Last updated: 2019-02-24
workflowr checks: (Click a bullet for more information) ✔ R Markdown file: up-to-date
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
✔ Environment: empty
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
✔ Seed:
set.seed(20190115)
The command set.seed(20190115) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
✔ Session information: recorded
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
✔ Repository version: 30aae07
wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: .sos/
Ignored: data/.DS_Store
Ignored: output/.DS_Store
Untracked files:
Untracked: docs/figure/test.Rmd/
Untracked: output/dscoutProblem475.rds
Untracked: output/dscoutProblem75.rds
Untracked: output/finemap_compare_random_data_null_dscout.rds
Untracked: output/finemap_compare_random_data_signal_dscout.rds
Untracked: output/finemap_compare_small_data_signal_dscout.rds
Untracked: output/finemap_compare_small_data_signal_dscout_RE8.rds
Untracked: output/random_data_100_sim_gaussian_null_1_get_sumstats_1_finemap_1.rds
Untracked: output/random_data_76.rds
Untracked: output/random_data_76_sim_gaussian_8.rds
Untracked: output/random_data_76_sim_gaussian_8_get_sumstats_1.rds
Untracked: output/small_data_42_sim_gaussian_36_get_sumstats_2_susie_z_2.rds
Untracked: output/small_data_92_sim_gaussian_30_get_sumstats_2_susie_z_2.rds
Unstaged changes:
Modified: analysis/SuSiEzRProblem42.Rmd
Modified: analysis/SuSiEzRProblem92.Rmd
Modified: output/dsc_susie_z_v_output.rds
DAP-z is not stable, so we exclude it from the simulation. It has two problems:
The algorithm doesn’t stop.
It uses more than 6GB memories.
There is no credible set for signals in FINEMAP. We generate the credible set of causal signals as the union of the variables included in the smallest set of causal configurations that already covered 95% of the total posterior probability.
We use datasets from dsc-finemap, which is from GTExV8 genotypes. The genotype matrix X is 574 by 1001.
We simulate a gaussian y under various number of causal variables, total percentage of variance explained (PVE) and whether the signals have equal effect. The reason I control the effect size is that if we random generate the effect size for the signals, some signals have large effect size by chance. Therefore these signals have larger PVE.
We fit SuSiE with L = 5, FINEMAP with max 5 causals.
In SuSiE z, we truncate the eigenvalue of R at \(10^{-3}\), instead of \(10^{-8}\). There are some failed cases. Problem 92
library(dscrutils)
dscout = dscquery('output/finemap_compare_small_data_signal', target='method sim_gaussian.pve sim_gaussian.n_signal sim_gaussian.effect_weight score_susie.objective score_susie.converged score.total score.valid score.size score.signal_pip score_susie.purity score_susie.top score_susie.overlap ',group = c("score: score_susie score_finemap", "method: susie_z susie_z_init finemap"))
