Last updated: 2022-06-20

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Knit directory: dsc_susierss/

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Rmd f36f73e Yuxin Zou 2022-06-20 wflow_publish("analysis/power_vs_fdr.Rmd")
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Rmd 94f6317 zouyuxin 2022-06-20 add code generating plots
html 45fa987 Peter Carbonetto 2022-01-26 Added grid line to in-sample-ld power vs. fdr plot.
Rmd cb03902 Peter Carbonetto 2022-01-26 workflowr::wflow_publish("analysis/power_vs_fdr.Rmd", verbose = TRUE)
html a03de31 Peter Carbonetto 2022-01-26 Build site.
Rmd 339f6ec Peter Carbonetto 2022-01-26 workflowr::wflow_publish("analysis/power_vs_fdr.Rmd")
Rmd ede4be4 Peter Carbonetto 2022-01-25 A few small edits to the power_vs_fdr analysis.
Rmd 9cd2a26 Peter Carbonetto 2022-01-25 Small edit to power_vs_fdr analysis.
html e5c993d Peter Carbonetto 2022-01-25 Added in-sample power vs. fdr plot to power_vs_fdr analysis.
Rmd ba7d922 Peter Carbonetto 2022-01-25 workflowr::wflow_publish("analysis/power_vs_fdr.Rmd")
html 0861415 Peter Carbonetto 2022-01-25 Created power_vs_fdr page.
Rmd 153840a Peter Carbonetto 2022-01-25 workflowr::wflow_publish("analysis/power_vs_fdr.Rmd")
Rmd b1ff774 Peter Carbonetto 2022-01-25 Added new power_vs_fdr analysis file.

Load some packages used to create the power vs. FDR plots:

library(ggplot2)
library(cowplot)

Load the results from the UK Biobank simulations:

res0.005 = readRDS("data/roc_pve005.rds")
res0.1 = readRDS("data/roc_pve1.rds")
res0.3 = readRDS("data/roc_pve3.rds")

Create the power vs. PDR plot comparing refinement procedure with the ‘in-sample’ LD:

pdat <-
  rbind(data.frame(method = "without refinement",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineFALSE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineFALSE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineFALSE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "with refinement",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]))
rows <- with(pdat,order(method,fdr))
pdat <- pdat[rows,]
p <- ggplot(pdat,aes(x = fdr,y = power,color = method,linetype = method)) +
  geom_line(orientation = "y") +
  geom_point(data = subset(pdat,thresh == 0.95),show.legend = FALSE) +
  scale_color_manual(values = c("orange","blue")) +
  scale_linetype_manual(values = c("solid","solid")) +
  scale_x_continuous(breaks = seq(0,0.3,0.05)) +
  scale_y_continuous(breaks = seq(0,0.3,0.05)) +
  coord_cartesian(xlim = c(0,0.3),ylim = c(0,0.3)) +
  theme_cowplot(font_size = 12) +
  theme(panel.grid.major = element_line(colour = "gainsboro",size = 0.3))
print(p)

Version Author Date
02c5d23 Yuxin Zou 2022-06-20

Create the power vs. FDR plot comparing different fine-mapping methods when they are provided with the “in sample” LD:

