data-microbiome: A remade example for evaluating performance of methods of a...
In markrobinsonuzh/benchmarkR: benchmarkR.
Description
Usage
Author(s)
References
Examples
Description
A remade example for evaluating performance of methods of a simulated “microbiome” dataset from McMurdie et al [1]. The original figure (Figure 6 doi:10.1371/journal.pcbi.1003531.g006) evaluates AUC, specificity and sensitivity of different methods across different conditions, including effected sizes, number of samples, number of reads and different normalization methods. We present powerFDR curves and modify the original figure focusing on one side (effected sizes=c(2.5,5,10,20), number of samples=10 and number of reads=50000, methods=c(“edge”,“DESeq”)) but not every condition of settings. The details procedures can be found on examples below.)
Usage
1
Author(s)
Xiaobei zhou and Mark D. Robinson
References
[1]McMurdie PJ, Holmes S (2014). Waste not, want not: why rarefying microbiome data isinadmissible.
PLoS computational biology 2014;10(4):e1003531.
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.
pcbi.1003531.
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# powerFDR curves
library(benchmarkR)
data(microbiome)
powerFDR(microbiome[[3]], xlim=c(0,0.1),ylim=c(0.2,1), pch=1, lwd=5, lwd.line=5,
legend=list("bottomright",lty=1,pch=NA, lwd=4,cex=1.5))
powerFDR(microbiome[[4]], add=TRUE, legend=NULL, pch=2, lwd=5, lwd.line=5)
powerFDR(microbiome[[1]], add=TRUE, legend=NULL, pch=0, lwd=5, lwd.line=5)
powerFDR(microbiome[[2]], add=TRUE, legend=NULL, pch=6,lwd=5, lwd.line=5)
legend <- paste0("EffectSize=",c(2.5,5,10,20))
legend("topright",lty=1, pch=c(1,2,0,6), legend=legend, cex=1.5, lwd=2)
# modify Figure 6
##download the link file:
##\url{http://journals.plos.org/ploscompbiol/article/asset?unique&id=info:doi/10.1371/journal.pcbi.1003531.s001}
##run the Rcode on ``simulation-differential-abundance/simulation-differential-abundance-server.html"
##run the following codes
##superlist=list(DESeq=DESeqreslist, edgeR=edgeRreslist)
##nreadskeep <- 50000
##nsampleskeep <- 10
##maindat = transform(dfmeansd, group=paste0(nsamples,Normalization,Method))
##Normalizationkeep <- "Rarefied"
##maindat <- subset(maindat, as.numeric(nreads) %in% nreadskeep)
##maindat <- subset(maindat, as.numeric(nsamples) %in% nsampleskeep)
##maindat <- subset(maindat, !Normalization %in% Normalizationkeep)
##pSpecificity
##pSpecificity=ggplot(maindat, aes(EffectSize,Specificity,color=Normalization,
## alpha=factor(nsamples))) + geom_path(size=pathsize, aes(group = group)) +
## geom_errorbar(aes(ymax=Specificity + sd.Specificity, ymin=Specificity -
## sd.Specificity), width=errorwidth, alpha=erroralpha, size=errorsize,
## position="dodge") + geom_point(aes(shape=Normalization), size=pointsize) +
## facet_grid(nreads ~ Method, labeller=label_parsed) + scale_alpha_discrete(range=c(1,
## 0.4), guide = guide_legend(title=nsampleslegtitle)) +
## scale_colour_discrete(guide=guide_legend(title=normlegtitle)) +
## scale_shape_discrete(guide=guide_legend(title = normlegtitle)) +
## scale_y_continuous(breaks = c(0.6,0.9,0.95,0.975,1), limits=c(0.95,1),
## oob=function(x, limits) {x}) + ggtitle("Differential Abundance Specificity")
##pSensitivity
##pSensitivity = ggplot(maindat, aes(EffectSize,Sensitivity,color = Normalization,
## alpha=factor(nsamples))) + geom_path(size=pathsize, aes(group=group)) +
## geom_errorbar(aes(ymax=Sensitivity + sd.Sensitivity, ymin=Sensitivity -
## sd.Sensitivity), width=errorwidth, alpha=erroralpha, size=errorsize,
## position = "dodge") + geom_point(aes(shape=Normalization), size=pointsize) +
## facet_grid(nreads ~ Method, labeller=label_parsed) + scale_alpha_discrete(range = c(1,
## 0.4), guide = guide_legend(title = nsampleslegtitle)) +
## scale_colour_discrete(guide=guide_legend(title=normlegtitle)) +
## scale_shape_discrete(guide=guide_legend(title=normlegtitle)) +
## scale_y_continuous(breaks=c(0.2,0.5,0.6,0.8,1), limits=c(0.3, 1),
## oob=function(x, limits) {x}) + xlab("Effect Size") +
## ggtitle("Differential Abundance Sensitivity")
##pDAFP
##pDAFP = ggplot(maindat, aes(EffectSize,FP,color=Normalization)) + geom_path(size=0.5) +
