Simulation Results using simulated (correlated)
shengqian
2023-12-3
Last updated: 2023-12-03
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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source("/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/analysis/simulation_help_functions.R")Simulation 1: Liver and three 95% correlated tissues
Shared effect size parameters
30% PVE and 2.5e-4 prior inclusion for SNPs, 5% PVE and 0.015 prior inclusion for Liver, 1% PVE and 0.003 prior inclusion for other three mixed groups.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_four_tissues_95/"
runtag = "ukb-s80.45-3_corr"
configtag <- 1
simutags <- paste(1, 1:3, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8#results using PIP threshold (gene+tissue)
results_df[,c("simutag", "n_causal", "n_detected_pip", "n_detected_pip_in_causal")] simutag n_causal n_detected_pip n_detected_pip_in_causal
1 1-1 176 1 1
2 1-2 178 3 3
3 1-3 200 5 3#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.7777778#results using combined PIP threshold
results_df[,c("simutag", "n_causal_combined", "n_detected_comb_pip", "n_detected_comb_pip_in_causal")] simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 1-1 175 51 47
2 1-2 175 60 54
3 1-3 197 62 56#mean percent causal using combined PIP > 0.8
sum(results_df$n_detected_comb_pip_in_causal)/sum(results_df$n_detected_comb_pip)[1] 0.9075145#prior inclusion and mean prior inclusion
results_df[,c(which(colnames(results_df)=="simutag"), setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df))))] simutag prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
1 1-1 0.0002182741 0.008049709 0.002235799 0.008521669 0.002138643
2 1-2 0.0002292157 0.020976160 0.003852905 0.003329239 0.004967307
3 1-3 0.0002578078 0.024117884 0.004999158 0.001374073 0.003628265colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
0.0002350992 0.0177145843 0.0036959539 0.0044083270 0.0035780717 #prior variance and mean prior variance
results_df[,c(which(colnames(results_df)=="simutag"), grep("prior_var", names(results_df)))] simutag prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
1 1-1 9.932523 29.57061 29.57061 29.57061
2 1-2 8.836244 16.44995 16.44995 16.44995
3 1-3 8.211823 17.05528 17.05528 17.05528
prior_var_weight4
1 29.57061
2 16.44995
3 17.05528colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
8.99353 21.02528 21.02528 21.02528
prior_var_weight4
21.02528 #PVE and mean PVE
results_df[,c(which(colnames(results_df)=="simutag"), grep("pve", names(results_df)))] simutag pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
1 1-1 0.2552079 0.03911705 0.009540975 0.036320293 0.009126375
2 1-2 0.2384209 0.05670435 0.009146453 0.007893582 0.011791945
3 1-3 0.2492114 0.06759641 0.012304258 0.003377792 0.008930126colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
0.247613377 0.054472603 0.010330562 0.015863889 0.009949482 #TWAS results
results_df[,c(which(colnames(results_df)=="simutag"), grep("twas", names(results_df)))] simutag n_detected_twas n_detected_twas_in_causal n_detected_comb_twas
1 1-1 352 53 98
2 1-2 274 50 79
3 1-3 307 54 86
n_detected_comb_twas_in_causal
1 53
2 50
3 53sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.5931559
Separate effect size parameters
For the cTWAS analysis, each tissue had its own prior inclusion parameter end effect size parameter.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_four_tissues_95/"
runtag = "ukb-s80.45-3_corr"
configtag <- 2
simutags <- paste(1, 1:3, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8#results using PIP threshold (gene+tissue)
results_df[,c("simutag", "n_causal", "n_detected_pip", "n_detected_pip_in_causal")] simutag n_causal n_detected_pip n_detected_pip_in_causal
1 1-1 176 1 1
