Last updated: 2019-04-16

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Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    .sos/
    Ignored:    analysis/.DS_Store
    Ignored:    data/.DS_Store
    Ignored:    output/.DS_Store

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_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
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    Untracked:  figure/
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    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
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Unstaged changes:
    Modified:   analysis/SuSiEDAP_Power_data31_35.Rmd
    Modified:   analysis/SusieZPerformance.Rmd
    Modified:   analysis/SusieZPerformanceRE3.Rmd
    Modified:   output/dsc_susie_z_v_output.rds
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This data is simulated with PVE 0.6. There is only one signal.

library(susieR)
library(data.table)
Warning: package 'data.table' was built under R version 3.5.2
r.out = as.matrix(fread('data/small_data_69.ld_out_file.out.ld'))
r.in = as.matrix(fread('data/small_data_69.ld_in_file.in.ld'))
data = readRDS('data/small_data_69_sim_gaussian_3.rds')
ss = readRDS('data/small_data_69_sim_gaussian_3_get_sumstats_1.rds')
z = ss$sumstats$bhat/ss$sumstats$shat
susie_plot(z, y = "z", b=data$meta$true_coef)

Expand here to see past versions of unnamed-chunk-2-1.png:
Version Author Date
9267aac zouyuxin 2019-04-15

This is the reuslt from susie_bhat:

fit_b = susie_bhat(ss$sumstats$bhat, ss$sumstats$shat, R = r.in, n=287, L=5, estimate_residual_variance=TRUE)
susie_plot(fit_b, y='PIP', b = data$meta$true_coef)

Expand here to see past versions of unnamed-chunk-3-1.png:
Version Author Date
9267aac zouyuxin 2019-04-15

susie_rss fails to converge with L = 5:

fit_rss = susie_rss(z, R = r.in, L=5, max_iter = 20)
Warning in susie_rss(z, R = r.in, L = 5, max_iter = 20): The maximum number
of non-zero effects is greater than 1, this feature is experimental.
Warning in susie_rss(z, R = r.in, L = 5, max_iter = 20): IBSS algorithm did
not converge in 20 iterations!
plot(fit_rss$elbo)

Expand here to see past versions of unnamed-chunk-4-1.png:
Version Author Date
9267aac zouyuxin 2019-04-15

Using L=1, it converges.

fit_rss_init = susie_rss(z, R=r.in, L=1)
susie_plot(fit_rss_init, y='PIP', b=data$meta$true_coef)

Expand here to see past versions of unnamed-chunk-5-1.png:
Version Author Date
9267aac zouyuxin 2019-04-15

If we relax the constraint on residual variance, it converges. There is no significant credible set, and the estimated residual variance is 740.

fit_rss_relax = susie_rss(z, R = r.in, L=5, max_iter = 20, restrict = FALSE)
Warning in susie_rss(z, R = r.in, L = 5, max_iter = 20, restrict = FALSE):
The maximum number of non-zero effects is greater than 1, this feature is
experimental.

This data is simulated with PVE 0.8. There are five signals.

r.out = as.matrix(fread('data/small_data_132.ld_out_file.out.ld'))
r.in = as.matrix(fread('data/small_data_132.ld_in_file.in.ld'))
data = readRDS('data/small_data_132_sim_gaussian_12.rds')
ss = readRDS('data/small_data_132_sim_gaussian_12_get_sumstats_1.rds')
z = ss$sumstats$bhat/ss$sumstats$shat
susie_plot(z, y = "z", b=data$meta$true_coef)

Expand here to see past versions of unnamed-chunk-8-1.png:
Version Author Date
9267aac zouyuxin 2019-04-15

This is the reuslt from susie_bhat:

fit_b = susie_bhat(ss$sumstats$bhat, ss$sumstats$shat, R = r.in, n=287, L=5, estimate_residual_variance=TRUE)
susie_plot(fit_b, y='PIP', b = data$meta$true_coef)

Expand here to see past versions of unnamed-chunk-9-1.png:
Version Author Date
9267aac zouyuxin 2019-04-15

susie_rss fails to converge with L = 5. Using L=1, it converges, but it picks up the wrong one.

fit_rss = susie_rss(z, R = r.in, L=1)
susie_plot(fit_rss, y='PIP', b = data$meta$true_coef)

If we relax the constraint on residual variance, it converges. There is no significant credible set, and the estimated residual variance is 9560.

fit_rss_relax = susie_rss(z, R = r.in, L=5, max_iter = 20, restrict = FALSE)
Warning in susie_rss(z, R = r.in, L = 5, max_iter = 20, restrict = FALSE):
The maximum number of non-zero effects is greater than 1, this feature is
experimental.

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] data.table_1.12.0 susieR_0.7.1.500 

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_1.0.0        lattice_0.20-38  
 [4] digest_0.6.18     rprojroot_1.3-2   R.methodsS3_1.7.1
 [7] grid_3.5.1        backports_1.1.3   git2r_0.24.0     
[10] magrittr_1.5      evaluate_0.12     stringi_1.2.4    
[13] whisker_0.3-2     R.oo_1.22.0       R.utils_2.7.0    
[16] Matrix_1.2-15     rmarkdown_1.11    tools_3.5.1      
[19] stringr_1.3.1     yaml_2.2.0        compiler_3.5.1   
[22] htmltools_0.3.6   knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.1.1