## geom_errorbar(aes(ymax=FP+sd.FP, ymin=FP-sd.FP), width=0.2, alpha=0.25,
## size=0.35, position="dodge") + geom_point(aes(shape=Normalization),size=1.5) +
## facet_grid(nreads ~ Method) + scale_y_continuous(breaks=c(-0.05,0,0.02,0.05,0.075,0.1))
## + coord_cartesian(ylim=c(-0.1,0.1)) + ylab("False Positive Rate") +
## ggtitle("Differential Abundance Detection Performance,False Positives")
##remove main title
##pSpecificity$labels$title <- NULL
##pSensitivity$labels$title <- NULL
##pDApower$labels$title <- NULL
##pDAFP$labels$title <- NULL
##Remove margins
##pSensitivity = pSensitivity + theme(text=element_text(size=plotlabtextsize),
## plot.margin=unit(c(0,0,0,0),"cm"),legend.position="top",
## legend.box="horizontal",legend.margin=unit(-0.5,"cm"))
##pSpecificity = pSpecificity+theme(text=element_text(size=plotlabtextsize),
## plot.margin=unit(c(0,0,0,0),"cm"))
##pDAFP = pDAFP+theme(text=element_text(size=plotlabtextsize),
## plot.margin=unit(c(0,0,0,0),"cm"))
##pSensitivity = pSensitivity+theme(axis.text.x=element_blank(),
## axis.title.x=element_blank(),plot.margin=unit(c(0, 0, -0.4, 0),"cm"))
##pSpecificity = pSpecificity + theme(axis.text.x=element_blank(),
## axis.title.x=element_blank(),plot.margin=unit(c(0, 0, -0.4, 0),"cm"))
##Remove redundant x-labels, legends
##pSensitivity = pSensitivity+theme(strip.background=element_blank())
##pSpecificity = pSpecificity+theme(legend.position="none",
## strip.text.x=element_blank(),strip.background=element_blank())
##pDAFP = pDAFP + theme(legend.position="none", strip.text.x=element_blank(),
## strip.background=element_blank())
##Create multi-plot grid layout
##Layout = grid.layout(3,1,heights=c(1.5, 1, 1))
##grid.show.layout(Layout)
##pdf("Figure_6_modify.pdf",width=6.8, height=4)
##pushViewport(viewport(layout = Layout))
##print(pSensitivity, vp=viewport(layout.pos.row=1,layout.pos.col=1))
##print(pSpecificity, vp=viewport(layout.pos.row=2,layout.pos.col=1))
##print(pDAFP, vp=viewport(layout.pos.row=3,layout.pos.col=1))
##dev.off()
markrobinsonuzh/benchmarkR documentation built on May 21, 2019, 12:24 p.m.
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Description Usage Author(s) References Examples
Description
A remade example for evaluating performance of methods of a simulated “microbiome” dataset from McMurdie et al [1]. The original figure (Figure 6 doi:10.1371/journal.pcbi.1003531.g006) evaluates AUC, specificity and sensitivity of different methods across different conditions, including effected sizes, number of samples, number of reads and different normalization methods. We present powerFDR curves and modify the original figure focusing on one side (effected sizes=c(2.5,5,10,20), number of samples=10 and number of reads=50000, methods=c(“edge”,“DESeq”)) but not every condition of settings. The details procedures can be found on examples below.)
Usage
1 |
Author(s)
Xiaobei zhou and Mark D. Robinson
References
[1]McMurdie PJ, Holmes S (2014). Waste not, want not: why rarefying microbiome data isinadmissible. PLoS computational biology 2014;10(4):e1003531. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal. pcbi.1003531.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | # powerFDR curves
library(benchmarkR)
data(microbiome)
powerFDR(microbiome[[3]], xlim=c(0,0.1),ylim=c(0.2,1), pch=1, lwd=5, lwd.line=5,
legend=list("bottomright",lty=1,pch=NA, lwd=4,cex=1.5))
powerFDR(microbiome[[4]], add=TRUE, legend=NULL, pch=2, lwd=5, lwd.line=5)
powerFDR(microbiome[[1]], add=TRUE, legend=NULL, pch=0, lwd=5, lwd.line=5)
powerFDR(microbiome[[2]], add=TRUE, legend=NULL, pch=6,lwd=5, lwd.line=5)
legend <- paste0("EffectSize=",c(2.5,5,10,20))
legend("topright",lty=1, pch=c(1,2,0,6), legend=legend, cex=1.5, lwd=2)
# modify Figure 6
##download the link file:
##\url{http://journals.plos.org/ploscompbiol/article/asset?unique&id=info:doi/10.1371/journal.pcbi.1003531.s001}
##run the Rcode on ``simulation-differential-abundance/simulation-differential-abundance-server.html"
##run the following codes
##superlist=list(DESeq=DESeqreslist, edgeR=edgeRreslist)
##nreadskeep <- 50000
##nsampleskeep <- 10