2 1-2 178 3 3
3 1-3 200 21 14#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.72#results using combined PIP threshold
results_df[,c("simutag", "n_causal_combined", "n_detected_comb_pip", "n_detected_comb_pip_in_causal")] simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 1-1 175 53 48
2 1-2 175 60 54
3 1-3 197 63 57#mean percent causal using combined PIP > 0.8
sum(results_df$n_detected_comb_pip_in_causal)/sum(results_df$n_detected_comb_pip)[1] 0.9034091#prior inclusion and mean prior inclusion
results_df[,c(which(colnames(results_df)=="simutag"), setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df))))] simutag prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
1 1-1 0.0002156299 0.009754366 0.003853150 0.009011783 0.004044699
2 1-2 0.0002286522 0.021176055 0.004074381 0.003607471 0.005337731
3 1-3 0.0002548608 0.022754393 0.006421529 0.002955947 0.004590780colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
0.0002330476 0.0178949378 0.0047830197 0.0051917339 0.0046577367 #prior variance and mean prior variance
results_df[,c(which(colnames(results_df)=="simutag"), grep("prior_var", names(results_df)))] simutag prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
1 1-1 10.016861 10.00122 56.660406 11.854264
2 1-2 8.851659 14.60905 24.140806 24.871112
3 1-3 8.286763 20.17711 9.179338 3.275441
prior_var_weight4
1 56.92859
2 10.20275
3 10.66796colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
9.051761 14.929124 29.993517 13.333606
prior_var_weight4
25.933100 #PVE and mean PVE
results_df[,c(which(colnames(results_df)=="simutag"), grep("pve", names(results_df)))] simutag pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
1 1-1 0.2542569 0.01603163 0.031506148 0.015397484 0.033228934
2 1-2 0.2382497 0.05083848 0.014194273 0.012931906 0.007859119
3 1-3 0.2486109 0.07544836 0.008506473 0.001395503 0.007067527colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
0.247039159 0.047439489 0.018068965 0.009908298 0.016051860 #TWAS results
results_df[,c(which(colnames(results_df)=="simutag"), grep("twas", names(results_df)))] simutag n_detected_twas n_detected_twas_in_causal n_detected_comb_twas
1 1-1 352 53 98
2 1-2 274 50 79
3 1-3 307 54 86
n_detected_comb_twas_in_causal
1 53
2 50
3 53sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.5931559
## Simulation 2: Liver and 50% correlated tissues
Shared effect size parameters
30% PVE and 2.5e-4 prior inclusion for SNPs, 5% PVE and 0.015 prior inclusion for Liver, 1% PVE and 0.003 prior inclusion for other three mixed groups.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_four_tissues_50/"
runtag = "ukb-s80.45-3_corr"
configtag <- 1
simutags <- paste(1, 1:3, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8#results using PIP threshold (gene+tissue)
results_df[,c("simutag", "n_causal", "n_detected_pip", "n_detected_pip_in_causal")] simutag n_causal n_detected_pip n_detected_pip_in_causal
1 1-1 181 10 9
2 1-2 189 11 7
3 1-3 178 20 16#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.7804878#results using combined PIP threshold
results_df[,c("simutag", "n_causal_combined", "n_detected_comb_pip", "n_detected_comb_pip_in_causal")] simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 1-1 180 50 45
2 1-2 188 45 40
3 1-3 178 70 63#mean percent causal using combined PIP > 0.8
sum(results_df$n_detected_comb_pip_in_causal)/sum(results_df$n_detected_comb_pip)[1] 0.8969697#prior inclusion and mean prior inclusion
results_df[,c(which(colnames(results_df)=="simutag"), setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df))))] simutag prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
1 1-1 0.0002538055 0.013269735 0.001099716 0.0039010391 7.857330e-03
2 1-2 0.0002275846 0.006612305 0.008718409 0.0003923411 2.812843e-05
