 1,10,11,12,13,14,15,16,17,18,19,2,20,21,22,3,4,5,6,7,8,9,X,Y p2 <- plotGrandLinear( l 3.0 l l l l gr.snp, l l 2.5 l l l l l l l l l l l l l l l l 2.0 l ll l l aes(y = pvalue)) l l l l l l l l l l l l l l l l l l l l ll l l l l ll l l l l l l l l ly l l l l l ll l l l l l l l l l l ll l 1.5 l l l l l l l l l l l l l l l l l ll l l l l l l l l l l l l ll l l l l l l l l l l ll l l l l l l l l l l l l l l l l l l l l ll l l l l l l l l ll l ll l l l l l l l l l l l l l l l l l l l l l l l ll l l l l l l l l l l l l l ll l l l l l l l l l l l l l ll l l l ll l l l l l l l 1.0 l l l ll l l l l l l l l ll l ll l l ll l l l l l l l l ll l l ll l l ll l l ll l l l ll l l ll l l ll ll ll l l l l ll l l l l ll l ll ll l ll l l l l l l l l l l ll l l ll ll ll l l l l l ll l l l l l l ll l l l ll l l ll l ll l ll l l l l l l ll l ll ll l l l l l l ll l ll ll l l l l l ll l l l ll l ll l l l l l ll l l l l l l l l ll l l l l l l l ll ll l l ll l ll l l ll l l l l l ll ll l l l ll l l ll ll l l l l ll l ll l l ll l l ll l l l l ll l l l ll l ll l l l l l ll l l l ll l l l l l ll l l l l l l l l l l l l ll l l l l ll l l ll l l l l ll l l ll l l l l l l l l ll ll l l ll l l l l l l l l ll ll ll ll l ll l l l l ll l ll l l ll lll l l l l l l l l l l l ll l l l ll ll ll ll l ll ll l ll l lll ll ll l l l l l l l l l l l l ll ll lll l l l ll ll l l l l l ll ll l l ll l l l l ll l l l ll l l lll l l l ll l l ll l ll ll l l ll l l ll ll l lll l l l l l l l ll l lll l l l l l l ll l ll 0.5 l l lll l l ll l ll l l l l l lll l l l ll l l ll l ll l l ll l l ll ll ll l ll l ll l l ll l ll ll l ll l l ll ll l l l l ll l ll l l l l ll l lll ll l l ll l l l l ll l l l ll l ll ll l l l ll ll l l ll ll l l l l l l l l ll ll ll l l l l l l l ll ll l ll l ll l lll l l l l l ll l l ll l lll l l l l ll l l lll ll l l llll l lll l l l l ll l lll ll ll l l l l l l l l ll l ll ll ll l l l l llll l l ll l l l ll l l l ll ll l l l l l l ll l ll l l ll l l l ll l l l l l l ll l l ll l l l l lll l l l l l ll ll l l l l lll l l lll l lll l l ll ll l l ll l l l l ll l ll l l ll l l l l l lll l l l l l ll l l l l ll l l l l ll l l ll l l l ll ll l l l ll l ll ll l l l l ll l l ll ll ll l l ll l l l l l llll l l l ll l ll l l l l l l l l ll l ll ll l l l ll l ll l l ll l l l lll l ll l l l ll l ll l l l l ll l l ll l l l ll ll l l l ll l lll l l l l l l l ll l l l ll l l ll l l l ll l l l ll ll ll l l l ll l l l l ll l l lll l l l l ll l l l lll ll lllll l lll l l lll l l ll l l ll lll ll l ll lll l lll l l l l ll ll l l l l l ll l l l l lll lll ll l l ll l l l l ll ll l lll ll ll l ll l l ll ll l l ll ll ll lll ll l ll l ll ll l l l l ll l l ll l l l ll l llll ll l ll l l ll ll ll ll ll l l l l ll ll l ll lll l l ll ll lll l l l ll l ll l l lll ll l l l l l ll l ll l l l l l ll l l l l ll l l ll ll l l l l lll l ll ll l l l lll l l l ll l l l l l l ll l l ll l l ll ll l l l ll l l l ll ll l l ll ll