Last updated: 2019-04-15

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The design matrix X are real human genotype data from GTEx project, the 150 data in dsc-finemap repo. We simulate under various number of causal variables (1,3,5) and total percentage of variance explained (0.05, 0.2, 0.6, 0.8). We set effect size of each causal variable to be equal. Using the summary statistics from univariate regression, we fit SuSiE model using in-sample/out-sample correlation matrix, and compare their results.

library(dscrutils)
library(tibble)
Warning: package 'tibble' was built under R version 3.5.2
library(kableExtra)

Import DSC results

dscout = dscquery('r_compare_data_signal', targets = 'get_sumstats sim_gaussian.pve sim_gaussian.n_signal sim_gaussian.effect_weight data.N_in susie_bhat.ld_method susie_z.ld_method finemap.ld_method score_susie.total score_susie.valid score_susie.size score_susie.purity score_susie.top score_susie.converged score_finemap.pip', omit.filenames = FALSE)
dscout.tibble = as_tibble(dscout)
dscout = readRDS('output/r_compare_dscout_susie_finemappip_tibble.rds')
dscout$method = rep('susie_b', nrow(dscout))
dscout$method[!is.na(dscout$susie_z.ld_method)] = 'susie_rss'
dscout$method[!is.na(dscout$finemap.ld_method)] = 'finemap'

dscout$ld_method = dscout$susie_bhat.ld_method
dscout$ld_method[!is.na(dscout$susie_z.ld_method)] = dscout$susie_z.ld_method[!is.na(dscout$susie_z.ld_method)]
dscout$ld_method[!is.na(dscout$finemap.ld_method)] = dscout$finemap.ld_method[!is.na(dscout$finemap.ld_method)]
dscout$sim_gaussian.effect_weight[which(dscout$sim_gaussian.effect_weight == 'rep(1/n_signal, n_signal)')] = 'equal'
dscout$sim_gaussian.effect_weight[which(dscout$sim_gaussian.effect_weight != 'equal')] = 'notequal'
dscout = dscout[,-c(6,8,9,10)]
colnames(dscout) = c('DSC', 'filename','pve', 'n_signal', 'effect_weight', 'N_in', 'total', 'valid', 'size', 'purity', 'top', 'converged', 'pip', 'method', 'ld_method')
dscout.equal = dscout[dscout$effect_weight == 'equal',]
dscout.equal.susierss = dscout.equal[dscout.equal$method == 'susie_rss',]
dscout.equal.susieb = dscout.equal[dscout.equal$method == 'susie_b',]
dscout.equal.finemap = dscout.equal[dscout.equal$method == 'finemap',]

susie_bhat

dscout.equal.susieb.in_sample = dscout.equal.susieb[dscout.equal.susieb$ld_method == 'in_sample',]
dscout.equal.susieb.out_sample = dscout.equal.susieb[dscout.equal.susieb$ld_method == 'out_sample',]
  • Converge

The model from susie_bhat all converge. But lots of cases with out-sample R failed (274 out of 1800). The estimated residual variance becomes negative.

converge.summary = aggregate(converged ~ ld_method, dscout.equal.susieb, sum)
converge.summary$Fail = 1800 - converge.summary$converged
Fail = converge.summary[converge.summary$Fail!=0,]
Fail[,-2] %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F)
ld_method Fail
2 out_sample 274
  • Purity of CS:
purity.susieb.in_sample = round(aggregate(purity~n_signal+pve, dscout.equal.susieb.in_sample, mean), 3)
colnames(purity.susieb.in_sample)[colnames(purity.susieb.in_sample) == 'purity'] <- 'purity.in_sample'
purity.susieb.out_sample = round(aggregate(purity~n_signal+pve, dscout.equal.susieb.out_sample[!is.na(dscout.equal.susieb.out_sample$converged),], mean), 3)
colnames(purity.susieb.out_sample)[colnames(purity.susieb.out_sample) == 'purity'] <- 'purity.out_sample'
purity.susieb = merge(purity.susieb.in_sample, purity.susieb.out_sample)

