BMI - Brain Spinal cord cervical c-1
sheng Qian
2021-2-6
Last updated: 2022-02-13
Checks: 6 1
Knit directory: cTWAS_analysis/
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| Rmd | 87fee8b | sq-96 | 2022-02-13 | update |
Introduction
Weight QC
qclist_all <- list()
qc_files <- paste0(results_dir, "/", list.files(results_dir, pattern="exprqc.Rd"))
for (i in 1:length(qc_files)){
load(qc_files[i])
chr <- unlist(strsplit(rev(unlist(strsplit(qc_files[i], "_")))[1], "[.]"))[1]
qclist_all[[chr]] <- cbind(do.call(rbind, lapply(qclist,unlist)), as.numeric(substring(chr,4)))
}
qclist_all <- data.frame(do.call(rbind, qclist_all))
colnames(qclist_all)[ncol(qclist_all)] <- "chr"
rm(qclist, wgtlist, z_gene_chr)
#number of imputed weights
nrow(qclist_all)[1] 10532#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1032 741 611 415 500 593 513 394 399 413 617 585 228 354 366 467
17 18 19 20 21 22
630 170 801 314 124 265 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8584#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8150399Load ctwas results
Check convergence of parameters
library(ggplot2)
library(cowplot)
********************************************************Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot())********************************************************load(paste0(results_dir, "/", analysis_id, "_ctwas.s2.susieIrssres.Rd"))
df <- data.frame(niter = rep(1:ncol(group_prior_rec), 2),
value = c(group_prior_rec[1,], group_prior_rec[2,]),
group = rep(c("Gene", "SNP"), each = ncol(group_prior_rec)))
df$group <- as.factor(df$group)
df$value[df$group=="SNP"] <- df$value[df$group=="SNP"]*thin #adjust parameter to account for thin argument
p_pi <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(pi)) +
ggtitle("Prior mean") +
theme_cowplot()
df <- data.frame(niter = rep(1:ncol(group_prior_var_rec), 2),
value = c(group_prior_var_rec[1,], group_prior_var_rec[2,]),
group = rep(c("Gene", "SNP"), each = ncol(group_prior_var_rec)))
df$group <- as.factor(df$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0053427438 0.0002972845 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
30.33477 17.09673 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10532 7535010#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.005078533 0.113944037 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.4380626 15.3065312Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag susie_pip mu2 PVE z
9777 KLHDC8B 3_34 1.0000000 2542.57627 7.564782e-03 -5.051557
789 SDHA 5_1 1.0000000 21042.52138 6.260661e-02 3.012479
11293 AC078842.3 7_84 1.0000000 19171.29914 5.703927e-02 -3.207993
4274 IGHMBP2 11_38 1.0000000 31336.96431 9.323508e-02 -4.378813
5140 MFAP1 15_16 1.0000000 29203.27973 8.688685e-02 4.302998
695 MAPK6 15_21 1.0000000 28649.03417 8.523784e-02 -4.646216
7232 PPM1M 3_36 0.9999999 352.45212 1.048631e-03 4.731816
1472 ASCC2 22_10 0.7776235 8574.76169 1.983873e-02 -2.815534
8471 EFEMP2 11_36 0.7758290 50.60634 1.168136e-04 -7.483691
12868 PANO1 11_1 0.7575940 27.27802 6.148538e-05 4.978592
3443 ZMIZ2 7_33 0.7504608 67.76347 1.513025e-04 -8.105339
7328 ZNF12 7_9 0.7474493 28.09975 6.248944e-05 5.064700
1464 RASD2 22_14 0.7402740 24.87497 5.478700e-05 -4.361708
12828 RP11-340F14.6 12_74 0.7297061 30.01736 6.516929e-05 -4.742464
11162 VPS52 6_28 0.7203038 127.03285 2.722414e-04 1.606101