pdat <-
  rbind(data.frame(method = "SuSiE-RSS, est sigma",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "SuSiE-RSS, sigma=1",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDin"]]$pr[,3]),
        data.frame(method = "SuSiE-RSS, sigma=1, L=true",
                   fdr    = 1-res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDin"]]$pr[,3]),
        data.frame(method = "FINEMAP",
                   fdr    = 1-res0.005[["FINEMAP-L5-LDin"]]$pr[,1],
                   power  = res0.005[["FINEMAP-L5-LDin"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-L5-LDin"]]$pr[,3]), 
        data.frame(method = "FINEMAP, L=true",
                   fdr    = 1-res0.005[["FINEMAP-Loracle-LDin"]]$pr[,1],
                   power  = res0.005[["FINEMAP-Loracle-LDin"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-Loracle-LDin"]]$pr[,3]),
        data.frame(method = "DAP-G",
                   fdr    = 1-res0.005[["DAP-G-LDin"]]$pr[,1],
                   power  = res0.005[["DAP-G-LDin"]]$pr[,2],
                   thresh = res0.005[["DAP-G-LDin"]]$pr[,3]),
        data.frame(method = "CAVIAR",
                   fdr    = 1-res0.005[["CAVIAR-LDin"]]$pr[,1],
                   power  = res0.005[["CAVIAR-LDin"]]$pr[,2],
                   thresh = res0.005[["CAVIAR-LDin"]]$pr[,3]))
rows <- with(pdat,order(method,fdr))
pdat <- pdat[rows,]
pdat$method = factor(pdat$method, levels = c('SuSiE-RSS, est sigma',
                                             'SuSiE-RSS, sigma=1','SuSiE-RSS, sigma=1, L=true',
                                             'FINEMAP', 'FINEMAP, L=true',
                                             'DAP-G', 'CAVIAR'))
p <- ggplot(pdat,aes(x = fdr,y = power,color = method,linetype = method)) +
  geom_line(orientation = "y") +
  geom_point(data = subset(pdat,thresh == 0.95),show.legend = FALSE) +
  scale_color_manual(values = c("black","magenta","magenta","darkorange",
                                "darkorange","dodgerblue","limegreen")) +
  scale_linetype_manual(values = c("dotted","solid","dashed","solid",
                                   "dashed","solid","solid")) +
  scale_x_continuous(breaks = seq(0,0.3,0.05)) +
  scale_y_continuous(breaks = seq(0,0.3,0.05)) +
  coord_cartesian(xlim = c(0,0.3),ylim = c(0,0.3)) +
  theme_cowplot(font_size = 12) +
  theme(panel.grid.major = element_line(colour = "gainsboro",size = 0.3))
print(p)

Version Author Date
02c5d23 Yuxin Zou 2022-06-20
45fa987 Peter Carbonetto 2022-01-26
a03de31 Peter Carbonetto 2022-01-26
e5c993d Peter Carbonetto 2022-01-25

Create the power vs. FDR plot comparing different fine-mapping methods when they are provided with the “out of sample” LD:

pdat1 <-
  rbind(data.frame(method = "SuSiE-RSS (in-sample LD)",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "in-sample LD",
                   fdr    = 1 - res0.005[["CAVIAR-LDin"]]$pr[,1],
                   power  = res0.005[["CAVIAR-LDin"]]$pr[,2],
                   thresh = res0.005[["CAVIAR-LDin"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0",
                   fdr    = 1-res0.005[["CAVIAR-LDref1000"]]$pr[,1],
                   power  = res0.005[["CAVIAR-LDref1000"]]$pr[,2],
                   thresh = res0.005[["CAVIAR-LDref1000"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0.001",
                   fdr    = 1 - res0.005[["CAVIAR-LDref1000-lambda0.001"]]$pr[,1],
                   power  = res0.005[["CAVIAR-LDref1000-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["CAVIAR-LDref1000-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=estimated",
                   fdr    = 1-res0.005[["CAVIAR-LDref1000-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["CAVIAR-LDref1000-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["CAVIAR-LDref1000-lambdaEstimate"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0",
                   fdr    = 1 - res0.005[["CAVIAR-LDref500"]]$pr[,1],
                   power  = res0.005[["CAVIAR-LDref500"]]$pr[,2],
                   thresh = res0.005[["CAVIAR-LDref500"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0.001",
                   fdr    = 1 - res0.005[["CAVIAR-LDref500-lambda0.001"]]$pr[,1],
                   power  = res0.005[["CAVIAR-LDref500-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["CAVIAR-LDref500-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=500,lambda=estimated",
                   fdr    = 1 - res0.005[["CAVIAR-LDref500-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["CAVIAR-LDref500-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["CAVIAR-LDref500-lambdaEstimate"]]$pr[,3]))
pdat2 <-
  rbind(data.frame(method = "SuSiE-RSS (in-sample LD)",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "in-sample LD",
                   fdr    = 1 - res0.005[["DAP-G-LDin"]]$pr[,1],
                   power  = res0.005[["DAP-G-LDin"]]$pr[,2],
                   thresh = res0.005[["DAP-G-LDin"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0",
                   fdr    = 1-res0.005[["DAP-G-LDref1000"]]$pr[,1],
                   power  = res0.005[["DAP-G-LDref1000"]]$pr[,2],
                   thresh = res0.005[["DAP-G-LDref1000"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0.001",
                   fdr    = 1 - res0.005[["DAP-G-LDref1000-lambda0.001"]]$pr[,1],
                   power  = res0.005[["DAP-G-LDref1000-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["DAP-G-LDref1000-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=estimated",
                   fdr    = 1-res0.005[["DAP-G-LDref1000-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["DAP-G-LDref1000-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["DAP-G-LDref1000-lambdaEstimate"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0",
                   fdr    = 1 - res0.005[["DAP-G-LDref500"]]$pr[,1],
                   power  = res0.005[["DAP-G-LDref500"]]$pr[,2],
                   thresh = res0.005[["DAP-G-LDref500"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0.001",
                   fdr    = 1 - res0.005[["DAP-G-LDref500-lambda0.001"]]$pr[,1],
                   power  = res0.005[["DAP-G-LDref500-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["DAP-G-LDref500-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=500,lambda=estimated",
                   fdr    = 1 - res0.005[["DAP-G-LDref500-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["DAP-G-LDref500-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["DAP-G-LDref500-lambdaEstimate"]]$pr[,3]))
pdat3 <-
  rbind(data.frame(method = "SuSiE-RSS (in-sample LD)",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "in-sample LD",
                   fdr    = 1 - res0.005[["FINEMAP-Loracle-LDin"]]$pr[,1],
                   power  = res0.005[["FINEMAP-Loracle-LDin"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-Loracle-LDin"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0",
                   fdr    = 1-res0.005[["FINEMAP-Loracle-LDref1000"]]$pr[,1],
                   power  = res0.005[["FINEMAP-Loracle-LDref1000"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-Loracle-LDref1000"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0.001",
                   fdr    = 1 - res0.005[["FINEMAP-Loracle-LDref1000-lambda0.001"]]$pr[,1],
                   power  = res0.005[["FINEMAP-Loracle-LDref1000-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-Loracle-LDref1000-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=estimated",
                   fdr    = 1-res0.005[["FINEMAP-Loracle-LDref1000-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["FINEMAP-Loracle-LDref1000-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-Loracle-LDref1000-lambdaEstimate"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0",
                   fdr    = 1 - res0.005[["FINEMAP-Loracle-LDref500"]]$pr[,1],
                   power  = res0.005[["FINEMAP-Loracle-LDref500"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-Loracle-LDref500"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0.001",
                   fdr    = 1 - res0.005[["FINEMAP-Loracle-LDref500-lambda0.001"]]$pr[,1],
                   power  = res0.005[["FINEMAP-Loracle-LDref500-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-Loracle-LDref500-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=500,lambda=estimated",
                   fdr    = 1 - res0.005[["FINEMAP-Loracle-LDref500-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["FINEMAP-Loracle-LDref500-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-Loracle-LDref500-lambdaEstimate"]]$pr[,3]))
pdat4 <-
  rbind(data.frame(method = "SuSiE-RSS (in-sample LD)",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "in-sample LD",
                   fdr    = 1 - res0.005[["FINEMAP-L5-LDin"]]$pr[,1],
                   power  = res0.005[["FINEMAP-L5-LDin"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-L5-LDin"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0",
                   fdr    = 1-res0.005[["FINEMAP-L5-LDref1000"]]$pr[,1],
