Simulation Results using three Tissues (uncorrelated)
shengqian
2023-10-13
Last updated: 2023-10-21
Checks: 5 2
Knit directory: cTWAS_analysis/
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source("/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/analysis/simulation_help_functions.R")Simulation 1: Three causal tissues with equal high PVE (3% each)
Separate effect size parameters
30% PVE and 2.5e-4 prior inclusion for SNPs, 3% PVE and 0.009 prior inclusion for Liver expression, 3% PVE and 0.009 prior inclusion for Lung expression, 3% PVE and 0.009 prior inclusion for Brain Hippocampus expression. For the cTWAS analysis, each tissue had its own prior inclusion parameter and effect size parameter.
#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 1-1 211 18 15
2 1-2 228 33 32
3 1-3 199 19 19
4 1-4 202 15 14
5 1-5 230 45 35#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.8846154#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 1-1 211 31 26
2 1-2 228 53 50
3 1-3 199 29 28
4 1-4 201 39 34
5 1-5 229 72 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] 0.8705357#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
1 1-1 0.0002425920 0.010177436 0.008623747 0.004769606
2 1-2 0.0002466862 0.010292635 0.003108254 0.009989375
3 1-3 0.0002626968 0.005504924 0.009932813 0.006229771
4 1-4 0.0002604598 0.002595350 0.011445972 0.008293892
5 1-5 0.0002372624 0.005251018 0.019663392 0.009663112colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3
0.0002499394 0.0067642725 0.0105548357 0.0077891513 #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 9.038498 3.856506 14.351212 32.70334
2 1-2 8.226705 15.565359 35.320799 18.97877
3 1-3 8.540242 28.372561 6.987738 26.73232
4 1-4 8.742224 15.309434 10.017471 26.50675
5 1-5 8.260003 30.294016 13.022285 23.62614colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
8.561535 18.679575 15.939901 25.709463 #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
1 1-1 0.2581101 0.006449976 0.02688094 0.02759495
2 1-2 0.2388928 0.026327607 0.02384552 0.03353988
3 1-3 0.2640931 0.025667024 0.01507540 0.02946211
4 1-4 0.2680371 0.006529511 0.02490409 0.03889286
5 1-5 0.2306967 0.026141228 0.05561673 0.04038916colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3
0.25196596 0.01822307 0.02926453 0.03397579 #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 1-1 247 55 160
2 1-2 326 65 200
3 1-3 327 59 198
4 1-4 214 47 124
5 1-5 230 63 146
n_detected_comb_twas_in_causal
1 55
2 66
3 59
4 47
5 63sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.3502415
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Individual tissue analyses
#results using weight1
results_df[,c("simutag", colnames(results_df)[grep("weight1", colnames(results_df))])] simutag n_detected_weight1 n_detected_in_causal_weight1
1 1-1 21 18
2 1-2 32 30
3 1-3 21 21
4 1-4 15 14
5 1-5 17 15#mean percent causal using PIP > 0.8 for weight1
sum(results_df$n_detected_in_causal_weight1)/sum(results_df$n_detected_weight1)[1] 0.9245283#results using weight2
results_df[,c("simutag", colnames(results_df)[grep("weight2", colnames(results_df))])] simutag n_detected_weight2 n_detected_in_causal_weight2
1 1-1 27 22
2 1-2 28 26
3 1-3 20 16
4 1-4 22 21
5 1-5 38 33#mean percent causal using PIP > 0.8 for weight1
sum(results_df$n_detected_in_causal_weight2)/sum(results_df$n_detected_weight2)[1] 0.8740741#results using weight3
results_df[,c("simutag", colnames(results_df)[grep("weight3", colnames(results_df))])] simutag n_detected_weight3 n_detected_in_causal_weight3
1 1-1 22 19
2 1-2 25 20
3 1-3 21 19
4 1-4 23 18
5 1-5 32 25#mean percent causal using PIP > 0.8 for weight3