2760 PDCD10 3_103 0.7017297 23.84490 4.978378e-05 -4.064505
2845 ITGB6 2_96 0.6533228 59.14060 1.149572e-04 5.514945
3959 KLK14 19_35 0.6372520 28.25002 5.356147e-05 -4.061766
1588 NINL 20_19 0.6092758 34.74160 6.297761e-05 -5.532192
1304 CBX5 12_33 0.6017194 25.83998 4.626033e-05 4.691159
num_eqtl
9777 2
789 1
11293 1
4274 1
5140 1
695 1
7232 2
1472 2
8471 2
12868 2
3443 1
7328 2
1464 2
12828 2
11162 1
2760 1
2845 1
3959 1
1588 2
1304 1Genes with largest effect sizes
#plot PIP vs effect size
plot(ctwas_gene_res$susie_pip, ctwas_gene_res$mu2, xlab="PIP", ylab="mu^2", main="Gene PIPs vs Effect Size")
#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z num_eqtl
10032 SLC38A3 3_35 0 66888.53 0.00000000 6.725828 1
7397 CCDC171 9_13 0 42632.91 0.00000000 8.471161 2
38 RBM6 3_35 0 40475.73 0.00000000 12.536042 1
7227 MST1R 3_35 0 34543.29 0.00000000 -12.635394 2
8111 CALML6 1_1 0 32850.88 0.00000000 -5.718338 1
4274 IGHMBP2 11_38 1 31336.96 0.09323508 -4.378813 1
9078 STX19 3_59 0 30618.53 0.00000000 -5.059656 1
5140 MFAP1 15_16 1 29203.28 0.08688685 4.302998 1
695 MAPK6 15_21 1 28649.03 0.08523784 -4.646216 1
1280 WDR76 15_16 0 25920.29 0.00000000 4.454356 1
2418 CPT1A 11_38 0 24846.46 0.00000000 -4.676837 1
4970 TMOD3 15_21 0 23045.27 0.00000000 5.411998 1
7223 RNF123 3_35 0 22889.67 0.00000000 -10.959165 1
789 SDHA 5_1 1 21042.52 0.06260661 3.012479 1
4873 TUBGCP4 15_16 0 20509.43 0.00000000 3.366163 1
11293 AC078842.3 7_84 1 19171.30 0.05703927 -3.207993 1
7966 ADAL 15_16 0 17919.16 0.00000000 -2.861302 1
7967 LCMT2 15_16 0 17919.16 0.00000000 -2.861302 1
9868 HYAL3 3_35 0 17850.05 0.00000000 6.264073 2
859 MCM6 2_80 0 17636.26 0.00000000 -3.886179 1Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
4274 IGHMBP2 11_38 1.00000000 31336.96431 9.323508e-02 -4.378813
5140 MFAP1 15_16 1.00000000 29203.27973 8.688685e-02 4.302998
695 MAPK6 15_21 1.00000000 28649.03417 8.523784e-02 -4.646216
789 SDHA 5_1 1.00000000 21042.52138 6.260661e-02 3.012479
11293 AC078842.3 7_84 1.00000000 19171.29914 5.703927e-02 -3.207993
1472 ASCC2 22_10 0.77762350 8574.76169 1.983873e-02 -2.815534
9777 KLHDC8B 3_34 1.00000000 2542.57627 7.564782e-03 -5.051557
262 CPS1 2_124 0.49202420 4722.14667 6.912711e-03 3.534889
2872 LANCL1 2_124 0.49202420 4722.14667 6.912711e-03 -3.534889
7232 PPM1M 3_36 0.99999991 352.45212 1.048631e-03 4.731816
11162 VPS52 6_28 0.72030380 127.03285 2.722414e-04 1.606101
8727 ASPHD1 16_24 0.59150663 120.67330 2.123700e-04 -11.848514
10189 ATP2A1 16_23 0.50557684 100.96198 1.518684e-04 -10.759014
3443 ZMIZ2 7_33 0.75046079 67.76347 1.513025e-04 -8.105339
6433 GPR61 1_67 0.56829014 81.16210 1.372290e-04 8.755235
10756 LY6G5C 6_26 0.43404866 106.00275 1.368920e-04 8.417860
12619 CTD-2186M15.3 5_22 0.02120586 2014.86154 1.271228e-04 2.933876
8471 EFEMP2 11_36 0.77582899 50.60634 1.168136e-04 -7.483691
2845 ITGB6 2_96 0.65332282 59.14060 1.149572e-04 5.514945
13013 DHRS11 17_22 0.51954196 63.45942 9.809326e-05 -8.128326
num_eqtl
4274 1
5140 1
695 1
789 1
11293 1
1472 2
9777 2
262 1
2872 1
7232 2
11162 1
8727 1
10189 1
3443 1
6433 1
10756 1
12619 2
8471 2
2845 1
13013 1Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
7227 MST1R 3_35 0.000000e+00 34543.28743 0.000000e+00 -12.635394
38 RBM6 3_35 0.000000e+00 40475.72984 0.000000e+00 12.536042
8727 ASPHD1 16_24 5.915066e-01 120.67330 2.123700e-04 -11.848514