purity.susieb %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F)
n_signal pve purity.in_sample purity.out_sample
1 0.05 0.239 0.245
1 0.20 0.952 0.945
1 0.60 0.997 0.996
1 0.80 0.999 1.000
3 0.05 0.116 0.119
3 0.20 0.814 0.840
3 0.60 0.987 0.997
3 0.80 0.996 0.999
5 0.05 0.056 0.060
5 0.20 0.683 0.732
5 0.60 0.974 0.992
5 0.80 0.994 0.998
  • Power:
valid.in = aggregate(valid ~ n_signal + pve, dscout.equal.susieb.in_sample, sum)
total.in = aggregate(DSC~ n_signal + pve, dscout.equal.susieb.in_sample, length)
total.in$total_true = total.in$DSC * total.in$n_signal
power.susie.in = merge(valid.in, total.in)
power.susie.in$power.susie.in_sample = round(power.susie.in$valid/(power.susie.in$total_true), 3)
colnames(power.susie.in)[colnames(power.susie.in) == 'valid'] <- 'valid.in_sample'
power.susie.in = power.susie.in[,-c(4,5)]

valid.out = aggregate(valid ~ n_signal + pve, dscout.equal.susieb.out_sample[!is.na(dscout.equal.susieb.out_sample$converged),], sum)
total.out = aggregate(DSC~ n_signal + pve, dscout.equal.susieb.out_sample[!is.na(dscout.equal.susieb.out_sample$converged),], length)
total.out$total_true = total.out$DSC * total.out$n_signal
power.susie.out = merge(valid.out, total.out)
power.susie.out$power.susie.out_sample = round(power.susie.out$valid/(power.susie.out$total_true), 3)
colnames(power.susie.out)[colnames(power.susie.out) == 'valid'] <- 'valid.out_sample'
power.susie.out = power.susie.out[,-c(4,5)]

power.susie = Reduce(function(...) merge(...),
       list(power.susie.in, power.susie.out))
power.susie %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F) %>% add_header_above(c(" ", " ","IN sample" = 2, "OUT sample" = 2)) %>% column_spec(c(4, 6), bold = T)
IN sample
OUT sample
n_signal pve valid.in_sample power.susie.in_sample valid.out_sample power.susie.out_sample
1 0.05 45 0.300 45 0.300
1 0.20 149 0.993 140 0.952
1 0.60 149 0.993 94 0.797
1 0.80 149 0.993 42 0.778
3 0.05 17 0.038 18 0.040
3 0.20 215 0.478 201 0.453
3 0.60 375 0.833 178 0.467
3 0.80 385 0.856 107 0.457
5 0.05 10 0.013 9 0.012
5 0.20 137 0.183 150 0.203
5 0.60 579 0.772 286 0.400
5 0.80 612 0.816 229 0.405
  • FDR
valid.in = aggregate(valid ~ n_signal + pve, dscout.equal.susieb.in_sample, sum)
total.in = aggregate(total~ n_signal + pve, dscout.equal.susieb.in_sample, sum)
fdr.in = merge(valid.in, total.in)
fdr.in$fdr.in = round((fdr.in$total - fdr.in$valid)/fdr.in$total, 4)
colnames(fdr.in)[colnames(fdr.in) == 'valid'] <- 'valid.in_sample'
fdr.in = fdr.in[,-4]

valid.out = aggregate(valid ~ n_signal + pve, dscout.equal.susieb.out_sample[!is.na(dscout.equal.susieb.out_sample$converged),], sum)
total.out = aggregate(total~ n_signal + pve, dscout.equal.susieb.out_sample[!is.na(dscout.equal.susieb.out_sample$converged),], sum)
fdr.out = merge(valid.out, total.out)
fdr.out$fdr.out = round((fdr.out$total - fdr.out$valid)/fdr.out$total, 4)
colnames(fdr.out)[colnames(fdr.out) == 'valid'] <- 'valid.out_sample'
fdr.out = fdr.out[,-4]

fdr = Reduce(function(...) merge(...),
       list(fdr.in, fdr.out))

fdr %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F) %>% add_header_above(c(" ", " ", "IN sample" = 2, "OUT sample" = 2)) %>% column_spec(c(4,6), bold = T)
IN sample
OUT sample
n_signal pve valid.in_sample fdr.in valid.out_sample fdr.out
1 0.05 45 0.0217 45 0.0625
1 0.20 149 0.0067 140 0.3694
1 0.60 149 0.0067 94 0.8182
1 0.80 149 0.0067 42 0.8433
3 0.05 17 0.2273 18 0.2500
3 0.20 215 0.1502 201 0.3619
3 0.60 375 0.1176 178 0.6973
3 0.80 385 0.1387 107 0.7206
5 0.05 10 0.0909 9 0.1818
5 0.20 137 0.1988 150 0.4094
5 0.60 579 0.1267 286 0.5855
5 0.80 612 0.1319 229 0.5918