sum(results_df$n_detected_in_causal_weight3)/sum(results_df$n_detected_weight3)[1] 0.8211382#results using combined analysis
results_df[,c("simutag", colnames(results_df)[grep("combined", colnames(results_df))])] simutag n_causal_combined n_detected_combined n_detected_in_causal_combined
1 1-1 211 53 44
2 1-2 228 59 51
3 1-3 199 47 41
4 1-4 201 40 35
5 1-5 229 70 57#mean percent causal using PIP > 0.8 for weight3
sum(results_df$n_detected_in_causal_combined)/sum(results_df$n_detected_combined)[1] 0.8475836
Simulation 2: Three causal tissues with equal low PVE (1% each)
Separate effect size parameters
30% PVE and 2.5e-4 prior inclusion for SNPs, 1% PVE and 0.003 prior inclusion for Liver expression, 1% PVE and 0.003 prior inclusion for Lung expression, 1% PVE and 0.003 prior inclusion for Brain Hippocampus expression. Each tissue had its own prior inclusion parameter and effect size parameter.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_drop_merge/"
runtag = "ukb-s80.45-3_uncorr"
configtag <- 2
simutags <- paste(2, 1:5, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8results_df <- get_sim_joint_res(results_dir,runtag,configtag,simutags,thin,sample_size,PIP_threshold)#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 2-1 66 10 10
2 2-2 80 12 11
3 2-3 60 9 9
4 2-4 70 7 6
5 2-5 67 2 0#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.9#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 2-1 66 16 14
2 2-2 80 19 17
3 2-3 60 14 13
4 2-4 70 11 9
5 2-5 67 10 6#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] 0.8428571#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
1 2-1 0.0002315651 0.001568621 0.002224819 0.004115142
2 2-2 0.0002800566 0.003182041 0.004210946 0.007715860
3 2-3 0.0002814913 0.007796100 0.002747631 0.005283211
4 2-4 0.0002955710 0.002673367 0.010048005 0.003678914
5 2-5 0.0002608473 0.004279611 0.003968238 0.002141939colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3
0.0002699062 0.0038999481 0.0046399278 0.0045870132 #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 2-1 8.767998 68.34907 28.857947 20.589987
2 2-2 7.609580 38.69702 7.795359 19.540985
3 2-3 7.387488 11.73957 21.807436 12.946441
4 2-4 7.829719 21.89263 7.033374 9.215843
5 2-5 7.889612 11.77339 21.272685 13.379534colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
7.896879 30.490337 17.353360 15.134558 #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
1 2-1 0.2390043 0.017618804 0.013945043 0.014989805
2 2-2 0.2508642 0.020235268 0.007129772 0.026673860
3 2-3 0.2447901 0.015040262 0.013014363 0.012100505
4 2-4 0.2724208 0.009617943 0.015349823 0.005998047
5 2-5 0.2422558 0.008280022 0.018334952 0.005069945colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3
0.24986705 0.01415846 0.01355479 0.01296643 #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 2-1 160 20 103
2 2-2 171 24 107
3 2-3 195 24 126
4 2-4 124 20 81
5 2-5 126 14 81
n_detected_comb_twas_in_causal
1 20
2 24
3 24
4 20
5 14sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.2048193y1 <- results_df$prior_weight1
y2 <- results_df$prior_weight2
y3 <- results_df$prior_weight3
truth <- rbind(c(1,0.003),c(2,0.003),c(3,0.003))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,0.025),ylab="Prior inclusion")
y1 <- results_df$prior_weight1/results_df$prior_snp
y2 <- results_df$prior_weight2/results_df$prior_snp
y3 <- results_df$prior_weight3/results_df$prior_snp
truth <- rbind(c(1,12),c(2,12),c(3,12))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,60),ylab="Enrichment")
y1 <- results_df$pve_weight1
y2 <- results_df$pve_weight2
y3 <- results_df$pve_weight3
