Last updated: 2019-04-24

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Ignored files:
    Ignored:    .DS_Store
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Untracked files:
    Untracked:  data/random_data_31.rds
    Untracked:  data/random_data_31_sim_gaussian_35.rds
    Untracked:  data/random_data_31_sim_gaussian_35_get_sumstats_1.rds
    Untracked:  data/small_data_1.ld_in_file.in.ld
    Untracked:  data/small_data_1.ld_out_file.out.ld
    Untracked:  data/small_data_132.ld_in_file.in.ld
    Untracked:  data/small_data_132.ld_out_file.out.ld
    Untracked:  data/small_data_132_sim_gaussian_12.rds
    Untracked:  data/small_data_132_sim_gaussian_12_get_sumstats_1.rds
    Untracked:  data/small_data_1_sim_gaussian_2.rds
    Untracked:  data/small_data_1_sim_gaussian_2_get_sumstats_1.rds
    Untracked:  data/small_data_46.rds
    Untracked:  data/small_data_46_sim_gaussian_10.rds
    Untracked:  data/small_data_46_sim_gaussian_10_get_sumstats_2.rds
    Untracked:  data/small_data_69.ld_in_file.in.ld
    Untracked:  data/small_data_69.ld_out_file.out.ld
    Untracked:  data/small_data_69.rds
    Untracked:  data/small_data_69_sim_gaussian_3.rds
    Untracked:  data/small_data_69_sim_gaussian_3_get_sumstats_1.rds
    Untracked:  data/small_data_69_sim_gaussian_3_get_sumstats_1_susie_z_1.rds
    Untracked:  data/small_data_69_sim_gaussian_3_get_sumstats_1_susie_z_2.rds
    Untracked:  docs/figure/r_compare_add_z_finemap.Rmd/
    Untracked:  docs/figure/r_compare_add_z_susieb_ROC.Rmd/
    Untracked:  docs/figure/r_compare_susie_ROC.Rmd/
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    Untracked:  output/dscoutProblem475.rds
    Untracked:  output/dscoutProblem75.rds
    Untracked:  output/finemap_compare_random_data_null_dscout.rds
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    Untracked:  output/finemap_compare_small_data_signal_dscout_RE8.rds
    Untracked:  output/r_compare_FINEMAP_PIP_ROC.rds
    Untracked:  output/r_compare_add_z_FINEMAP_PIP_ROC.rds
    Untracked:  output/r_compare_add_z_dscout_susie_finemap_tibble.rds
    Untracked:  output/r_compare_dscout_susie_finemappip_tibble.rds
    Untracked:  output/r_compare_dscout_susie_finemappip_truth_tibble.rds
    Untracked:  output/r_compare_susieb_PIP_ROC.rds
    Untracked:  output/r_compare_susiepip_tibble.rds
    Untracked:  output/r_compare_susierss_PIP_ROC.rds
    Untracked:  output/random_data_100_sim_gaussian_null_1_get_sumstats_1_finemap_1.rds
    Untracked:  output/random_data_31_35_fit_em.rds
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    Untracked:  output/small_data_92_sim_gaussian_30_get_sumstats_2_susie_z_2.rds

Unstaged changes:
    Modified:   analysis/SuSiEDAP_Power_data31_35.Rmd
    Modified:   analysis/SuSiErssNotConverge.Rmd
    Modified:   analysis/SusieZPerformance.Rmd
    Modified:   analysis/SusieZPerformanceRE3.Rmd
    Modified:   output/dsc_susie_z_v_output.rds
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    File Version Author Date Message
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library(ggplot2)
library(cowplot)
Warning: package 'cowplot' was built under R version 3.5.2