susie_rss

dscout.equal.susierss.in_sample = dscout.equal.susierss[dscout.equal.susierss$ld_method == 'in_sample',]
dscout.equal.susierss.out_sample = dscout.equal.susierss[dscout.equal.susierss$ld_method == 'out_sample',]
  • Converge

There are cases fail to converge in susie_rss.

converge.summary = aggregate(converged ~ pve + n_signal+ld_method, dscout.equal.susierss, sum)
converge.summary$NotConverge = 150 - converge.summary$converged
NotConverge = converge.summary[converge.summary$NotConverge!=0,]
colnames(NotConverge) = c('pve', 'n_signal', 'ld', 'converged', 'NotConverge(out of 150)')
NotConverge[,-4] %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F)
pve n_signal ld NotConverge(out of 150)
3 0.6 1 in_sample 3
4 0.8 1 in_sample 8
8 0.8 3 in_sample 1
12 0.8 5 in_sample 1
15 0.6 1 out_sample 2
16 0.8 1 out_sample 6
20 0.8 3 out_sample 1
24 0.8 5 out_sample 1
  • Purity of CS:
purity.susierss.in_sample = round(aggregate(purity~n_signal+pve, dscout.equal.susierss.in_sample[dscout.equal.susierss.in_sample$converged==1,], mean), 3)
colnames(purity.susierss.in_sample)[colnames(purity.susierss.in_sample) == 'purity'] <- 'purity.in_sample'
purity.susierss.out_sample = round(aggregate(purity~n_signal+pve, dscout.equal.susierss.out_sample[dscout.equal.susierss.out_sample$converged==1,], mean), 3)
colnames(purity.susierss.out_sample)[colnames(purity.susierss.out_sample) == 'purity'] <- 'purity.out_sample'
purity.susierss = merge(purity.susierss.in_sample, purity.susierss.out_sample)

purity.susierss %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F)
n_signal pve purity.in_sample purity.out_sample
1 0.05 0.255 0.214
1 0.20 0.953 0.936
1 0.60 0.989 0.988
1 0.80 0.999 0.999
3 0.05 0.123 0.108
3 0.20 0.832 0.734
3 0.60 0.980 0.962
3 0.80 0.989 0.985
5 0.05 0.066 0.050
5 0.20 0.679 0.647
5 0.60 0.955 0.938
5 0.80 0.974 0.970
  • Power:
valid.in = aggregate(valid ~ n_signal + pve, dscout.equal.susierss.in_sample[dscout.equal.susierss.in_sample$converged==1,], sum)
total.in = aggregate(DSC~ n_signal + pve, dscout.equal.susierss.in_sample[dscout.equal.susierss.in_sample$converged==1,], length)
total.in$total_true = total.in$DSC * total.in$n_signal
power.susie.in = merge(valid.in, total.in)
power.susie.in$power.susie.in_sample = round(power.susie.in$valid/(power.susie.in$total_true), 3)
colnames(power.susie.in)[colnames(power.susie.in) == 'valid'] <- 'valid.in_sample'
power.susie.in = power.susie.in[,-c(4,5)]

valid.out = aggregate(valid ~ n_signal + pve, dscout.equal.susierss.out_sample[dscout.equal.susierss.out_sample$converged ==1,], sum)
total.out = aggregate(DSC~ n_signal + pve, dscout.equal.susierss.out_sample[dscout.equal.susierss.out_sample$converged ==1,], length)
total.out$total_true = total.out$DSC * total.out$n_signal
power.susie.out = merge(valid.out, total.out)
power.susie.out$power.susie.out_sample = round(power.susie.out$valid/(power.susie.out$total_true), 3)
colnames(power.susie.out)[colnames(power.susie.out) == 'valid'] <- 'valid.out_sample'
power.susie.out = power.susie.out[,-c(4,5)]