truth <- rbind(c(1,0.01),c(2,0.01),c(3,0.01))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,0.05),ylab="PVE")
Individual tissue analyses
For the cTWAS analysis, each tissue was analyzed individually and the results were combined.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_drop_merge/"
runtag = "ukb-s80.45-3_uncorr"
configtag <- 2
simutags <- paste(2, 1:5, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8results_df <- get_sim_ind_res(results_dir,runtag,configtag,simutags,thin,sample_size,PIP_threshold)#results using weight1
results_df[,c("simutag", colnames(results_df)[grep("weight1", colnames(results_df))])] simutag n_detected_weight1 n_detected_in_causal_weight1
1 2-1 8 7
2 2-2 11 10
3 2-3 7 7
4 2-4 5 4
5 2-5 5 5#mean percent causal using PIP > 0.8 for weight1
sum(results_df$n_detected_in_causal_weight1)/sum(results_df$n_detected_weight1)[1] 0.9166667#results using weight2
results_df[,c("simutag", colnames(results_df)[grep("weight2", colnames(results_df))])] simutag n_detected_weight2 n_detected_in_causal_weight2
1 2-1 3 2
2 2-2 7 4
3 2-3 7 6
4 2-4 6 5
5 2-5 7 4#mean percent causal using PIP > 0.8 for weight1
sum(results_df$n_detected_in_causal_weight2)/sum(results_df$n_detected_weight2)[1] 0.7#results using weight3
results_df[,c("simutag", colnames(results_df)[grep("weight3", colnames(results_df))])] simutag n_detected_weight3 n_detected_in_causal_weight3
1 2-1 4 4
2 2-2 14 12
3 2-3 8 7
4 2-4 5 4
5 2-5 1 1#mean percent causal using PIP > 0.8 for weight3
sum(results_df$n_detected_in_causal_weight3)/sum(results_df$n_detected_weight3)[1] 0.875#results using combined analysis
results_df[,c("simutag", colnames(results_df)[grep("combined", colnames(results_df))])] simutag n_causal_combined n_detected_combined n_detected_in_causal_combined
1 2-1 66 12 11
2 2-2 80 23 19
3 2-3 60 20 18
4 2-4 70 11 8
5 2-5 67 8 5#mean percent causal using PIP > 0.8 for weight3
sum(results_df$n_detected_in_causal_combined)/sum(results_df$n_detected_combined)[1] 0.8243243Figure
Simulation 3: Three causal tissues with unequal PVE
Separate effect size parameters
30% PVE and 2.5e-4 prior inclusion for SNPs, 3% PVE and 0.009 prior inclusion for Liver expression, 2% PVE and 0.006 prior inclusion for Lung expression, 1% PVE and 0.003 prior inclusion for Brain Hippocampus expression. Each tissue had its own prior inclusion parameter and effect size parameter.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_drop_merge/"
runtag = "ukb-s80.45-3_uncorr"
configtag <- 2
simutags <- paste(3, 1:5, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8results_df <- get_sim_joint_res(results_dir,runtag,configtag,simutags,thin,sample_size,PIP_threshold)#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 3-1 129 13 12
2 3-2 154 15 13
3 3-3 119 16 15
4 3-4 129 19 18
5 3-5 140 17 15#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.9125#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 3-1 129 26 22
2 3-2 153 30 24
3 3-3 118 22 20
4 3-4 129 35 31
5 3-5 139 27 23#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] 0.8571429#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
1 3-1 0.0002595366 0.007402438 0.004632794 0.002491405
2 3-2 0.0002790883 0.005940978 0.004177815 0.011391572
3 3-3 0.0002755165 0.004023845 0.004185074 0.006010862
4 3-4 0.0002173136 0.008609928 0.004372277 0.004850031
5 3-5 0.0002534751 0.010863189 0.008317122 0.003776230colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3
0.000256986 0.007368075 0.005137016 0.005704020 #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 3-1 8.218313 15.99248 10.340997 18.268167
2 3-2 7.111651 26.20390 13.757128 12.163354
3 3-3 7.785969 40.83585 13.481862 13.289472