3 1-3 0.0002575079 0.017556519 0.009965381 0.0048511858 1.812605e-03colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
0.0002462993 0.0124795200 0.0065945021 0.0030481887 0.0032326877 #prior variance and mean prior variance
results_df[,c(which(colnames(results_df)=="simutag"), grep("prior_var", names(results_df)))] simutag prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
1 1-1 8.189835 20.30518 20.30518 20.30518
2 1-2 9.413612 32.04652 32.04652 32.04652
3 1-3 8.839990 17.65084 17.65084 17.65084
prior_var_weight4
1 20.30518
2 32.04652
3 17.65084colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
8.814479 23.334184 23.334184 23.334184
prior_var_weight4
23.334184 #PVE and mean PVE
results_df[,c(which(colnames(results_df)=="simutag"), grep("pve", names(results_df)))] simutag pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
1 1-1 0.2446856 0.04427870 0.003398121 0.012210865 0.0245946769
2 1-2 0.2521920 0.03482246 0.042517660 0.001938224 0.0001389586
3 1-3 0.2679628 0.05092483 0.026767675 0.013199959 0.0049320531colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
0.254946829 0.043341997 0.024227819 0.009116349 0.009888563 #TWAS results
results_df[,c(which(colnames(results_df)=="simutag"), grep("twas", names(results_df)))] simutag n_detected_twas n_detected_twas_in_causal n_detected_comb_twas
1 1-1 283 53 106
2 1-2 342 53 119
3 1-3 315 62 117
n_detected_comb_twas_in_causal
1 54
2 54
3 62sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.497076
Separate effect size parameters
For the cTWAS analysis, each tissue had its own prior inclusion parameter end effect size parameter.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_four_tissues_50/"
runtag = "ukb-s80.45-3_corr"
configtag <- 2
simutags <- paste(1, 1:3, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8#results using PIP threshold (gene+tissue)
results_df[,c("simutag", "n_causal", "n_detected_pip", "n_detected_pip_in_causal")] simutag n_causal n_detected_pip n_detected_pip_in_causal
1 1-1 181 20 17
2 1-2 189 9 7
3 1-3 178 22 16#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.7843137#results using combined PIP threshold
results_df[,c("simutag", "n_causal_combined", "n_detected_comb_pip", "n_detected_comb_pip_in_causal")] simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 1-1 180 55 50
2 1-2 188 45 40
3 1-3 178 71 63#mean percent causal using combined PIP > 0.8
sum(results_df$n_detected_comb_pip_in_causal)/sum(results_df$n_detected_comb_pip)[1] 0.8947368#prior inclusion and mean prior inclusion
results_df[,c(which(colnames(results_df)=="simutag"), setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df))))] simutag prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
1 1-1 0.0002350076 0.021212973 0.008084894 0.008906379 0.003546911
2 1-2 0.0002244601 0.006141248 0.010388003 0.002017603 0.002129400
3 1-3 0.0002545989 0.014210689 0.011878563 0.006344062 0.003998068colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
0.0002380222 0.0138549704 0.0101171534 0.0057560148 0.0032247932 #prior variance and mean prior variance
results_df[,c(which(colnames(results_df)=="simutag"), grep("prior_var", names(results_df)))] simutag prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
1 1-1 8.681052 10.49673 3.341442 5.257904
2 1-2 9.506664 51.85556 16.651411 5.853073
3 1-3 8.914319 23.52398 13.154874 13.600157
prior_var_weight4
1 73.938139
2 2.786334
3 7.012010colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
9.034012 28.625427 11.049242 8.237044
prior_var_weight4
27.912161 #PVE and mean PVE
results_df[,c(which(colnames(results_df)=="simutag"), grep("pve", names(results_df)))] simutag pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