power.susie = Reduce(function(...) merge(...),
       list(power.susie.in, power.susie.out))
power.susie %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F) %>% add_header_above(c(" ", " ","IN sample" = 2, "OUT sample" = 2)) %>% column_spec(c(4, 6), bold = T)
IN sample
OUT sample
n_signal pve valid.in_sample power.susie.in_sample valid.out_sample power.susie.out_sample
1 0.05 47 0.313 36 0.240
1 0.20 145 0.967 122 0.813
1 0.60 102 0.694 73 0.493
1 0.80 95 0.669 83 0.576
3 0.05 18 0.040 17 0.038
3 0.20 195 0.433 150 0.333
3 0.60 350 0.778 224 0.498
3 0.80 333 0.745 202 0.452
5 0.05 11 0.015 8 0.011
5 0.20 132 0.176 104 0.139
5 0.60 497 0.663 307 0.409
5 0.80 527 0.707 309 0.415
  • FDR:
valid.in = aggregate(valid ~ n_signal + pve, dscout.equal.susierss.in_sample[dscout.equal.susierss.in_sample$converged==1,], sum)
total.in = aggregate(total~ n_signal + pve, dscout.equal.susierss.in_sample[dscout.equal.susierss.in_sample$converged==1,], sum)
fdr.in = merge(valid.in, total.in)
fdr.in$fdr.in = round((fdr.in$total - fdr.in$valid)/fdr.in$total, 4)
colnames(fdr.in)[colnames(fdr.in) == 'valid'] <- 'valid.in_sample'
fdr.in = fdr.in[,-4]

valid.out = aggregate(valid ~ n_signal + pve, dscout.equal.susierss.out_sample[dscout.equal.susierss.out_sample$converged==1,], sum)
total.out = aggregate(total~ n_signal + pve, dscout.equal.susierss.out_sample[dscout.equal.susierss.out_sample$converged==1,], sum)
fdr.out = merge(valid.out, total.out)
fdr.out$fdr.out = round((fdr.out$total - fdr.out$valid)/fdr.out$total, 4)
colnames(fdr.out)[colnames(fdr.out) == 'valid'] <- 'valid.out_sample'
fdr.out = fdr.out[,-4]

fdr = Reduce(function(...) merge(...),
       list(fdr.in, fdr.out))

fdr %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F) %>% add_header_above(c(" ", " ", "IN sample" = 2, "OUT sample" = 2)) %>% column_spec(c(4,6), bold = T)
IN sample
OUT sample
n_signal pve valid.in_sample fdr.in valid.out_sample fdr.out
1 0.05 47 0.0208 36 0.1220
1 0.20 145 0.0333 122 0.2229
1 0.60 102 0.6832 73 0.8617
1 0.80 95 0.8370 83 0.8754
3 0.05 18 0.2174 17 0.1905
3 0.20 195 0.1772 150 0.3056
3 0.60 350 0.1898 224 0.5650
3 0.80 333 0.2854 202 0.6599
5 0.05 11 0.1538 8 0.2000
5 0.20 132 0.2048 104 0.3418
5 0.60 497 0.1605 307 0.4661
5 0.80 527 0.1930 309 0.5261

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] kableExtra_1.0.1 tibble_2.0.1     dscrutils_0.3.3 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0        rstudioapi_0.9.0  xml2_1.2.0       
 [4] knitr_1.20        whisker_0.3-2     magrittr_1.5     
 [7] workflowr_1.1.1   hms_0.4.2         munsell_0.5.0    
[10] rvest_0.3.2       viridisLite_0.3.0 colorspace_1.4-0 
[13] R6_2.3.0          rlang_0.3.1       highr_0.7        
[16] httr_1.4.0        stringr_1.3.1     tools_3.5.1      
[19] webshot_0.5.1     R.oo_1.22.0       git2r_0.24.0     
[22] htmltools_0.3.6   yaml_2.2.0        rprojroot_1.3-2  
[25] digest_0.6.18     crayon_1.3.4      readr_1.3.1      
[28] R.utils_2.7.0     glue_1.3.0        evaluate_0.12    
[31] rmarkdown_1.11    stringi_1.2.4     compiler_3.5.1   
[34] pillar_1.3.1      scales_1.0.0      backports_1.1.3  
[37] R.methodsS3_1.7.1 pkgconfig_2.0.2

This reproducible R Markdown analysis was created with workflowr 1.1.1