colnames(dscout) = c('DSC', 'method', 'output.file', 'pve', 'n_signal', 'effect_weight', 'score', 'objective', 'converged', 'total', 'valid', 'size', 'signal_pip', 'purity', 'top', 'overlap')
dscout$effect_weight[which(dscout$effect_weight == 'rep(1/n_signal, n_signal)')] = 'equal'
dscout$effect_weight[which(dscout$effect_weight != 'equal')] = 'notequal'library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(knitr)
library(kableExtra)
library(susieR)
dscout = readRDS('output/finemap_compare_small_data_signal_dscout.rds')
dscout.susie = dscout[dscout$method == 'susie_z',]
dscout.susie.init = dscout[dscout$method == 'susie_z_init',]
dscout.finemap = dscout[dscout$method == 'finemap',]success.susie = aggregate(converged~effect_weight+n_signal+pve, dscout.susie, sum)
times.susie = aggregate(DSC~effect_weight+n_signal+pve, dscout.susie, length)
success.times.susie = merge(times.susie, success.susie)
success.times.susie$Fail.susie = success.times.susie$DSC - success.times.susie$converged
colnames(success.times.susie)[colnames(success.times.susie) == 'DSC'] = 'times'
success.times.susie = success.times.susie[,-5]
success.susie.init = aggregate(converged~effect_weight+n_signal+pve, dscout.susie.init, sum)
times.susie.init = aggregate(DSC~effect_weight+n_signal+pve, dscout.susie.init, length)
success.times.susie.init = merge(times.susie, success.susie.init)
success.times.susie.init$Fail.susie.init = success.times.susie.init$DSC - success.times.susie.init$converged
colnames(success.times.susie.init)[colnames(success.times.susie.init) == 'DSC'] = 'times'
success.times.susie.init = success.times.susie.init[,-5]
success.times = Reduce(function(...) merge(...),
list(success.times.susie, success.times.susie.init))
success.times %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F) | effect_weight | n_signal | pve | times | Fail.susie | Fail.susie.init |
|---|---|---|---|---|---|
| equal | 1 | 0.05 | 150 | 0 | 0 |
| equal | 1 | 0.10 | 150 | 0 | 0 |
| equal | 1 | 0.20 | 150 | 0 | 0 |
| equal | 1 | 0.60 | 150 | 0 | 0 |
| equal | 1 | 0.80 | 150 | 2 | 0 |
| equal | 10 | 0.05 | 150 | 0 | 0 |
| equal | 10 | 0.10 | 150 | 0 | 0 |
| equal | 10 | 0.20 | 150 | 0 | 0 |
| equal | 10 | 0.60 | 150 | 0 | 0 |
| equal | 10 | 0.80 | 150 | 0 | 0 |
| equal | 3 | 0.05 | 150 | 0 | 0 |
| equal | 3 | 0.10 | 150 | 0 | 0 |
| equal | 3 | 0.20 | 150 | 0 | 0 |
| equal | 3 | 0.60 | 150 | 0 | 0 |
| equal | 3 | 0.80 | 150 | 0 | 0 |
| equal | 5 | 0.05 | 150 | 0 | 0 |
| equal | 5 | 0.10 | 150 | 0 | 0 |
| equal | 5 | 0.20 | 150 | 0 | 0 |
| equal | 5 | 0.60 | 150 | 0 | 0 |
| equal | 5 | 0.80 | 150 | 0 | 0 |
| notequal | 1 | 0.05 | 150 | 0 | 0 |
| notequal | 1 | 0.10 | 150 | 0 | 0 |
| notequal | 1 | 0.20 | 150 | 0 | 0 |
| notequal | 1 | 0.60 | 150 | 0 | 0 |
| notequal | 1 | 0.80 | 150 | 2 | 0 |
| notequal | 10 | 0.05 | 150 | 0 | 0 |
| notequal | 10 | 0.10 | 150 | 0 | 0 |
| notequal | 10 | 0.20 | 150 | 0 | 0 |
| notequal | 10 | 0.60 | 150 | 0 | 0 |
| notequal | 10 | 0.80 | 150 | 2 | 2 |
| notequal | 3 | 0.05 | 150 | 0 | 0 |