                   power  = res0.005[["FINEMAP-L5-LDref1000"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-L5-LDref1000"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0.001",
                   fdr    = 1 - res0.005[["FINEMAP-L5-LDref1000-lambda0.001"]]$pr[,1],
                   power  = res0.005[["FINEMAP-L5-LDref1000-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-L5-LDref1000-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=estimated",
                   fdr    = 1-res0.005[["FINEMAP-L5-LDref1000-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["FINEMAP-L5-LDref1000-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-L5-LDref1000-lambdaEstimate"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0",
                   fdr    = 1 - res0.005[["FINEMAP-L5-LDref500"]]$pr[,1],
                   power  = res0.005[["FINEMAP-L5-LDref500"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-L5-LDref500"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0.001",
                   fdr    = 1 - res0.005[["FINEMAP-L5-LDref500-lambda0.001"]]$pr[,1],
                   power  = res0.005[["FINEMAP-L5-LDref500-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-L5-LDref500-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=500,lambda=estimated",
                   fdr    = 1 - res0.005[["FINEMAP-L5-LDref500-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["FINEMAP-L5-LDref500-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-L5-LDref500-lambdaEstimate"]]$pr[,3]))
pdat5 <-
  rbind(data.frame(method = "SuSiE-RSS (in-sample LD)",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "in-sample LD",
                   fdr    = 1 - res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDin"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0",
                   fdr    = 1-res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0.001",
                   fdr    = 1 - res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000-lambda0.001"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=estimated",
                   fdr    = 1-res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000-lambdaEstimate"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0",
                   fdr    = 1 - res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref500"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref500"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref500"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0.001",
                   fdr    = 1 - res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref500-lambda0.001"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref500-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref500-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=500,lambda=estimated",
                   fdr    = 1 - res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref500-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref500-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref500-lambdaEstimate"]]$pr[,3]))
pdat6 <-
  rbind(data.frame(method = "SuSiE-RSS (in-sample LD)",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "in-sample LD",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDin"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0",
                   fdr    = 1-res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=0.001",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000-lambda0.001"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=1000,lambda=estimated",
                   fdr    = 1-res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000-lambdaEstimate"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref500"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref500"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref500"]]$pr[,3]),
        data.frame(method = "n=500,lambda=0.001",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref500-lambda0.001"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref500-lambda0.001"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref500-lambda0.001"]]$pr[,3]),
        data.frame(method = "n=500,lambda=estimated",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref500-lambdaEstimate"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref500-lambdaEstimate"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref500-lambdaEstimate"]]$pr[,3]))
pdat <- rbind(cbind(row = 1,col = 1,pdat1),
              cbind(row = 1,col = 2,pdat2),
              cbind(row = 2,col = 1,pdat3),
              cbind(row = 2,col = 2,pdat4),
              cbind(row = 3,col = 1,pdat5),
              cbind(row = 3,col = 2,pdat6))
rows <- with(pdat,order(row,col,method,fdr))
pdat <- pdat[rows,]
pdat$method = factor(pdat$method, levels = c('SuSiE-RSS (in-sample LD)',
                                             'in-sample LD',
                                             'n=1000,lambda=0',
                                             'n=1000,lambda=0.001', 
                                             'n=1000,lambda=estimated',
                                             'n=500,lambda=0',
                                             'n=500,lambda=0.001',
                                             'n=500,lambda=estimated'))
p <- ggplot(pdat,aes(x = fdr,y = power,color = method,linetype = method,
                     size = method)) +
  facet_grid(rows = vars(row),cols = vars(col)) +
  geom_line(orientation = "y") +
  geom_point(data = subset(pdat,thresh == 0.95),
             mapping = aes(x = fdr,y = power,color = method),
             inherit.aes = FALSE,show.legend = FALSE) +
  scale_color_manual(values = c("black","darkorange","royalblue","royalblue",
                                "royalblue","limegreen","limegreen",
                                "limegreen")) +
  scale_linetype_manual(values = c("dotted","solid","solid","dotted","solid",
                                   "solid","dotted","solid")) +
  scale_size_manual(values = c(0.75,0.75,0.3,0.75,0.75,0.3,0.75,0.75)) +
  scale_x_continuous(breaks = seq(0,0.3,0.05)) +
  scale_y_continuous(breaks = seq(0,0.3,0.05)) +
  coord_cartesian(xlim = c(0,0.3),ylim = c(0,0.3)) +
  theme_cowplot(font_size = 10) +
  theme(panel.grid.major = element_line(colour = "gainsboro",size = 0.3))
print(p)