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave
library(gridExtra)
dscout = readRDS('output/r_compare_add_z_dscout_susie_finemap_tibble.rds')
dscout$method = rep(NA, nrow(dscout))
dscout$method[!is.na(dscout$susie.maxL)] = 'susie'
dscout$method[!is.na(dscout$susie_bhat.L)] = 'susie_b'
dscout$method[!is.na(dscout$susie_bhat_add_z.L)] = 'susie_b'
dscout$method[!is.na(dscout$susie_rss.L)] = 'susie_rss'
dscout$method[!is.na(dscout$susie_rss_add_z.L)] = 'susie_rss'
dscout$method[!is.na(dscout$finemap.ld_method)] = 'finemap'
dscout$method[!is.na(dscout$finemap_add_z.ld_method)] = 'finemap'

dscout$add_z = rep(FALSE, nrow(dscout))
dscout$add_z[!is.na(dscout$susie_bhat_add_z.L)] = TRUE
dscout$add_z[!is.na(dscout$susie_rss_add_z.L)] = TRUE
dscout$add_z[!is.na(dscout$finemap_add_z.ld_method)] = TRUE

dscout$ld_method = dscout$susie_bhat.ld_method
dscout$ld_method[!is.na(dscout$susie_bhat_add_z.ld_method)] = dscout$susie_bhat_add_z.ld_method[!is.na(dscout$susie_bhat_add_z.ld_method)]
dscout$ld_method[!is.na(dscout$susie_rss.ld_method)] = dscout$susie_rss.ld_method[!is.na(dscout$susie_rss.ld_method)]
dscout$ld_method[!is.na(dscout$susie_rss_add_z.ld_method)] = dscout$susie_rss_add_z.ld_method[!is.na(dscout$susie_rss_add_z.ld_method)]
dscout$ld_method[!is.na(dscout$finemap.ld_method)] = dscout$finemap.ld_method[!is.na(dscout$finemap.ld_method)]
dscout$ld_method[!is.na(dscout$finemap_add_z.ld_method)] = dscout$finemap_add_z.ld_method[!is.na(dscout$finemap_add_z.ld_method)]

dscout$L = dscout$susie.maxL
dscout$L[!is.na(dscout$susie_bhat.L)] = dscout$susie_bhat.L[!is.na(dscout$susie_bhat.L)]
dscout$L[!is.na(dscout$susie_bhat_add_z.L)] = dscout$susie_bhat_add_z.L[!is.na(dscout$susie_bhat_add_z.L)]
dscout$L[!is.na(dscout$susie_rss.L)] = dscout$susie_rss.L[!is.na(dscout$susie_rss.L)]
dscout$L[!is.na(dscout$susie_rss_add_z.L)] = dscout$susie_rss_add_z.L[!is.na(dscout$susie_rss_add_z.L)]

dscout = dscout[,-c(2,6,8:23,26)]
colnames(dscout) = c('DSC', 'pve', 'n_signal', 'meta','N_in','converged','susie.pip', 'method', 'add_z', 'ld_method', 'L')

SuSiE RSS (L=5)

dscout.susierss = dscout[dscout$method == 'susie_rss',]
dscout.susierss = dscout.susierss[dscout.susierss$L==5,]
dscout.susierss = dscout.susierss[dscout.susierss$converged==TRUE,]
dscout.susierss.list = list('in_sample' = dscout.susierss[dscout.susierss$ld_method == 'in_sample',],
                          'out_sample' = dscout.susierss[as.logical((dscout.susierss$ld_method == 'out_sample')*(dscout.susierss$add_z == FALSE)),],
                          'out_sample.addz' = dscout.susierss[as.logical((dscout.susierss$ld_method == 'out_sample')*(dscout.susierss$add_z == TRUE)),],
                          'all' = dscout.susierss[as.logical((dscout.susierss$ld_method == 'all')*(dscout.susierss$add_z == FALSE)),],
                          'all.addz' = dscout.susierss[as.logical((dscout.susierss$ld_method == 'all')*(dscout.susierss$add_z == TRUE)),])
dat = list('in_sample'=matrix(NA, 0, 2), 'out_sample'=matrix(NA, 0, 2), 'out_sample.addz'=matrix(NA, 0, 2), 'all'=matrix(NA, 0, 2), 'all.addz'=matrix(NA, 0, 2))
for(Rtype in names(dat)){
  for(j in 1:nrow(dscout.susierss.list[[Rtype]])){
    datj = cbind(dscout.susierss.list[[Rtype]]$susie.pip[[j]], dscout.susierss.list[[Rtype]]$meta[[j]]$true_coef)
    dat[[Rtype]] = rbind(dat[[Rtype]], datj)
  }
  colnames(dat[[Rtype]]) = c('pip', 'truth')
}
bin_size = 20
bins = cbind(seq(1:bin_size)/bin_size-1/bin_size, seq(1:bin_size)/bin_size)