| notequal | 3 | 0.10 | 150 | 0 | 0 |
| notequal | 3 | 0.20 | 150 | 0 | 0 |
| notequal | 3 | 0.60 | 150 | 1 | 1 |
| notequal | 3 | 0.80 | 150 | 2 | 1 |
| notequal | 5 | 0.05 | 150 | 0 | 0 |
| notequal | 5 | 0.10 | 150 | 0 | 0 |
| notequal | 5 | 0.20 | 150 | 0 | 0 |
| notequal | 5 | 0.60 | 150 | 1 | 1 |
| notequal | 5 | 0.80 | 150 | 3 | 3 |
size.susie = aggregate(size~effect_weight+n_signal+pve, dscout.susie, FUN = function(x) round(mean(x), 2))
colnames(size.susie)[colnames(size.susie) == 'size'] <- 'size.susie'
size.susie.init = aggregate(size~effect_weight+n_signal+pve, dscout.susie.init, FUN = function(x) round(mean(x), 2))
colnames(size.susie.init)[colnames(size.susie.init) == 'size'] <- 'size.susie.init'
size.finemap = aggregate(size~effect_weight+n_signal+pve, dscout.finemap, FUN = function(x) round(mean(x), 2))
colnames(size.finemap)[colnames(size.finemap) == 'size'] <- 'size.finemap'
size = Reduce(function(...) merge(...),
list(size.susie, size.susie.init, size.finemap))
size %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F) | effect_weight | n_signal | pve | size.susie | size.susie.init | size.finemap |
|---|---|---|---|---|---|
| equal | 1 | 0.05 | 7.84 | 9.11 | 998.07 |
| equal | 1 | 0.10 | 6.12 | 6.52 | 992.40 |
| equal | 1 | 0.20 | 11.11 | 10.75 | 968.32 |
| equal | 1 | 0.60 | 17.34 | 16.99 | 942.25 |
| equal | 1 | 0.80 | 4.60 | 4.02 | 563.46 |
| equal | 10 | 0.05 | 12.06 | 12.37 | 996.23 |
| equal | 10 | 0.10 | 11.34 | 11.02 | 942.31 |
| equal | 10 | 0.20 | 14.03 | 13.64 | 681.95 |
| equal | 10 | 0.60 | 25.43 | 25.44 | 229.12 |
| equal | 10 | 0.80 | 18.72 | 18.59 | 147.27 |
| equal | 3 | 0.05 | 9.97 | 10.53 | 990.67 |
| equal | 3 | 0.10 | 7.77 | 9.41 | 905.46 |
| equal | 3 | 0.20 | 12.04 | 12.30 | 702.05 |
| equal | 3 | 0.60 | 20.63 | 20.94 | 427.31 |
| equal | 3 | 0.80 | 12.26 | 12.18 | 174.79 |
| equal | 5 | 0.05 | 12.20 | 12.19 | 992.99 |
| equal | 5 | 0.10 | 12.87 | 12.82 | 942.01 |
| equal | 5 | 0.20 | 11.99 | 12.01 | 580.85 |
| equal | 5 | 0.60 | 19.21 | 19.22 | 165.55 |
| equal | 5 | 0.80 | 18.91 | 18.78 | 99.75 |
| notequal | 1 | 0.05 | 7.84 | 9.11 | 998.08 |
| notequal | 1 | 0.10 | 6.12 | 6.52 | 992.31 |
| notequal | 1 | 0.20 | 11.11 | 10.75 | 967.49 |
| notequal | 1 | 0.60 | 17.34 | 16.99 | 941.80 |
| notequal | 1 | 0.80 | 4.60 | 4.02 | 565.49 |
| notequal | 10 | 0.05 | 9.04 | 8.87 | 992.35 |
| notequal | 10 | 0.10 | 9.44 | 8.79 | 967.02 |
| notequal | 10 | 0.20 | 14.33 | 14.41 | 902.51 |
| notequal | 10 | 0.60 | 19.50 | 19.50 | 746.36 |
| notequal | 10 | 0.80 | 12.47 | 12.25 | 664.63 |
| notequal | 3 | 0.05 | 12.83 | 14.16 | 993.11 |
| notequal | 3 | 0.10 | 11.06 | 12.86 | 972.49 |
| notequal | 3 | 0.20 | 15.63 | 15.77 | 885.79 |
| notequal | 3 | 0.60 | 22.00 | 22.05 | 712.27 |
| notequal | 3 | 0.80 | 12.33 | 12.07 | 548.23 |
| notequal | 5 | 0.05 | 10.44 | 10.91 | 994.36 |
| notequal | 5 | 0.10 | 9.64 | 9.62 | 979.18 |
| notequal | 5 | 0.20 | 12.39 | 12.28 | 927.49 |