1 1-1 0.2401522 0.03659160 0.004111114 0.007218933 0.0404276064
2 1-2 0.2511884 0.05233324 0.026322937 0.001820451 0.0009146388
3 1-3 0.2671634 0.05493532 0.023779452 0.013300577 0.0043216730colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
0.252834660 0.047953390 0.018071168 0.007446653 0.015221306 #TWAS results
results_df[,c(which(colnames(results_df)=="simutag"), grep("twas", names(results_df)))] simutag n_detected_twas n_detected_twas_in_causal n_detected_comb_twas
1 1-1 283 53 106
2 1-2 342 53 119
3 1-3 315 62 117
n_detected_comb_twas_in_causal
1 54
2 54
3 62sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.497076
Simulation 3: Liver and 5% correlated tissues
Shared effect size parameters
30% PVE and 2.5e-4 prior inclusion for SNPs, 5% PVE and 0.015 prior inclusion for Liver, 1% PVE and 0.003 prior inclusion for other three mixed groups.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_four_tissues_05/"
runtag = "ukb-s80.45-3_corr"
configtag <- 1
simutags <- paste(1, 1:3, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8#results using PIP threshold (gene+tissue)
results_df[,c("simutag", "n_causal", "n_detected_pip", "n_detected_pip_in_causal")] simutag n_causal n_detected_pip n_detected_pip_in_causal
1 1-1 182 24 21
2 1-2 171 23 22
3 1-3 180 15 14#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.9193548#results using combined PIP threshold
results_df[,c("simutag", "n_causal_combined", "n_detected_comb_pip", "n_detected_comb_pip_in_causal")] simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 1-1 181 56 49
2 1-2 170 47 43
3 1-3 180 41 37#mean percent causal using combined PIP > 0.8
sum(results_df$n_detected_comb_pip_in_causal)/sum(results_df$n_detected_comb_pip)[1] 0.8958333#prior inclusion and mean prior inclusion
results_df[,c(which(colnames(results_df)=="simutag"), setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df))))] simutag prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
1 1-1 0.0002390564 0.01441694 0.004476677 0.001645418 0.003315762
2 1-2 0.0002709239 0.01398781 0.003382912 0.002615942 0.003369291
3 1-3 0.0002718175 0.01076297 0.001823666 0.002717918 0.002124119colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
0.0002605993 0.0130559074 0.0032277514 0.0023264262 0.0029363908 #prior variance and mean prior variance
results_df[,c(which(colnames(results_df)=="simutag"), grep("prior_var", names(results_df)))] simutag prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
1 1-1 7.909464 23.14759 23.14759 23.14759
2 1-2 8.180589 22.46541 22.46541 22.46541
3 1-3 8.100000 28.78220 28.78220 28.78220
prior_var_weight4
1 23.14759
2 22.46541
3 28.78220colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
8.063351 24.798397 24.798397 24.798397
prior_var_weight4
24.798397 #PVE and mean PVE
results_df[,c(which(colnames(results_df)=="simutag"), grep("pve", names(results_df)))] simutag pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
1 1-1 0.2225767 0.05484088 0.016936814 0.006238728 0.012553200
2 1-2 0.2608940 0.05164042 0.012421530 0.009626231 0.012379928
3 1-3 0.2591759 0.05090751 0.008579052 0.012813689 0.009999266colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
0.247548867 0.052462937 0.012645799 0.009559549 0.011644131 #TWAS results
results_df[,c(which(colnames(results_df)=="simutag"), grep("twas", names(results_df)))] simutag n_detected_twas n_detected_twas_in_causal n_detected_comb_twas
1 1-1 371 62 134
2 1-2 290 52 116
3 1-3 348 54 135
n_detected_comb_twas_in_causal
1 62
2 53
3 54sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.438961
Separate effect size parameters
For the cTWAS analysis, each tissue had its own prior inclusion parameter end effect size parameter.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_four_tissues_05/"