pip_cali = list('in_sample'=matrix(NA, nrow(bins), 3), 'out_sample'=matrix(NA, nrow(bins), 3), 'out_sample.addz'=matrix(NA, nrow(bins), 3), 'all'=matrix(NA, nrow(bins), 3), 'all.addz'=matrix(NA, nrow(bins), 3))
for(Rtype in names(pip_cali)){
  for (i in 1:nrow(bins)) {
    data_in_bin = dat[[Rtype]][which(dat[[Rtype]][,1] > bins[i,1] & dat[[Rtype]][,1] < bins[i,2]),, drop=FALSE]
    pip_cali[[Rtype]][i,1] = sum(data_in_bin[,'pip'])
    pip_cali[[Rtype]][i,2] = sum(data_in_bin[,'truth'])
    pip_cali[[Rtype]][i,3] = nrow(data_in_bin)
  }
}

for(Rtype in names(pip_cali)){
  pip_cali[[Rtype]][,c(1,2)] = pip_cali[[Rtype]][,c(1,2)] / pip_cali[[Rtype]][,3]
}
dot_plot = function(dataframe) {
  ggplot(dataframe, aes(x=mean_pip, y=observed_freq)) + 
    geom_errorbar(aes(ymin=observed_freq-se, ymax=observed_freq+se), colour="gray", size = 0.2, width=.01) + 
    geom_point(size=1.5, shape=21, fill="#002b36") + # 21 is filled circle 
    xlab("Mean PIP") +
    ylab("Observed frequency") +
    coord_cartesian(ylim=c(0,1), xlim=c(0,1)) +
    geom_abline(slope=1,intercept=0,colour='red', size=0.2) +
    expand_limits(y=0) +                        # Expand y range
    theme_cowplot()
}

Calibrated PIP

ROC

pip_cutoff = 0.05

roc_data = function(d1, cutoff = c(pip_cutoff, 0.999), connect_org = T) {
  grid = 500
  ttv = seq(1:grid)/grid
  ttv = ttv[which(ttv>=cutoff[1] & ttv<=cutoff[2])]
  rst1 = t(sapply(ttv, function(x) c(sum(d1[,2][d1[,1]>=x]), length(d1[,2][d1[,1]>=x]))))
  rst1 = cbind(rst1, sum(d1[,2]))
  rst1 = as.data.frame(rst1)
  colnames(rst1) = c('true_positive', 'total_positive', 'total_signal')
  rst2 = as.data.frame(cbind(rst1$true_positive / rst1$total_positive, rst1$true_positive / rst1$total_signal,  ttv))
  if (connect_org) {
    # make a stair to origin
    rst2 = rbind(rst2, c(max(0.995, rst2[nrow(rst2),1]), max(rst2[nrow(rst2),2]-0.01, 0), rst2[nrow(rst2),3]))
    rst2 = rbind(rst2, c(1, 0, 1))
  }
  colnames(rst2) = c('Precision', 'Recall', 'Threshold')
  return(list(counts = rst1, rates = rst2))
}