##maindat = transform(dfmeansd, group=paste0(nsamples,Normalization,Method))
##Normalizationkeep <- "Rarefied"
##maindat <- subset(maindat, as.numeric(nreads) %in% nreadskeep)
##maindat <- subset(maindat, as.numeric(nsamples) %in% nsampleskeep)
##maindat <- subset(maindat, !Normalization %in% Normalizationkeep)
##pSpecificity
##pSpecificity=ggplot(maindat, aes(EffectSize,Specificity,color=Normalization,
## alpha=factor(nsamples))) + geom_path(size=pathsize, aes(group = group)) +
## geom_errorbar(aes(ymax=Specificity + sd.Specificity, ymin=Specificity -
## sd.Specificity), width=errorwidth, alpha=erroralpha, size=errorsize,
## position="dodge") + geom_point(aes(shape=Normalization), size=pointsize) +
## facet_grid(nreads ~ Method, labeller=label_parsed) + scale_alpha_discrete(range=c(1,
## 0.4), guide = guide_legend(title=nsampleslegtitle)) +
## scale_colour_discrete(guide=guide_legend(title=normlegtitle)) +
## scale_shape_discrete(guide=guide_legend(title = normlegtitle)) +
## scale_y_continuous(breaks = c(0.6,0.9,0.95,0.975,1), limits=c(0.95,1),
## oob=function(x, limits) {x}) + ggtitle("Differential Abundance Specificity")
##pSensitivity
##pSensitivity = ggplot(maindat, aes(EffectSize,Sensitivity,color = Normalization,
## alpha=factor(nsamples))) + geom_path(size=pathsize, aes(group=group)) +
## geom_errorbar(aes(ymax=Sensitivity + sd.Sensitivity, ymin=Sensitivity -
## sd.Sensitivity), width=errorwidth, alpha=erroralpha, size=errorsize,
## position = "dodge") + geom_point(aes(shape=Normalization), size=pointsize) +
## facet_grid(nreads ~ Method, labeller=label_parsed) + scale_alpha_discrete(range = c(1,
## 0.4), guide = guide_legend(title = nsampleslegtitle)) +
## scale_colour_discrete(guide=guide_legend(title=normlegtitle)) +
## scale_shape_discrete(guide=guide_legend(title=normlegtitle)) +
## scale_y_continuous(breaks=c(0.2,0.5,0.6,0.8,1), limits=c(0.3, 1),
## oob=function(x, limits) {x}) + xlab("Effect Size") +
## ggtitle("Differential Abundance Sensitivity")
##pDAFP
##pDAFP = ggplot(maindat, aes(EffectSize,FP,color=Normalization)) + geom_path(size=0.5) +
## geom_errorbar(aes(ymax=FP+sd.FP, ymin=FP-sd.FP), width=0.2, alpha=0.25,
## size=0.35, position="dodge") + geom_point(aes(shape=Normalization),size=1.5) +
## facet_grid(nreads ~ Method) + scale_y_continuous(breaks=c(-0.05,0,0.02,0.05,0.075,0.1))
## + coord_cartesian(ylim=c(-0.1,0.1)) + ylab("False Positive Rate") +
## ggtitle("Differential Abundance Detection Performance,False Positives")
##remove main title
##pSpecificity$labels$title <- NULL
##pSensitivity$labels$title <- NULL
##pDApower$labels$title <- NULL
##pDAFP$labels$title <- NULL
##Remove margins
##pSensitivity = pSensitivity + theme(text=element_text(size=plotlabtextsize),
## plot.margin=unit(c(0,0,0,0),"cm"),legend.position="top",
## legend.box="horizontal",legend.margin=unit(-0.5,"cm"))
##pSpecificity = pSpecificity+theme(text=element_text(size=plotlabtextsize),
## plot.margin=unit(c(0,0,0,0),"cm"))
##pDAFP = pDAFP+theme(text=element_text(size=plotlabtextsize),
## plot.margin=unit(c(0,0,0,0),"cm"))
##pSensitivity = pSensitivity+theme(axis.text.x=element_blank(),
## axis.title.x=element_blank(),plot.margin=unit(c(0, 0, -0.4, 0),"cm"))
##pSpecificity = pSpecificity + theme(axis.text.x=element_blank(),
## axis.title.x=element_blank(),plot.margin=unit(c(0, 0, -0.4, 0),"cm"))
##Remove redundant x-labels, legends
##pSensitivity = pSensitivity+theme(strip.background=element_blank())
##pSpecificity = pSpecificity+theme(legend.position="none",
## strip.text.x=element_blank(),strip.background=element_blank())
##pDAFP = pDAFP + theme(legend.position="none", strip.text.x=element_blank(),
## strip.background=element_blank())
##Create multi-plot grid layout
##Layout = grid.layout(3,1,heights=c(1.5, 1, 1))
##grid.show.layout(Layout)
##pdf("Figure_6_modify.pdf",width=6.8, height=4)
##pushViewport(viewport(layout = Layout))
##print(pSensitivity, vp=viewport(layout.pos.row=1,layout.pos.col=1))
##print(pSpecificity, vp=viewport(layout.pos.row=2,layout.pos.col=1))
##print(pDAFP, vp=viewport(layout.pos.row=3,layout.pos.col=1))
##dev.off()
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