llllll l l l l l l l l l l l l l ll l l l l l l l l lllllll l l ll l l l l l l ll l l l l l ll l ll l l l l l llll llll ll l l ll l l ll l l l l l llll l l ll ll l ll l l ll l l l l l ll l lll l ll l l lll l l ll ll l l ll l ll l l l lllll ll l ll l l lll l l llll ll ll l l l lll lll l l l l ll ll l l l ll l ll ll l ll ll l l lll ll l l l ll l ll l l ll ll l lll ll l ll l ll ll ll l l lll l l l lll llll l l l ll lll l l l l ll lllll l lll l l ll ll l l l l l l l l l l l l ll ll l l ll l l ll l l l l l l l ll l l lll ll ll l l ll lllll l l l l l ll l l ll l l l ll l ll l l l l ll ll lll ll ll ll llll l lll l lll lll l l ll llll lll ll l l l l ll lll l lll lll ll l ll ll ll l l l l ll l l l ll l ll ll l l ll l l l l l l l l l l l ll l l l l l l ll l ll l l l l l ll ll ll l l l ll l l l ll l l l l l ll l l l l l l ll l l l llll l ll l l ll l lll ll ll ll lll l l l ll l l ll l l lll l ll ll l l l l l lll l ll ll ll ll l l lll l l l l l l lll l l l l ll l l ll lll ll l lll l l lll lll lll ll l lll l l llll lll l l ll lll l ll l ll lll l l l l llll ll lll l 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y genome Manhattan plot: features plotted against genomic position ggbio - Genomic Data Vis - Interface 2012, Rice University 24 /31
    • Examples 3.0 1 2 3 4 5 6 l 7 8 9 10 11 12 13 l 14 15 16 17 18 19 20 21 l 22 X Y p1 <- autoplot(gr.snp, l geom = "point", l l l 2.5 l l l l l l l l l l l l l l aes(y = pvalue)) l l 2.0 l ll l l ll l l l l l l l l l lpvalue l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l ll l l l ll l l l ll l l 1.5 l l l l ll l l l l l l Facets by l l l l l l ll l l l l ll l ll l l ll l l l l l l l l l l l l l ll l l l l l l ll ll l ll l ll l l l ll l l l ll l l l l l l l l l l l l l l ll l l l l ll l l l l l l l l l l ll l l l l ll l l l l l l l l l l l l ll l ll l l l l l l l l l l l l ll l l l 1.0 l l lll l l l l l l ll l l l l l l l l l l l l ll ll l l l l ll l l l l l ll l ll ll l l l l l l ll ll l ll l ll l ll ll l l ll l ll l l l l ll ll l ll l l l l l l l l l l l ll ll l l ll ll l l l ll l l l l ll ll l l l l l l ll l lll l ll ll l ll l l l ll l l l l ll l l l l l ll ll l l ll l ll ll l l lll l l l l l l lll l l l ll l l l llll ll l l ll l l l l chromosome # l l ll l ll ll l l l ll l l l l l l ll l l l l ll ll ll ll l lll l l l l lll ll l l l ll ll l ll ll ll l l l ll l ll l ll l l l l l ll l l l l ll l ll l l ll l l l l l l l l l l l l ll l lll l l l l ll ll ll l l l ll l l ll l l ll l l ll l l ll lll ll l l l ll lll ll ll l l ll l l l ll l l l l ll l l l l l ll l l l l ll ll l l ll ll l ll l l l l l l l ll l l l l ll ll l l l l l ll l ll l l l l l l l l ll l lll lll ll ll l ll ll l ll l l l 0.5 l ll l l l l l lll l ll l l l ll l l l ll ll l ll l lll l ll ll ll l ll lll ll l l ll ll ll l l ll l ll l l l l l l l l l ll ll l lll l l l l l ll l ll l l ll ll l ll l lll l l l ll l l ll l ll ll l l l l l l l ll l ll l ll l l l l