| notequal | 5 | 0.60 | 19.27 | 19.27 | 855.17 |
| notequal | 5 | 0.80 | 13.61 | 13.61 | 644.88 |
purity.susie = aggregate(purity~effect_weight+n_signal+pve, dscout.susie, FUN = function(x) round(mean(x), 3))
colnames(purity.susie)[colnames(purity.susie) == 'purity'] <- 'purity.susie'
purity.susie.init = aggregate(purity~effect_weight+n_signal+pve, dscout.susie.init, FUN = function(x) round(mean(x), 3))
colnames(purity.susie.init)[colnames(purity.susie.init) == 'purity'] <- 'purity.susie.init'
purity = Reduce(function(...) merge(...),
list(purity.susie, purity.susie.init))
purity %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F) | effect_weight | n_signal | pve | purity.susie | purity.susie.init |
|---|---|---|---|---|
| equal | 1 | 0.05 | 0.927 | 0.881 |
| equal | 1 | 0.10 | 0.968 | 0.970 |
| equal | 1 | 0.20 | 0.978 | 0.987 |
| equal | 1 | 0.60 | 0.906 | 0.914 |
| equal | 1 | 0.80 | 0.666 | 0.683 |
| equal | 10 | 0.05 | 0.769 | 0.760 |
| equal | 10 | 0.10 | 0.911 | 0.893 |
| equal | 10 | 0.20 | 0.947 | 0.952 |
| equal | 10 | 0.60 | 0.943 | 0.944 |
| equal | 10 | 0.80 | 0.893 | 0.892 |
| equal | 3 | 0.05 | 0.836 | 0.823 |
| equal | 3 | 0.10 | 0.939 | 0.931 |
| equal | 3 | 0.20 | 0.965 | 0.963 |
| equal | 3 | 0.60 | 0.947 | 0.946 |
| equal | 3 | 0.80 | 0.863 | 0.866 |
| equal | 5 | 0.05 | 0.787 | 0.786 |
| equal | 5 | 0.10 | 0.917 | 0.916 |
| equal | 5 | 0.20 | 0.959 | 0.959 |
| equal | 5 | 0.60 | 0.959 | 0.960 |
| equal | 5 | 0.80 | 0.905 | 0.904 |
| notequal | 1 | 0.05 | 0.927 | 0.881 |
| notequal | 1 | 0.10 | 0.968 | 0.970 |
| notequal | 1 | 0.20 | 0.978 | 0.987 |
| notequal | 1 | 0.60 | 0.906 | 0.914 |
| notequal | 1 | 0.80 | 0.666 | 0.683 |
| notequal | 10 | 0.05 | 0.937 | 0.939 |
| notequal | 10 | 0.10 | 0.949 | 0.960 |
| notequal | 10 | 0.20 | 0.953 | 0.956 |
| notequal | 10 | 0.60 | 0.955 | 0.956 |
| notequal | 10 | 0.80 | 0.870 | 0.871 |
| notequal | 3 | 0.05 | 0.918 | 0.917 |
| notequal | 3 | 0.10 | 0.941 | 0.943 |
| notequal | 3 | 0.20 | 0.955 | 0.951 |
| notequal | 3 | 0.60 | 0.941 | 0.940 |
| notequal | 3 | 0.80 | 0.828 | 0.832 |
| notequal | 5 | 0.05 | 0.913 | 0.913 |
| notequal | 5 | 0.10 | 0.951 | 0.951 |
| notequal | 5 | 0.20 | 0.945 | 0.946 |
| notequal | 5 | 0.60 | 0.944 | 0.944 |
| notequal | 5 | 0.80 | 0.833 | 0.833 |
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.susie, sum)
total = aggregate(DSC~ effect_weight + n_signal + pve, dscout.susie, length)
total$total_true = total$DSC * total$n_signal
power.susie = merge(valid, total)
power.susie$power.susie = round(power.susie$valid/(power.susie$total_true), 3)
colnames(power.susie)[colnames(power.susie) == 'valid'] <- 'valid.susie'
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.susie.init, sum)
total = aggregate(DSC~ effect_weight + n_signal + pve, dscout.susie.init, length)
total$total_true = total$DSC * total$n_signal
power.susie.init = merge(valid, total)
power.susie.init$power.susie.init = round(power.susie.init$valid/(power.susie.init$total_true), 3)