print("Computing ROC data ...")
[1] "Computing ROC data ..."
roc = list()
for (method in names(dat)) {
  roc[[method]] = roc_data(dat[[method]])
}
chunks = 0
smooth = FALSE
colors = c('#A60628', '#7A68A6', '#348ABD', '#467821', '#FF0000', '#188487', '#E2A233','#A9A9A9', '#000000', '#FF00FF', '#FFD700', '#ADFF2F', '#00FFFF')
library(scam)
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-26. For overview type 'help("mgcv-package")'.
This is scam 1.2-3.
create_chunks = function(item, n) {
  splitted = suppressWarnings(split(item, 1:n))
  return(c(splitted[[1]], splitted[[length(splitted)]][length(splitted[[length(splitted)]])]))
}
make_smooth = function(x,y,subset=chunks, smooth = FALSE){
  if (smooth) {
    if (subset < length(x) && subset > 0) {
      x = create_chunks(x, subset)
      y = create_chunks(y, subset)
    }
    dat = data.frame(cbind(x,y))
    colnames(dat) = c('x','y')
    y=predict(scam(y ~ s(x, bs = "mpi"), data = dat))
    }
  return(list(x=x,y=y))
}
add_text = function(thresholds,x,y,threshold,color,delta = -0.06) {
  idx = which(thresholds == threshold)
  text(x[idx] - delta,y[idx],labels = threshold,col = color,cex = 0.8)
  points(x[idx], y[idx])
}
labels = vector()
i = 1
for (method in names(roc)) {
  yy = make_smooth(1 - roc[[method]]$rates$Precision, roc[[method]]$rates$Recall)
  if (i == 1) {
    plot(yy$x, yy$y, t="l", col=colors[i], ylab = "power", xlab ="FDR", main = 'ROC', bty='l', lwd = 2, xlim = c(0,0.8), ylim = c(0,0.8))
  } else {
    lines(yy$x, yy$y, col=colors[i], lwd = 2, xlim = c(0,0.8), ylim = c(0,0.8))
  }
  #add_text(dat[[method]]$rates$Threshold, yy$x, yy$y, 0.9, colors[i])
  add_text(roc[[method]]$rates$Threshold, yy$x, yy$y, 0.95, colors[i])
  labels[i] = method
  i = i + 1
}
legend("topright", legend=labels, col=colors[1:i], lty=c(1,1,1), cex=0.8)

Only 1 signal

dscout.susierss = dscout[dscout$method == 'susie_rss',]
dscout.susierss = dscout.susierss[dscout.susierss$L==5,]
dscout.susierss.suc = dscout.susierss[dscout.susierss$converged==TRUE,]
dscout.susierss.suc = dscout.susierss.suc[dscout.susierss.suc$n_signal == 1,]
dscout.susierss.suc.list = list('in_sample' = dscout.susierss.suc[dscout.susierss.suc$ld_method == 'in_sample',],
                              'out_sample' = dscout.susierss.suc[as.logical((dscout.susierss.suc$ld_method == 'out_sample')*(dscout.susierss.suc$add_z == FALSE)),],
                              'out_sample.addz' = dscout.susierss.suc[as.logical((dscout.susierss.suc$ld_method == 'out_sample')*(dscout.susierss.suc$add_z == TRUE)),],
                              'all' = dscout.susierss.suc[as.logical((dscout.susierss.suc$ld_method == 'all')*(dscout.susierss.suc$add_z == FALSE)),],
                              'all.addz' = dscout.susierss.suc[as.logical((dscout.susierss.suc$ld_method == 'all')*(dscout.susierss.suc$add_z == TRUE)),])
dat = list('in_sample'=matrix(NA, 0, 2), 'out_sample'=matrix(NA, 0, 2), 'out_sample.addz'=matrix(NA, 0, 2), 'all'=matrix(NA, 0, 2), 'all.addz'=matrix(NA, 0, 2))
for(method in names(dat)){
  for(j in 1:nrow(dscout.susierss.suc.list[[method]])){
    datj = cbind(dscout.susierss.suc.list[[method]]$susie.pip[[j]], dscout.susierss.suc.list[[method]]$meta[[j]]$true_coef)
    dat[[method]] = rbind(dat[[method]], datj)
  }
  colnames(dat[[method]]) = c('pip', 'truth')
}
bin_size = 20
bins = cbind(seq(1:bin_size)/bin_size-1/bin_size, seq(1:bin_size)/bin_size)