1048 EFR3B 2_15 1.114961e-08 203.24843 6.742318e-12 11.586593
8728 KCTD13 16_24 6.773020e-02 115.83721 2.334280e-05 -11.490673
8068 INO80E 16_24 1.260053e-02 103.17532 3.868004e-06 11.076716
7223 RNF123 3_35 0.000000e+00 22889.67075 0.000000e+00 -10.959165
1721 MAPK3 16_24 1.145581e-02 103.03163 3.511713e-06 10.880016
10189 ATP2A1 16_23 5.055768e-01 100.96198 1.518684e-04 -10.759014
11438 NPIPB7 16_23 7.272766e-02 100.96337 2.184670e-05 10.509650
10225 SULT1A1 16_23 2.946593e-02 99.06621 8.684967e-06 10.367233
10271 C6orf106 6_28 5.814351e-05 124.59696 2.155416e-08 -10.263559
10322 SULT1A2 16_23 1.683314e-02 95.71737 4.793783e-06 -10.171155
7747 ZNF668 16_24 1.090314e-01 80.22421 2.602433e-05 10.000364
5341 SAE1 19_33 1.095253e-03 100.68705 3.281034e-07 9.848747
8426 C1QTNF4 11_29 6.345703e-03 90.11964 1.701460e-06 9.563515
11752 LINC00461 5_52 8.338963e-11 357.44552 8.868381e-14 9.418048
10335 IL27 16_23 1.421364e-02 81.11968 3.430472e-06 -9.140265
8427 NEGR1 1_46 9.461144e-02 76.49903 2.153387e-05 -8.928461
7515 PSMC3 11_29 6.783380e-03 78.60556 1.586434e-06 -8.866477
num_eqtl
7227 2
38 1
8727 1
1048 1
8728 1
8068 1
7223 1
1721 1
10189 1
11438 1
10225 1
10271 1
10322 2
7747 1
5341 1
8426 1
11752 1
10335 1
8427 1
7515 1Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$z[order(ctwas_gene_res$z)]
exp_z <- qnorm((1:nrow(ctwas_gene_res))/nrow(ctwas_gene_res))
plot(exp_z, obs_z, xlab="Expected z", ylab="Observed z", main="Gene z score Q-Q plot")
abline(a=0,b=1)
#plot z score vs PIP
plot(abs(ctwas_gene_res$z), ctwas_gene_res$susie_pip, xlab="abs(z)", ylab="PIP")
abline(v=sig_thresh, col="red", lty=2)
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.02136346#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
7227 MST1R 3_35 0.000000e+00 34543.28743 0.000000e+00 -12.635394
38 RBM6 3_35 0.000000e+00 40475.72984 0.000000e+00 12.536042
8727 ASPHD1 16_24 5.915066e-01 120.67330 2.123700e-04 -11.848514
1048 EFR3B 2_15 1.114961e-08 203.24843 6.742318e-12 11.586593
8728 KCTD13 16_24 6.773020e-02 115.83721 2.334280e-05 -11.490673
8068 INO80E 16_24 1.260053e-02 103.17532 3.868004e-06 11.076716
7223 RNF123 3_35 0.000000e+00 22889.67075 0.000000e+00 -10.959165
1721 MAPK3 16_24 1.145581e-02 103.03163 3.511713e-06 10.880016
10189 ATP2A1 16_23 5.055768e-01 100.96198 1.518684e-04 -10.759014
11438 NPIPB7 16_23 7.272766e-02 100.96337 2.184670e-05 10.509650
10225 SULT1A1 16_23 2.946593e-02 99.06621 8.684967e-06 10.367233
10271 C6orf106 6_28 5.814351e-05 124.59696 2.155416e-08 -10.263559
10322 SULT1A2 16_23 1.683314e-02 95.71737 4.793783e-06 -10.171155
7747 ZNF668 16_24 1.090314e-01 80.22421 2.602433e-05 10.000364
5341 SAE1 19_33 1.095253e-03 100.68705 3.281034e-07 9.848747
8426 C1QTNF4 11_29 6.345703e-03 90.11964 1.701460e-06 9.563515
11752 LINC00461 5_52 8.338963e-11 357.44552 8.868381e-14 9.418048
10335 IL27 16_23 1.421364e-02 81.11968 3.430472e-06 -9.140265
8427 NEGR1 1_46 9.461144e-02 76.49903 2.153387e-05 -8.928461
7515 PSMC3 11_29 6.783380e-03 78.60556 1.586434e-06 -8.866477
num_eqtl
7227 2
38 1
8727 1
1048 1
8728 1
8068 1
7223 1
1721 1
10189 1
11438 1
10225 1
10271 1
10322 2
7747 1
5341 1
8426 1
11752 1
10335 1
8427 1
7515 1Sensitivity, specificity and precision for silver standard genes
library("readxl")
known_annotations <- read_xlsx("data/summary_known_genes_annotations.xlsx", sheet="BMI")