colnames(power.susie.init)[colnames(power.susie.init) == 'valid'] <- 'valid.susie.init'
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.finemap, sum)
total = aggregate(DSC ~ effect_weight + n_signal + pve, dscout.finemap, length)
total$total_true = total$DSC * total$n_signal
power.finemap = merge(valid, total)
power.finemap$power.finemap = round(power.finemap$valid/(power.finemap$total_true),3)
colnames(power.finemap)[colnames(power.finemap) == 'valid'] <- 'valid.finemap'
power = Reduce(function(...) merge(...),
list(power.susie, power.susie.init, power.finemap))
power = power[,-4]
power %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F) %>% add_header_above(c(" ", " ", " "," ", "SuSiE z" = 2, "SuSiE z init" = 2,"FINEMAP" = 2)) %>% column_spec(c(6, 8, 10), bold = T)| effect_weight | n_signal | pve | total_true | valid.susie | power.susie | valid.susie.init | power.susie.init | valid.finemap | power.finemap |
|---|---|---|---|---|---|---|---|---|---|
| equal | 1 | 0.05 | 150 | 110 | 0.733 | 110 | 0.733 | 150 | 1.000 |
| equal | 1 | 0.10 | 150 | 120 | 0.800 | 123 | 0.820 | 150 | 1.000 |
| equal | 1 | 0.20 | 150 | 141 | 0.940 | 141 | 0.940 | 150 | 1.000 |
| equal | 1 | 0.60 | 150 | 141 | 0.940 | 141 | 0.940 | 139 | 0.927 |
| equal | 1 | 0.80 | 150 | 101 | 0.673 | 104 | 0.693 | 140 | 0.933 |
| equal | 10 | 0.05 | 1500 | 100 | 0.067 | 105 | 0.070 | 1494 | 0.996 |
| equal | 10 | 0.10 | 1500 | 198 | 0.132 | 193 | 0.129 | 1455 | 0.970 |
| equal | 10 | 0.20 | 1500 | 327 | 0.218 | 396 | 0.264 | 1320 | 0.880 |
| equal | 10 | 0.60 | 1500 | 387 | 0.258 | 655 | 0.437 | 1104 | 0.736 |
| equal | 10 | 0.80 | 1500 | 350 | 0.233 | 573 | 0.382 | 1004 | 0.669 |
| equal | 3 | 0.05 | 450 | 125 | 0.278 | 111 | 0.247 | 449 | 0.998 |
| equal | 3 | 0.10 | 450 | 237 | 0.527 | 230 | 0.511 | 446 | 0.991 |
| equal | 3 | 0.20 | 450 | 307 | 0.682 | 311 | 0.691 | 446 | 0.991 |
| equal | 3 | 0.60 | 450 | 329 | 0.731 | 336 | 0.747 | 438 | 0.973 |
| equal | 3 | 0.80 | 450 | 304 | 0.676 | 311 | 0.691 | 412 | 0.916 |
| equal | 5 | 0.05 | 750 | 119 | 0.159 | 118 | 0.157 | 748 | 0.997 |
| equal | 5 | 0.10 | 750 | 277 | 0.369 | 275 | 0.367 | 743 | 0.991 |
| equal | 5 | 0.20 | 750 | 432 | 0.576 | 435 | 0.580 | 715 | 0.953 |
| equal | 5 | 0.60 | 750 | 512 | 0.683 | 513 | 0.684 | 686 | 0.915 |
| equal | 5 | 0.80 | 750 | 473 | 0.631 | 475 | 0.633 | 653 | 0.871 |
| notequal | 1 | 0.05 | 150 | 110 | 0.733 | 110 | 0.733 | 150 | 1.000 |
| notequal | 1 | 0.10 | 150 | 120 | 0.800 | 123 | 0.820 | 150 | 1.000 |
| notequal | 1 | 0.20 | 150 | 141 | 0.940 | 141 | 0.940 | 150 | 1.000 |
| notequal | 1 | 0.60 | 150 | 141 | 0.940 | 141 | 0.940 | 138 | 0.920 |
| notequal | 1 | 0.80 | 150 | 101 | 0.673 | 104 | 0.693 | 142 | 0.947 |
| notequal | 10 | 0.05 | 1500 | 137 | 0.091 | 139 | 0.093 | 1492 | 0.995 |
| notequal | 10 | 0.10 | 1500 | 163 | 0.109 | 159 | 0.106 | 1471 | 0.981 |
| notequal | 10 | 0.20 | 1500 | 203 | 0.135 | 211 | 0.141 | 1414 | 0.943 |