4 3-4 9.186761 23.66419 21.154161 9.848333
5 3-5 7.832367 23.24540 5.908057 17.928854colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
8.027012 25.988364 12.928441 14.299636 #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
1 3-1 0.2510808 0.01945432 0.01040555 0.008051828
2 3-2 0.2336384 0.02558289 0.01248351 0.024512754
3 3-3 0.2525181 0.02700278 0.01225499 0.014131868
4 3-4 0.2350074 0.03348242 0.02008923 0.008450109
5 3-5 0.2337010 0.04149733 0.01067278 0.011977498colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3
0.24118915 0.02940395 0.01318121 0.01342481 #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 3-1 260 34 159
2 3-2 217 38 143
3 3-3 200 33 134
4 3-4 197 36 111
5 3-5 243 39 148
n_detected_comb_twas_in_causal
1 36
2 38
3 34
4 36
5 39sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.2633094y1 <- results_df$prior_weight1
y2 <- results_df$prior_weight2
y3 <- results_df$prior_weight3
truth <- rbind(c(1,0.009),c(2,0.006),c(3,0.003))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,0.025),ylab="Prior inclusion")
| Version | Author | Date |
|---|---|---|
| f296597 | sq-96 | 2023-10-20 |
y1 <- results_df$prior_weight1/results_df$prior_snp
y2 <- results_df$prior_weight2/results_df$prior_snp
y3 <- results_df$prior_weight3/results_df$prior_snp
truth <- rbind(c(1,36),c(2,24),c(3,12))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,60),ylab="Enrichment")
y1 <- results_df$pve_weight1
y2 <- results_df$pve_weight2
y3 <- results_df$pve_weight3
truth <- rbind(c(1,0.03),c(2,0.02),c(3,0.01))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,0.05),ylab="PVE")
Individual tissue analyses
For the cTWAS analysis, each tissue was analyzed individually and the results were combined.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_drop_merge/"
runtag = "ukb-s80.45-3_uncorr"
configtag <- 2
simutags <- paste(3, 1:5, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8results_df <- get_sim_ind_res(results_dir,runtag,configtag,simutags,thin,sample_size,PIP_threshold)#results using weight1
results_df[,c("simutag", colnames(results_df)[grep("weight1", colnames(results_df))])] simutag n_detected_weight1 n_detected_in_causal_weight1
1 3-1 20 16
2 3-2 20 16
3 3-3 12 12
4 3-4 23 20
5 3-5 18 16#mean percent causal using PIP > 0.8 for weight1
sum(results_df$n_detected_in_causal_weight1)/sum(results_df$n_detected_weight1)[1] 0.8602151#results using weight2
results_df[,c("simutag", colnames(results_df)[grep("weight2", colnames(results_df))])] simutag n_detected_weight2 n_detected_in_causal_weight2
1 3-1 9 7
2 3-2 14 10
3 3-3 7 7
4 3-4 14 13
5 3-5 9 9#mean percent causal using PIP > 0.8 for weight1
sum(results_df$n_detected_in_causal_weight2)/sum(results_df$n_detected_weight2)[1] 0.8679245#results using weight3
results_df[,c("simutag", colnames(results_df)[grep("weight3", colnames(results_df))])] simutag n_detected_weight3 n_detected_in_causal_weight3
1 3-1 7 7
2 3-2 15 11
3 3-3 4 3
4 3-4 13 11
5 3-5 6 5#mean percent causal using PIP > 0.8 for weight3
sum(results_df$n_detected_in_causal_weight3)/sum(results_df$n_detected_weight3)[1] 0.8222222#results using combined analysis
results_df[,c("simutag", colnames(results_df)[grep("combined", colnames(results_df))])] simutag n_causal_combined n_detected_combined n_detected_in_causal_combined
1 3-1 129 29 24
2 3-2 153 32 24
3 3-3 118 20 19
4 3-4 129 37 31
5 3-5 139 26 24#mean percent causal using PIP > 0.8 for weight3
sum(results_df$n_detected_in_causal_combined)/sum(results_df$n_detected_combined)[1] 0.8472222Figure
For the cTWAS analysis, each tissue was analyzed individually and the results were combined. 