known_annotations <- unique(known_annotations$`Gene Symbol`)
unrelated_genes <- ctwas_gene_res$genename[!(ctwas_gene_res$genename %in% known_annotations)]
#number of genes in known annotations
print(length(known_annotations))[1] 41#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 22#assign ctwas, TWAS, and bystander genes
ctwas_genes <- ctwas_gene_res$genename[ctwas_gene_res$susie_pip>0.8]
twas_genes <- ctwas_gene_res$genename[abs(ctwas_gene_res$z)>sig_thresh]
novel_genes <- ctwas_genes[!(ctwas_genes %in% twas_genes)]
#significance threshold for TWAS
print(sig_thresh)[1] 4.57565#number of ctwas genes
length(ctwas_genes)[1] 7#number of TWAS genes
length(twas_genes)[1] 225#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
789 SDHA 5_1 1 21042.52 0.06260661 3.012479 1
11293 AC078842.3 7_84 1 19171.30 0.05703927 -3.207993 1
4274 IGHMBP2 11_38 1 31336.96 0.09323508 -4.378813 1
5140 MFAP1 15_16 1 29203.28 0.08688685 4.302998 1#sensitivity / recall
sensitivity <- rep(NA,2)
names(sensitivity) <- c("ctwas", "TWAS")
sensitivity["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
sensitivity["TWAS"] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
sensitivity ctwas TWAS
0.00000000 0.07317073 #specificity
specificity <- rep(NA,2)
names(specificity) <- c("ctwas", "TWAS")
specificity["ctwas"] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
specificity["TWAS"] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
specificity ctwas TWAS
0.9993340 0.9788773 #precision / PPV
precision <- rep(NA,2)
names(precision) <- c("ctwas", "TWAS")
precision["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
precision["TWAS"] <- sum(twas_genes %in% known_annotations)/length(twas_genes)
precision ctwas TWAS
0.00000000 0.01333333 #ROC curves
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res$genename[ctwas_gene_res$susie_pip>=pip]
sensitivity[index] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
}
plot(1-specificity, sensitivity, type="l", xlim=c(0,1), ylim=c(0,1))
sig_thresh_range <- seq(from=0, to=max(abs(ctwas_gene_res$z)), length.out=length(pip_range))
for (index in 1:length(sig_thresh_range)){
sig_thresh_plot <- sig_thresh_range[index]
twas_genes <- ctwas_gene_res$genename[abs(ctwas_gene_res$z)>=sig_thresh_plot]
sensitivity[index] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
}
lines(1-specificity, sensitivity, xlim=c(0,1), ylim=c(0,1), col="red", lty=2)
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] readxl_1.3.1 cowplot_1.0.0 ggplot2_3.3.5 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.29 purrr_0.3.4 colorspace_2.0-2
[5] vctrs_0.3.8 generics_0.1.1 htmltools_0.5.2 yaml_2.2.1
[9] utf8_1.2.2 blob_1.2.2 rlang_0.4.12 jquerylib_0.1.4
[13] later_0.8.0 pillar_1.6.4 glue_1.5.1 withr_2.4.3
[17] DBI_1.1.1 bit64_4.0.5 lifecycle_1.0.1 stringr_1.4.0
[21] cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0 evaluate_0.14
[25] memoise_2.0.1 labeling_0.4.2 knitr_1.36 fastmap_1.1.0
[29] httpuv_1.5.1 fansi_0.5.0 highr_0.9 Rcpp_1.0.7
[33] promises_1.0.1 scales_1.1.1 cachem_1.0.6 farver_2.1.0
[37] fs_1.5.2 bit_4.0.4 digest_0.6.29 stringi_1.7.6
[41] dplyr_1.0.7 rprojroot_2.0.2 grid_3.6.1 tools_3.6.1
[45] magrittr_2.0.1 tibble_3.1.6 RSQLite_2.2.8 crayon_1.4.2
[49] whisker_0.3-2 pkgconfig_2.0.3 ellipsis_0.3.2 data.table_1.14.2
[53] assertthat_0.2.1 rmarkdown_2.11 R6_2.5.1 git2r_0.26.1
[57] compiler_3.6.1