l l l ll l l ll ll ll ll l ll l l lll ll l lllll ll lll ll l l l lll l l l ll ll ll l l lll ll l l ll ll l l ll l l l l lll lll ll ll ll ll lll ll ll l l l l l ll l l ll ll l l ll ll ll ll l l ll ll l ll l lll l ll lll ll l l ll l l l ll l l l ll ll lll l ll l l ll l l ll l l ll ll l l ll l l ll l ll l ll l ll l ll ll l l l l ll ll l l ll l ll l ll l l l l l l l l l ll l l l ll ll ll ll ll l ll ll ll ll lll l ll ll ll l l l l l ll l ll l l l ll ll l ll l ll ll l l ll l l l ll l ll l ll ll ll ll l ll ll l ll ll l ll ll l ll l ll ll l l l l l ll l ll llll l ll l ll ll l l ll l l ll l l l l l l ll l l ll l lll ll l l l l l ll l l ll ll l l l lll l l l llll l ll l ll lll ll l ll l ll ll ll ll ll ll ll ll l ll llll ll ll l l lll l ll ll l l ll ll l ll l ll l ll ll l l ll ll l l l l ll l l ll l ll l ll l ll l l l ll ll ll l ll l lll ll ll l lll ll lll ll l ll l ll l l l ll ll l l ll l l l ll l l l l ll l l ll l ll l ll ll l ll lll l ll l ll l l l ll ll l ll ll l lll lll lll l ll ll l ll ll lll l lll ll l ll ll l l l l l ll l l ll l l ll l lll lll ll ll l ll l l ll l ll ll ll l l lll l l ll l llll l ll l l ll l ll l ll l l ll l l l l l ll l ll l l l l l lll llll l ll l l ll l l ll l ll lll ll lllll ll ll ll ll l lll ll l l ll ll ll ll ll l ll l ll l ll ll ll ll ll l ll l ll l l ll l l l l ll l lllll l ll l l lll l ll ll l ll l ll ll l ll l lll l l ll l l l l l l l ll ll l l l l l l ll l ll l ll lll ll ll l ll ll l ll lll ll ll l ll ll l ll ll llll l l l ll ll ll l ll ll ll l l llllll ll l l l l ll l l l l lll l l ll l lll l l llll l l ll ll ll ll l lll l ll l ll l lll l l lll l ll l l ll lll l ll ll ll ll ll ll ll l l l l ll l lll l lll l ll ll l ll l lll lll ll lll ll ll lll l ll ll ll lll ll l ll ll ll l l ll ll ll l l l l l l llll l l l l l ll l l ll l l l l ll lll ll l ll l ll l ll l l l ll ll l llll l l lll ll l ll ll ll ll l l llll l ll l l ll l l ll l ll l lll l l ll l l l l l ll lllll llll l l l llll l l l l l l ll ll l l l ll ll l llll ll ll l ll l ll ll ll l ll lll l l l ll ll l ll ll ll ll ll ll l l ll l ll ll l l ll l l l l ll ll l l l ll ll ll ll l 5.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+075.0e+07 1.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+081.0e+08 1.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+081.5e+08 2.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+082.0e+08 1,10,11,12,13,14,15,16,17,18,19,2,20,21,22,3,4,5,6,7,8,9,X,Y p2 <- plotGrandLinear( l 3.0 l l l l gr.snp, l l 2.5 l l l l l l l l l l l l l l l l 2.0 l ll l l aes(y = pvalue)) l l l l l l l l l l l l l l l l l l l l ll l l l l ll l l l l l l l l ly l l l l l ll l l l l l l l l l l ll l 1.5 l l l l l l l l l l l l l l l l l ll l l l l l l l l l l l l ll l l l l l l l l l l ll l l l l l l l l l l l l l l l l l l l l ll l l l l l l l l ll l ll l l l l l l l l l l l l l ll Turns chromosome # l l l l l l l l l l l l l l l l l l l l l l l ll l l l l l l l