## Simulation 4: Two causal tissues with unequal PVE and one non-causal tissue
Separate effect size parameters
30% PVE and 2.5e-4 prior inclusion for SNPs, 3% PVE and 0.009 prior inclusion for Liver expression, 1% PVE and 0.003 prior inclusion for Lung expression, Brain Hippocampus expression has no effects. Each tissue had its own prior inclusion parameter and effect size parameter.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_drop_merge/"
runtag = "ukb-s80.45-3_uncorr"
configtag <- 2
simutags <- paste(4, 1:5, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8results_df <- get_sim_joint_res(results_dir,runtag,configtag,simutags,thin,sample_size,PIP_threshold)#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 4-1 85 14 14
2 4-2 93 15 14
3 4-3 116 22 21
4 4-4 82 13 13
5 4-5 99 11 10#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.96#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 4-1 85 19 18
2 4-2 93 23 18
3 4-3 116 33 30
4 4-4 82 14 14
5 4-5 99 16 13#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] 0.8857143#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
1 4-1 0.0002836507 0.007823094 0.002552276 0.0031298371
2 4-2 0.0002505663 0.004055552 0.003984521 0.0025329456
3 4-3 0.0002791923 0.012775958 0.004421434 0.0023033326
4 4-4 0.0002728597 0.008072991 0.003259917 0.0022185325
5 4-5 0.0002469015 0.007428323 0.007842161 0.0009239835colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3
0.0002666341 0.0080311834 0.0044120619 0.0022217263 #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 4-1 7.316408 31.15189 17.048708 7.159584
2 4-2 8.034003 32.73799 16.948387 41.189388
3 4-3 7.762142 16.82626 19.447154 2.698047
4 4-4 8.071062 24.48370 6.994372 3.304820
5 4-5 8.015189 29.22436 4.708917 1.167933colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
7.839761 26.884840 13.029507 11.103954 #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
1 4-1 0.2442947 0.04004872 0.009451025 0.0039642830
2 4-2 0.2369665 0.02181864 0.014667778 0.0184572210
3 4-3 0.2551040 0.03532700 0.018675793 0.0010994138
4 4-4 0.2592402 0.03248158 0.004952393 0.0012970858
5 4-5 0.2329537 0.03567480 0.008020779 0.0001909137colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3
0.245711822 0.033070145 0.011153554 0.005001783 #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 4-1 153 24 98
2 4-2 214 30 134
3 4-3 138 32 92
4 4-4 111 21 76
5 4-5 169 22 106
n_detected_comb_twas_in_causal
1 24
2 30
3 32
4 21
5 22sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.2549407y1 <- results_df$prior_weight1
y2 <- results_df$prior_weight2
y3 <- results_df$prior_weight3
truth <- rbind(c(1,0.009),c(2,0.003),c(3,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,0.025),ylab="Prior inclusion")
y1 <- results_df$prior_weight1/results_df$prior_snp
y2 <- results_df$prior_weight2/results_df$prior_snp
y3 <- results_df$prior_weight3/results_df$prior_snp
truth <- rbind(c(1,36),c(2,12),c(3,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,60),ylab="Enrichment")
y1 <- results_df$pve_weight1
y2 <- results_df$pve_weight2
y3 <- results_df$pve_weight3
truth <- rbind(c(1,0.03),c(2,0.01),c(3,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,0.05),ylab="PVE")
Individual tissue analyses
For the cTWAS analysis, each tissue was analyzed individually and the results were combined.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_drop_merge/"
runtag = "ukb-s80.45-3_uncorr"
configtag <- 2
simutags <- paste(4, 1:5, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8results_df <- get_sim_ind_res(results_dir,runtag,configtag,simutags,thin,sample_size,PIP_threshold)#results using weight1
results_df[,c("simutag", colnames(results_df)[grep("weight1", colnames(results_df))])] simutag n_detected_weight1 n_detected_in_causal_weight1
1 4-1 14 14
2 4-2 21 18
3 4-3 24 21
4 4-4 13 13
5 4-5 17 14#mean percent causal using PIP > 0.8 for weight1
sum(results_df$n_detected_in_causal_weight1)/sum(results_df$n_detected_weight1)[1] 0.8988764#results using weight2
results_df[,c("simutag", colnames(results_df)[grep("weight2", colnames(results_df))])] simutag n_detected_weight2 n_detected_in_causal_weight2
1 4-1 3 3
2 4-2 9 8
3 4-3 14 14
4 4-4 4 4
5 4-5 5 1#mean percent causal using PIP > 0.8 for weight1