pip_cali = list('in_sample'=matrix(NA, nrow(bins), 3), 'out_sample'=matrix(NA, nrow(bins), 3), 'out_sample.addz'=matrix(NA, nrow(bins), 3), 'all'=matrix(NA, nrow(bins), 3), 'all.addz'=matrix(NA, nrow(bins), 3))
for(Rtype in names(pip_cali)){
  for (i in 1:nrow(bins)) {
    data_in_bin = dat[[Rtype]][which(dat[[Rtype]][,1] > bins[i,1] & dat[[Rtype]][,1] < bins[i,2]),, drop=FALSE]
    pip_cali[[Rtype]][i,1] = sum(data_in_bin[,'pip'])
    pip_cali[[Rtype]][i,2] = sum(data_in_bin[,'truth'])
    pip_cali[[Rtype]][i,3] = nrow(data_in_bin)
  }
}

for(Rtype in names(pip_cali)){
  pip_cali[[Rtype]][,c(1,2)] = pip_cali[[Rtype]][,c(1,2)] / pip_cali[[Rtype]][,3]
}

Calibrated PIP

g = list()
idx = 0
for(Rtype in names(pip_cali)){
  idx = idx + 1
  pip_cali[[Rtype]][,3] = sqrt(pip_cali[[Rtype]][,2] * (1 - pip_cali[[Rtype]][,2]) / pip_cali[[Rtype]][,3]) * 2
  pip_cali[[Rtype]] = as.data.frame(pip_cali[[Rtype]])
  colnames(pip_cali[[Rtype]]) = c("mean_pip", "observed_freq", "se")
  g[[Rtype]] = dot_plot(pip_cali[[Rtype]]) + ggtitle(Rtype)
}
grid.arrange(g[[1]], g[[2]], g[[3]], g[[4]], g[[5]],nrow = 2)

ROC

print("Computing ROC data ...")
[1] "Computing ROC data ..."
roc = list()
for (method in names(dat)) {
  roc[[method]] = roc_data(dat[[method]])
}
labels = vector()
i = 1
for (method in names(roc)) {
  yy = make_smooth(1 - roc[[method]]$rates$Precision, roc[[method]]$rates$Recall)
  if (i == 1) {
    plot(yy$x, yy$y, t="l", col=colors[i], ylab = "power", xlab ="FDR", main = 'ROC', bty='l', lwd = 2, xlim = c(0,0.8), ylim = c(0,0.8))
  } else {
    lines(yy$x, yy$y, col=colors[i], lwd = 2, xlim = c(0,0.8), ylim = c(0,0.8))
  }
  #add_text(dat[[method]]$rates$Threshold, yy$x, yy$y, 0.9, colors[i])
  add_text(roc[[method]]$rates$Threshold, yy$x, yy$y, 0.95, colors[i])
  labels[i] = method
  i = i + 1
}
legend("topright", legend=labels, col=colors[1:i], lty=c(1,1,1), cex=0.8)

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.4

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] scam_1.2-3    mgcv_1.8-26   nlme_3.1-137  gridExtra_2.3 cowplot_0.9.4
[6] ggplot2_3.1.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0        compiler_3.5.1    pillar_1.3.1     
 [4] git2r_0.24.0      plyr_1.8.4        workflowr_1.1.1  
 [7] bindr_0.1.1       R.methodsS3_1.7.1 R.utils_2.7.0    
[10] tools_3.5.1       digest_0.6.18     lattice_0.20-38  
[13] evaluate_0.12     tibble_2.0.1      gtable_0.2.0     
[16] pkgconfig_2.0.2   rlang_0.3.1       Matrix_1.2-15    
[19] yaml_2.2.0        bindrcpp_0.2.2    withr_2.1.2      
[22] stringr_1.3.1     dplyr_0.7.8       knitr_1.20       
[25] rprojroot_1.3-2   grid_3.5.1        tidyselect_0.2.5 
[28] glue_1.3.0        R6_2.3.0          rmarkdown_1.11   
[31] purrr_0.2.5       magrittr_1.5      whisker_0.3-2    
[34] splines_3.5.1     backports_1.1.3   scales_1.0.0     
[37] htmltools_0.3.6   assertthat_0.2.0  colorspace_1.4-0 
[40] labeling_0.3      stringi_1.2.4     lazyeval_0.2.1   
[43] munsell_0.5.0     crayon_1.3.4      R.oo_1.22.0

This reproducible R Markdown analysis was created with workflowr 1.1.1