Version Author Date
02c5d23 Yuxin Zou 2022-06-20
a03de31 Peter Carbonetto 2022-01-26

Create the power vs. FDR plot comparing different fine-mapping methods when they are provided with the “out of sample” LD with higher PVE:

pdat1 <-
  rbind(data.frame(method = "SuSiE-RSS, est sigma (in-sample LD)",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "SuSiE-RSS, sigma=1",
                   fdr    = 1 - res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,3]),
        data.frame(method = "SuSiE-RSS, sigma=1, L=TRUE",
                   fdr    = 1-res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,1],
                   power  = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,2],
                   thresh = res0.005[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,3]),
        data.frame(method = "FINEMAP",
                   fdr    = 1 - res0.005[["FINEMAP-L5-LDref1000"]]$pr[,1],
                   power  = res0.005[["FINEMAP-L5-LDref1000"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-L5-LDref1000"]]$pr[,3]),
        data.frame(method = "FINEMAP, L=TRUE",
                   fdr    = 1-res0.005[["FINEMAP-Loracle-LDref1000"]]$pr[,1],
                   power  = res0.005[["FINEMAP-Loracle-LDref1000"]]$pr[,2],
                   thresh = res0.005[["FINEMAP-Loracle-LDref1000"]]$pr[,3]),
        data.frame(method = "DAP-G",
                   fdr    = 1 - res0.005[["DAP-G-LDref1000"]]$pr[,1],
                   power  = res0.005[["DAP-G-LDref1000"]]$pr[,2],
                   thresh = res0.005[["DAP-G-LDref1000"]]$pr[,3]),
        data.frame(method = "CAVIAR",
                   fdr    = 1 - res0.005[["CAVIAR-LDref1000"]]$pr[,1],
                   power  = res0.005[["CAVIAR-LDref1000"]]$pr[,2],
                   thresh = res0.005[["CAVIAR-LDref1000"]]$pr[,3]))
pdat2 <-
  rbind(data.frame(method = "SuSiE-RSS, est sigma (in-sample LD)",
                   fdr    = 1 - res0.1[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.1[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.1[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "SuSiE-RSS, sigma=1",
                   fdr    = 1 - res0.1[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,1],
                   power  = res0.1[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,2],
                   thresh = res0.1[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,3]),
        data.frame(method = "SuSiE-RSS, sigma=1, L=TRUE",
                   fdr    = 1-res0.1[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,1],
                   power  = res0.1[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,2],
                   thresh = res0.1[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,3]),
        data.frame(method = "FINEMAP",
                   fdr    = 1 - res0.1[["FINEMAP-L5-LDref1000"]]$pr[,1],
                   power  = res0.1[["FINEMAP-L5-LDref1000"]]$pr[,2],
                   thresh = res0.1[["FINEMAP-L5-LDref1000"]]$pr[,3]),
        data.frame(method = "FINEMAP, L=TRUE",
                   fdr    = 1-res0.1[["FINEMAP-Loracle-LDref1000"]]$pr[,1],
                   power  = res0.1[["FINEMAP-Loracle-LDref1000"]]$pr[,2],
                   thresh = res0.1[["FINEMAP-Loracle-LDref1000"]]$pr[,3]),
        data.frame(method = "DAP-G",
                   fdr    = 1 - res0.1[["DAP-G-LDref1000"]]$pr[,1],
                   power  = res0.1[["DAP-G-LDref1000"]]$pr[,2],
                   thresh = res0.1[["DAP-G-LDref1000"]]$pr[,3]),
        data.frame(method = "CAVIAR",
                   fdr    = 1 - res0.1[["CAVIAR-LDref1000"]]$pr[,1],
                   power  = res0.1[["CAVIAR-LDref1000"]]$pr[,2],
                   thresh = res0.1[["CAVIAR-LDref1000"]]$pr[,3]))
pdat3 <-
  rbind(data.frame(method = "SuSiE-RSS, est sigma (in-sample LD)",
                   fdr    = 1 - res0.3[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,1],
                   power  = res0.3[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,2],
                   thresh = res0.3[["SuSiE-RSS-L10-refineTRUE-ERTRUE-LDin"]]$pr[,3]),
        data.frame(method = "SuSiE-RSS, sigma=1",
                   fdr    = 1 - res0.3[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,1],
                   power  = res0.3[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,2],
                   thresh = res0.3[["SuSiE-RSS-L10-refineTRUE-ERFALSE-LDref1000"]]$pr[,3]),
        data.frame(method = "SuSiE-RSS, sigma=1, L=TRUE",
                   fdr    = 1-res0.3[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,1],
                   power  = res0.3[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,2],
                   thresh = res0.3[["SuSiE-RSS-Ltrue-refineTRUE-ERFALSE-LDref1000"]]$pr[,3]),
        data.frame(method = "FINEMAP",
                   fdr    = 1 - res0.3[["FINEMAP-L5-LDref1000"]]$pr[,1],
                   power  = res0.3[["FINEMAP-L5-LDref1000"]]$pr[,2],
                   thresh = res0.3[["FINEMAP-L5-LDref1000"]]$pr[,3]),
        data.frame(method = "FINEMAP, L=TRUE",
                   fdr    = 1-res0.3[["FINEMAP-Loracle-LDref1000"]]$pr[,1],
                   power  = res0.3[["FINEMAP-Loracle-LDref1000"]]$pr[,2],
                   thresh = res0.3[["FINEMAP-Loracle-LDref1000"]]$pr[,3]),
        data.frame(method = "DAP-G",
                   fdr    = 1 - res0.3[["DAP-G-LDref1000"]]$pr[,1],
                   power  = res0.3[["DAP-G-LDref1000"]]$pr[,2],
                   thresh = res0.3[["DAP-G-LDref1000"]]$pr[,3]),
        data.frame(method = "CAVIAR",
                   fdr    = 1 - res0.3[["CAVIAR-LDref1000"]]$pr[,1],
                   power  = res0.3[["CAVIAR-LDref1000"]]$pr[,2],
                   thresh = res0.3[["CAVIAR-LDref1000"]]$pr[,3]))
pdat <- rbind(cbind(row = 1,col = 0.005,pdat1),
              cbind(row = 1,col = 0.1,pdat2),
              cbind(row = 1,col = 0.3,pdat3))
rows <- with(pdat,order(row,col,method,fdr))
pdat <- pdat[rows,]
pdat$method = factor(pdat$method, levels = c('SuSiE-RSS, est sigma (in-sample LD)',
                                             'SuSiE-RSS, sigma=1',
                                             'SuSiE-RSS, sigma=1, L=TRUE',
                                             'FINEMAP', 
                                             'FINEMAP, L=TRUE',
                                             'DAP-G',
                                             'CAVIAR'))
p <- ggplot(pdat,aes(x = fdr,y = power,color = method,linetype = method)) +
  facet_grid(rows = vars(row),cols = vars(col)) +
  geom_line(orientation = "y") +
  geom_point(data = subset(pdat,thresh == 0.95),
             mapping = aes(x = fdr,y = power,color = method),
             inherit.aes = FALSE,show.legend = FALSE) +
  scale_color_manual(values = c("black","magenta","magenta","darkorange","darkorange",
                                "royalblue","limegreen")) +
  scale_linetype_manual(values = c("dotted","solid",'dashed',"solid","dashed",
                                   "solid","solid")) +
  scale_x_continuous(breaks = seq(0,0.3,0.05)) +
  scale_y_continuous(breaks = seq(0,0.3,0.05)) +
  coord_cartesian(xlim = c(0,0.3),ylim = c(0,0.3)) +
  theme_cowplot(font_size = 10) +
  theme(panel.grid.major = element_line(colour = "gainsboro",size = 0.3))
print(p)