sum(results_df$n_detected_in_causal_weight2)/sum(results_df$n_detected_weight2)[1] 0.8571429#results using weight3
results_df[,c("simutag", colnames(results_df)[grep("weight3", colnames(results_df))])] simutag n_detected_weight3 n_detected_in_causal_weight3
1 4-1 4 3
2 4-2 9 5
3 4-3 3 2
4 4-4 4 4
5 4-5 2 1#mean percent causal using PIP > 0.8 for weight3
sum(results_df$n_detected_in_causal_weight3)/sum(results_df$n_detected_weight3)[1] 0.6818182#results using combined analysis
results_df[,c("simutag", colnames(results_df)[grep("combined", colnames(results_df))])] simutag n_causal_combined n_detected_combined n_detected_in_causal_combined
1 4-1 85 17 16
2 4-2 93 27 21
3 4-3 116 33 29
4 4-4 82 16 16
5 4-5 99 21 15#mean percent causal using PIP > 0.8 for weight3
sum(results_df$n_detected_in_causal_combined)/sum(results_df$n_detected_combined)[1] 0.8508772Figure
Simulation 5: one causal tissues and two non-causal tissues
Separate effect size parameters
30% PVE and 2.5e-4 prior inclusion for SNPs, 3% PVE and 0.009 prior inclusion for Liver expression, Lung expression and Brain Hippocampus expression has no effects. Each tissue had its own prior inclusion parameter and effect size parameter.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_drop_merge/"
runtag = "ukb-s80.45-3_uncorr"
configtag <- 2
simutags <- paste(5, 1:5, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8results_df <- get_sim_joint_res(results_dir,runtag,configtag,simutags,thin,sample_size,PIP_threshold)#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 5-1 68 12 12
2 5-2 63 11 10
3 5-3 78 20 17
4 5-4 66 10 8
5 5-5 54 8 6#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.8688525#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 5-1 68 19 19
2 5-2 63 15 13
3 5-3 78 24 21
4 5-4 66 20 15
5 5-5 54 11 8#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] 0.8539326#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
1 5-1 0.0002260741 0.012487622 0.006394432 0.002248323
2 5-2 0.0002520776 0.008855700 0.001506860 0.001028561
3 5-3 0.0002604092 0.015701737 0.002744097 0.001945911
4 5-4 0.0002723394 0.008886928 0.005554238 0.004179820
5 5-5 0.0002953418 0.004996529 0.002761793 0.001641676colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_snp prior_weight1 prior_weight2 prior_weight3
0.0002612484 0.0101857033 0.0037922838 0.0022088580 #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 5-1 9.701863 12.98372 5.581177 17.569708
2 5-2 9.089926 22.74965 8.864505 10.116937
3 5-3 8.942667 16.42152 8.574377 3.018352
4 5-4 8.244432 15.40885 6.278919 9.578499
5 5-5 7.343877 20.37894 11.734378 2.023874colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_snp prior_var_weight1 prior_var_weight2 prior_var_weight3
8.664553 17.588533 8.206671 8.461474 #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
1 5-1 0.2581893 0.02664432 0.007751532 0.0069884089
2 5-2 0.2697285 0.03310726 0.002901263 0.0018409162
3 5-3 0.2741294 0.04237274 0.005110482 0.0010390776
4 5-4 0.2643038 0.02250336 0.007574765 0.0070828842
5 5-5 0.2553187 0.01673307 0.007039000 0.0005877952colMeans(results_df[,grep("pve", names(results_df))]) pve_snp pve_weight1 pve_weight2 pve_weight3
0.264333936 0.028272151 0.006075409 0.003507816 #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 5-1 111 21 77
2 5-2 145 19 84
3 5-3 190 33 118
4 5-4 145 19 93
5 5-5 98 15 65
n_detected_comb_twas_in_causal
1 21
2 19
3 33
4 20
5 16sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.2494279y1 <- results_df$prior_weight1
y2 <- results_df$prior_weight2
y3 <- results_df$prior_weight3
truth <- rbind(c(1,0.009),c(2,0),c(3,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,0.025),ylab="Prior inclusion")
y1 <- results_df$prior_weight1/results_df$prior_snp
y2 <- results_df$prior_weight2/results_df$prior_snp
y3 <- results_df$prior_weight3/results_df$prior_snp
truth <- rbind(c(1,36),c(2,0),c(3,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,60),ylab="Enrichment")
y1 <- results_df$pve_weight1
y2 <- results_df$pve_weight2
y3 <- results_df$pve_weight3
truth <- rbind(c(1,0.03),c(2,0),c(3,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3))
plot_par(truth,est,xlabels = c("Liver","Lung","Hippocampus"),ylim=c(0,0.05),ylab="PVE")
Individual tissue analyses
For the cTWAS analysis, each tissue was analyzed individually and the results were combined.