| notequal | 10 | 0.60 | 1500 | 194 | 0.129 | 199 | 0.133 | 1323 | 0.882 |
| notequal | 10 | 0.80 | 1500 | 156 | 0.104 | 154 | 0.103 | 1246 | 0.831 |
| notequal | 3 | 0.05 | 450 | 122 | 0.271 | 122 | 0.271 | 449 | 0.998 |
| notequal | 3 | 0.10 | 450 | 154 | 0.342 | 142 | 0.316 | 449 | 0.998 |
| notequal | 3 | 0.20 | 450 | 193 | 0.429 | 186 | 0.413 | 443 | 0.984 |
| notequal | 3 | 0.60 | 450 | 177 | 0.393 | 175 | 0.389 | 428 | 0.951 |
| notequal | 3 | 0.80 | 450 | 135 | 0.300 | 132 | 0.293 | 400 | 0.889 |
| notequal | 5 | 0.05 | 750 | 122 | 0.163 | 122 | 0.163 | 748 | 0.997 |
| notequal | 5 | 0.10 | 750 | 151 | 0.201 | 150 | 0.200 | 743 | 0.991 |
| notequal | 5 | 0.20 | 750 | 200 | 0.267 | 198 | 0.264 | 732 | 0.976 |
| notequal | 5 | 0.60 | 750 | 182 | 0.243 | 182 | 0.243 | 716 | 0.955 |
| notequal | 5 | 0.80 | 750 | 145 | 0.193 | 145 | 0.193 | 667 | 0.889 |
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.susie, sum)
total = aggregate(total~ effect_weight + n_signal + pve, dscout.susie, sum)
fdr.susie = merge(valid, total)
fdr.susie$fdr.susie = round((fdr.susie$total - fdr.susie$valid)/fdr.susie$total, 3)
colnames(fdr.susie)[colnames(fdr.susie) == 'valid'] <- 'valid.susie'
fdr.susie = fdr.susie[,-5]
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.susie.init, sum)
total = aggregate(total~ effect_weight + n_signal + pve, dscout.susie.init, sum)
fdr.susie.init = merge(valid, total)
fdr.susie.init$fdr.susie.init = round((fdr.susie.init$total - fdr.susie.init$valid)/fdr.susie.init$total, 3)
colnames(fdr.susie.init)[colnames(fdr.susie.init) == 'valid'] <- 'valid.susie.init'
fdr.susie.init = fdr.susie.init[,-5]
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.finemap, sum)
total = aggregate(size ~ effect_weight + n_signal + pve, dscout.finemap, sum)
fdr.finemap = merge(valid, total)
fdr.finemap$fdr.finemap = round((fdr.finemap$size - fdr.finemap$valid)/fdr.finemap$size, 3)
colnames(fdr.finemap)[colnames(fdr.finemap) == 'valid'] <- 'valid.finemap'
fdr.finemap = fdr.finemap[,-5]
fdr = Reduce(function(...) merge(...),
list(fdr.susie, fdr.susie.init, fdr.finemap))
fdr %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F) %>% add_header_above(c(" ", " ", " ", "SuSiE z" = 2, "SuSiE z init" = 2,"FINEMAP" = 2)) %>% column_spec(c(5, 7, 9), bold = T)| effect_weight | n_signal | pve | valid.susie | fdr.susie | valid.susie.init | fdr.susie.init | valid.finemap | fdr.finemap |
|---|---|---|---|---|---|---|---|---|
| equal | 1 | 0.05 | 110 | 0.534 | 110 | 0.231 | 150 | 0.999 |
| equal | 1 | 0.10 | 120 | 0.444 | 123 | 0.180 | 150 | 0.999 |
| equal | 1 | 0.20 | 141 | 0.217 | 141 | 0.060 | 150 | 0.999 |
| equal | 1 | 0.60 | 141 | 0.212 | 141 | 0.000 | 139 | 0.999 |
| equal | 1 | 0.80 | 101 | 0.409 | 104 | 0.000 | 140 | 0.998 |
| equal | 10 | 0.05 | 100 | 0.628 | 105 | 0.591 | 1494 | 0.990 |
| equal | 10 | 0.10 | 198 | 0.519 | 193 | 0.545 | 1455 | 0.990 |
| equal | 10 | 0.20 | 327 | 0.402 | 396 | 0.437 | 1320 | 0.987 |