runtag = "ukb-s80.45-3_corr"
configtag <- 2
simutags <- paste(1, 1:3, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8#results using PIP threshold (gene+tissue)
results_df[,c("simutag", "n_causal", "n_detected_pip", "n_detected_pip_in_causal")] simutag n_causal n_detected_pip n_detected_pip_in_causal
1 1-1 182 23 21
2 1-2 171 24 23
3 1-3 180 17 15#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.921875#results using combined PIP threshold
results_df[,c("simutag", "n_causal_combined", "n_detected_comb_pip", "n_detected_comb_pip_in_causal")] simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 1-1 181 58 50
2 1-2 170 47 43
3 1-3 180 40 36#mean percent causal using combined PIP > 0.8
sum(results_df$n_detected_comb_pip_in_causal)/sum(results_df$n_detected_comb_pip)[1] 0.8896552#prior inclusion and mean prior inclusion
results_df[,c(which(colnames(results_df)=="simutag"), setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df))))] simutag prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
1 1-1 0.0002365878 0.01220395 0.006245601 0.002380125 0.005435014
2 1-2 0.0002702815 0.01264810 0.003874380 0.003446936 0.004534815
3 1-3 0.0002706864 0.01373695 0.001432265 0.002097530 0.001674452colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3 prior_weight4
0.0002591852 0.0128630025 0.0038507491 0.0026415307 0.0038814268 #prior variance and mean prior variance
results_df[,c(which(colnames(results_df)=="simutag"), grep("prior_var", names(results_df)))] simutag prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
1 1-1 7.973941 31.16032 13.95068 11.73871
2 1-2 8.194644 27.41263 18.42119 15.04309
3 1-3 8.107647 20.31424 44.11721 43.79737
prior_var_weight4
1 12.48241
2 12.41527
3 44.11027colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
8.092077 26.295729 25.496361 23.526386
prior_var_weight4
23.002649 #PVE and mean PVE
results_df[,c(which(colnames(results_df)=="simutag"), grep("pve", names(results_df)))] simutag pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
1 1-1 0.2220739 0.06249251 0.01424098 0.004576506 0.011095950
2 1-2 0.2607225 0.05697729 0.01166515 0.008493450 0.009208331
3 1-3 0.2583411 0.04585817 0.01032765 0.015047700 0.012080297colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3 pve_weight4
0.247045851 0.055109321 0.012077926 0.009372552 0.010794860 #TWAS results
results_df[,c(which(colnames(results_df)=="simutag"), grep("twas", names(results_df)))] simutag n_detected_twas n_detected_twas_in_causal n_detected_comb_twas
1 1-1 371 62 134
2 1-2 290 52 116
3 1-3 348 54 135
n_detected_comb_twas_in_causal
1 62
2 53
3 54sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.438961
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpubr_0.6.0 ggplot2_3.4.4 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.32 bslib_0.4.0 purrr_1.0.2
[5] carData_3.0-5 colorspace_2.0-3 vctrs_0.6.4 generics_0.1.3
[9] htmltools_0.5.3 yaml_2.3.5 utf8_1.2.2 rlang_1.1.1
[13] jquerylib_0.1.4 later_1.3.0 pillar_1.8.1 glue_1.6.2
[17] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 stringr_1.5.0
[21] munsell_0.5.0 ggsignif_0.6.3 gtable_0.3.1 evaluate_0.16
[25] labeling_0.4.2 knitr_1.40 callr_3.7.2 fastmap_1.1.0
[29] httpuv_1.6.5 ps_1.7.1 fansi_1.0.3 highr_0.9
[33] broom_1.0.1 Rcpp_1.0.9 backports_1.4.1 promises_1.2.0.1
[37] scales_1.2.1 cachem_1.0.6 jsonlite_1.8.0 abind_1.4-5
[41] farver_2.1.1 fs_1.5.2 digest_0.6.29 stringi_1.7.8
[45] rstatix_0.7.2 processx_3.7.0 dplyr_1.0.10 getPass_0.2-2
[49] rprojroot_2.0.3 grid_4.2.0 cli_3.6.1 tools_4.2.0
[53] magrittr_2.0.3 sass_0.4.2 tibble_3.1.8 car_3.1-1
[57] tidyr_1.3.0 whisker_0.4 pkgconfig_2.0.3 data.table_1.14.2
[61] assertthat_0.2.1 rmarkdown_2.16 httr_1.4.4 rstudioapi_0.14
[65] R6_2.5.1 git2r_0.30.1 compiler_4.2.0