l l l l l l ll l l l ll l l l l l l l 1.0 l l l ll l l l l l l l l ll l ll l l ll l l l l l l l l ll l l ll l l ll l l ll l l l ll l l ll l l ll ll ll l l l l ll l l l l ll l ll ll l ll l l l l l l l l l l ll l l ll ll ll l l l l l ll l l l l l l ll l l l ll l l ll l ll l ll l l l l l l ll l ll ll l l l l l l ll l ll ll l l l l l ll l l l ll l ll l l l l l ll l l l l l l l l ll l l l l l l l ll ll l l ll l ll l l ll l l l l l ll ll l l l ll l l ll ll l l l l ll l ll l l ll l l ll l l l l ll l l l ll l ll l l l l l ll l l l ll l l l l l ll l l l l l l l l l l l l ll l l l l ll l l ll l l l l ll l l ll l l l l l l l l ll ll l l ll l l l l l l l l ll ll ll ll l ll l l l l ll l ll l l ll lll l l l l l l l l l l l ll l l l ll ll ll ll l ll ll l ll l lll ll ll l l l l l l l l l l l l ll ll lll l l l ll ll l l l l l ll ll l l ll l l l l ll l l l ll l l lll l l l ll l l ll l ll ll l l ll l l ll ll l lll l l l l l l l ll l lll l l l l l l ll l ll 0.5 l ll ll l l ll l l l l lll l l l ll l ll l into numerical scale l l l l l ll l l ll l l ll l ll ll ll ll l l l l l ll ll l ll l l lll l l ll l ll l l lll l l l l l l ll l ll l lll l ll ll l ll l ll l l ll l l ll l l ll l l ll ll ll l ll l l ll l l ll l l l l ll ll l ll l ll l l l ll ll ll l l l l l l ll ll l l l l l ll l ll l lll l l l l ll l l lll ll l l llll l lll l l l l lll l l l l l l l l ll l ll ll ll l l l l ll l ll l l l ll l l l ll ll l l l l l l ll l ll l l ll l l l ll l l l l l l ll l l ll l l l l lll l l l l l ll ll l l l l lll l l lll l lll l l ll ll l l ll l l l l ll l ll l l ll l l l l l lll l l l l l ll l l l l ll l l l l ll l l ll l l l ll ll l l l ll l ll ll l l l l ll l l ll ll ll l l ll l l l l l llll l l l ll l ll l l l l l l l l ll l ll ll l l l ll l ll l l ll l l l lll l ll l l l ll l ll l l l l ll l l ll l l l ll ll l l l ll l lll l l l l l l l ll l l l ll l l ll l l l ll l l l ll ll ll l l l ll l l l l ll l l lll l l l l ll l l l lll ll lllll l lll l l lll l l ll l l ll lll ll l ll lll l lll l l l l ll ll l l l l l ll l l l l lll lll ll l l ll l l l l ll ll l lll ll ll l ll l l ll ll l l ll ll ll lll ll l ll l ll ll l l l l ll l l ll l l l ll l llll ll l ll l l ll ll ll ll ll l l l l ll ll l ll lll l l ll ll lll l l l ll l ll l l lll ll l l l l l ll l ll l l l l l ll l l l l ll l l ll ll l l l l lll l ll ll l l l lll l l l ll l l l l l l ll l l ll l l ll ll l l l ll l l l ll ll l l ll ll llllll l l l l l l l l l l l l l ll l l l l l l l l lllllll l l ll l l l l l l ll l l l l l ll l ll l l l l l llll llll ll l l ll l l ll l l l l l llll l l ll ll l ll l l ll l l l l l ll l lll l ll l l lll l l ll ll l l ll l ll l l l lllll ll l ll l l lll l l llll ll ll l l l lll lll l l l l ll ll l l l ll l ll ll l ll ll l l lll ll l l l ll l ll l l ll ll l lll ll l ll l ll ll ll l l lll l l l lll llll l l l ll lll l l l l ll lllll l lll l l ll ll l l l l l l l l l l l l ll ll l l ll l l ll l l l l l l l ll l l