| equal | 10 | 0.60 | 387 | 0.273 | 655 | 0.234 | 1104 | 0.968 |
| equal | 10 | 0.80 | 350 | 0.236 | 573 | 0.201 | 1004 | 0.955 |
| equal | 3 | 0.05 | 125 | 0.604 | 111 | 0.554 | 449 | 0.997 |
| equal | 3 | 0.10 | 237 | 0.441 | 230 | 0.361 | 446 | 0.997 |
| equal | 3 | 0.20 | 307 | 0.315 | 311 | 0.228 | 446 | 0.996 |
| equal | 3 | 0.60 | 329 | 0.167 | 336 | 0.123 | 438 | 0.993 |
| equal | 3 | 0.80 | 304 | 0.172 | 311 | 0.114 | 412 | 0.984 |
| equal | 5 | 0.05 | 119 | 0.584 | 118 | 0.586 | 748 | 0.995 |
| equal | 5 | 0.10 | 277 | 0.409 | 275 | 0.415 | 743 | 0.995 |
| equal | 5 | 0.20 | 432 | 0.281 | 435 | 0.280 | 715 | 0.992 |
| equal | 5 | 0.60 | 512 | 0.142 | 513 | 0.142 | 686 | 0.972 |
| equal | 5 | 0.80 | 473 | 0.131 | 475 | 0.128 | 653 | 0.956 |
| notequal | 1 | 0.05 | 110 | 0.534 | 110 | 0.231 | 150 | 0.999 |
| notequal | 1 | 0.10 | 120 | 0.444 | 123 | 0.180 | 150 | 0.999 |
| notequal | 1 | 0.20 | 141 | 0.217 | 141 | 0.060 | 150 | 0.999 |
| notequal | 1 | 0.60 | 141 | 0.212 | 141 | 0.000 | 138 | 0.999 |
| notequal | 1 | 0.80 | 101 | 0.409 | 104 | 0.000 | 142 | 0.998 |
| notequal | 10 | 0.05 | 137 | 0.477 | 139 | 0.469 | 1492 | 0.990 |
| notequal | 10 | 0.10 | 163 | 0.455 | 159 | 0.463 | 1471 | 0.990 |
| notequal | 10 | 0.20 | 203 | 0.316 | 211 | 0.310 | 1414 | 0.990 |
| notequal | 10 | 0.60 | 194 | 0.174 | 199 | 0.191 | 1323 | 0.988 |
| notequal | 10 | 0.80 | 156 | 0.235 | 154 | 0.312 | 1246 | 0.988 |
| notequal | 3 | 0.05 | 122 | 0.540 | 122 | 0.463 | 449 | 0.997 |
| notequal | 3 | 0.10 | 154 | 0.462 | 142 | 0.396 | 449 | 0.997 |
| notequal | 3 | 0.20 | 193 | 0.306 | 186 | 0.271 | 443 | 0.997 |
| notequal | 3 | 0.60 | 177 | 0.088 | 175 | 0.054 | 428 | 0.996 |
| notequal | 3 | 0.80 | 135 | 0.220 | 132 | 0.143 | 400 | 0.995 |
| notequal | 5 | 0.05 | 122 | 0.472 | 122 | 0.472 | 748 | 0.995 |
| notequal | 5 | 0.10 | 151 | 0.415 | 150 | 0.414 | 743 | 0.995 |
| notequal | 5 | 0.20 | 200 | 0.270 | 198 | 0.272 | 732 | 0.995 |
| notequal | 5 | 0.60 | 182 | 0.180 | 182 | 0.180 | 716 | 0.994 |
| notequal | 5 | 0.80 | 145 | 0.341 | 145 | 0.344 | 667 | 0.993 |
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14.3
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] susieR_0.6.4.0438 kableExtra_1.0.1 knitr_1.20 dplyr_0.7.8
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 highr_0.7 compiler_3.5.1
[4] pillar_1.3.1 git2r_0.24.0 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 viridisLite_0.3.0
[16] pkgconfig_2.0.2 rlang_0.3.1 Matrix_1.2-15
[19] rstudioapi_0.9.0 yaml_2.2.0 bindrcpp_0.2.2
[22] stringr_1.3.1 httr_1.4.0 xml2_1.2.0
[25] hms_0.4.2 grid_3.5.1 webshot_0.5.1
[28] rprojroot_1.3-2 tidyselect_0.2.5 glue_1.3.0
[31] R6_2.3.0 rmarkdown_1.11 purrr_0.2.5
[34] readr_1.3.1 magrittr_1.5 whisker_0.3-2
[37] backports_1.1.3 scales_1.0.0 htmltools_0.3.6
[40] assertthat_0.2.0 rvest_0.3.2 colorspace_1.4-0
[43] stringi_1.2.4 munsell_0.5.0 crayon_1.3.4
[46] R.oo_1.22.0 This reproducible R Markdown analysis was created with workflowr 1.1.1