Version Author Date
02c5d23 Yuxin Zou 2022-06-20

sessionInfo()
# R version 4.1.0 (2021-05-18)
# Platform: x86_64-apple-darwin17.0 (64-bit)
# Running under: macOS Big Sur 10.16
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] cowplot_1.1.1   ggplot2_3.3.6   workflowr_1.7.0
# 
# loaded via a namespace (and not attached):
#  [1] tidyselect_1.1.2  xfun_0.30         bslib_0.3.1       purrr_0.3.4      
#  [5] colorspace_2.0-3  vctrs_0.4.1       generics_0.1.2    htmltools_0.5.2  
#  [9] yaml_2.3.5        utf8_1.2.2        rlang_1.0.2       jquerylib_0.1.4  
# [13] later_1.3.0       pillar_1.7.0      glue_1.6.2        withr_2.5.0      
# [17] DBI_1.1.2         lifecycle_1.0.1   stringr_1.4.0     munsell_0.5.0    
# [21] gtable_0.3.0      ragg_1.2.2        evaluate_0.15     knitr_1.39       
# [25] callr_3.7.0       fastmap_1.1.0     httpuv_1.6.5      ps_1.7.0         
# [29] fansi_1.0.3       highr_0.9         Rcpp_1.0.8.3      promises_1.2.0.1 
# [33] scales_1.2.0      jsonlite_1.8.0    systemfonts_1.0.4 farver_2.1.0     
# [37] fs_1.5.2          textshaping_0.3.6 digest_0.6.29     stringi_1.7.6    
# [41] processx_3.5.3    dplyr_1.0.9       getPass_0.2-2     rprojroot_2.0.3  
# [45] grid_4.1.0        cli_3.3.0         tools_4.1.0       magrittr_2.0.3   
# [49] sass_0.4.1        tibble_3.1.7      crayon_1.5.1      whisker_0.4      
# [53] pkgconfig_2.0.3   ellipsis_0.3.2    assertthat_0.2.1  rmarkdown_2.14   
# [57] httr_1.4.3        rstudioapi_0.13   R6_2.5.1          git2r_0.30.1     
# [61] compiler_4.1.0