results_dir <- "/project2/xinhe/shengqian/cTWAS/cTWAS_simulation/simulation_uncorrelated_drop_merge/"
runtag = "ukb-s80.45-3_uncorr"
configtag <- 2
simutags <- paste(5, 1:5, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8results_df <- get_sim_ind_res(results_dir,runtag,configtag,simutags,thin,sample_size,PIP_threshold) simutag n_detected_weight1 n_detected_in_causal_weight1
1 5-1 16 16
2 5-2 13 12
3 5-3 21 19
4 5-4 14 13
5 5-5 8 6[1] 0.9166667 simutag n_detected_weight2 n_detected_in_causal_weight2
1 5-1 2 2
2 5-2 6 4
3 5-3 9 5
4 5-4 5 3
5 5-5 4 2[1] 0.6153846 simutag n_detected_weight3 n_detected_in_causal_weight3
1 5-1 6 4
2 5-2 3 3
3 5-3 1 1
4 5-4 7 4
5 5-5 1 1[1] 0.7222222 simutag n_causal_combined n_detected_combined n_detected_in_causal_combined
1 5-1 68 18 16
2 5-2 63 15 12
3 5-3 78 25 19
4 5-4 66 19 13
5 5-5 54 10 6[1] 0.7586207Figure
For the cTWAS analysis, each tissue was analyzed individually and the results were combined. 
sessionInfo()R version 4.1.0 (2021-05-18)
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_3.4.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.35 bslib_0.4.1 purrr_1.0.2
[5] carData_3.0-4 colorspace_2.0-3 vctrs_0.6.3 generics_0.1.3
[9] htmltools_0.5.4 yaml_2.3.6 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] ggsignif_0.6.4 munsell_0.5.0 gtable_0.3.1 evaluate_0.19
[25] labeling_0.4.2 knitr_1.41 callr_3.7.3 fastmap_1.1.0
[29] httpuv_1.6.7 ps_1.7.2 fansi_1.0.3 highr_0.9
[33] broom_1.0.2 Rcpp_1.0.9 backports_1.2.1 promises_1.2.0.1
[37] scales_1.2.1 cachem_1.0.6 jsonlite_1.8.4 abind_1.4-5
[41] farver_2.1.0 fs_1.5.2 digest_0.6.31 stringi_1.7.8
[45] rstatix_0.7.2 processx_3.8.0 dplyr_1.0.10 getPass_0.2-2
[49] rprojroot_2.0.3 grid_4.1.0 cli_3.6.1 tools_4.1.0
[53] magrittr_2.0.3 sass_0.4.4 tibble_3.1.8 car_3.1-1
[57] tidyr_1.3.0 whisker_0.4.1 pkgconfig_2.0.3 data.table_1.14.6
[61] assertthat_0.2.1 rmarkdown_2.19 httr_1.4.4 rstudioapi_0.14
[65] R6_2.5.1 git2r_0.30.1 compiler_4.1.0