SCZ - Brain Frontal Cortex BA9
sheng Qian
2021-2-6
Last updated: 2022-02-28
Checks: 6 1
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 11507#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1142 839 648 452 570 593 533 445 426 459 695 674 240 377 369 524
17 18 19 20 21 22
705 179 882 347 120 288 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 9036#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7853Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0101748 0.0002529 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
8.988 8.832 #report sample size
print(sample_size)[1] 82315#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11507 7573890#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01278 0.20549 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.07922 1.52765Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
genename region_tag susie_pip mu2 PVE z num_eqtl
13483 RP11-230C9.4 6_102 0.9873 24.01 0.0002879 -4.866 2
7629 THOC7 3_43 0.9813 34.05 0.0004059 -6.066 2
11134 ZNF823 19_10 0.9749 29.06 0.0003441 5.468 2
12304 AC012074.2 2_15 0.8746 21.96 0.0002333 4.623 1
10221 ACOT1 14_34 0.8412 22.58 0.0002308 4.284 3
9133 MAP3K11 11_36 0.8338 23.52 0.0002382 -4.544 1
108 ELAC2 17_11 0.8056 21.71 0.0002124 4.542 1
6584 TADA1 1_82 0.7488 23.41 0.0002130 -4.174 2
3758 BHLHE41 12_18 0.7421 22.88 0.0002063 4.024 1
6336 ARFGAP2 11_29 0.7316 23.92 0.0002126 4.740 1
6470 PLBD2 12_68 0.7284 20.64 0.0001827 3.986 1
9457 LPCAT4 15_10 0.7113 20.24 0.0001749 -4.205 2
14019 ERICD 8_92 0.7082 21.16 0.0001821 -4.157 1
6317 CNNM2 10_66 0.6998 48.44 0.0004117 -8.902 2
9024 FUT9 6_65 0.6687 29.04 0.0002360 5.427 1
12293 AC073283.4 2_30 0.6190 20.75 0.0001561 -3.969 2
491 TRAPPC3 1_22 0.6176 23.44 0.0001759 4.907 1
733 PPP2R5B 11_36 0.6117 24.40 0.0001813 -4.623 1
4755 SOX5 12_17 0.6076 25.53 0.0001884 3.966 1
7965 GTF2A1 14_39 0.5965 20.91 0.0001515 -4.352 1Genes with largest effect sizes
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
genename region_tag susie_pip mu2 PVE z num_eqtl
3530 CRHR1 17_27 2.970e-01 3095.21 1.117e-02 -3.36232 1
7121 ARHGAP27 17_27 0.000e+00 2291.37 0.000e+00 -2.08012 1
11430 HLA-DOA 6_26 6.350e-14 565.12 4.360e-16 6.84691 1
10942 HLA-DQA1 6_26 1.136e-13 486.81 6.717e-16 1.95455 1
10825 HLA-DRB1 6_26 1.346e-13 364.90 5.965e-16 -1.49219 1
11728 CLIC1 6_26 7.577e-13 363.20 3.343e-15 8.81238 2
11464 MSH5 6_26 6.363e-13 260.67 2.015e-15 7.40963 2
12571 C4A 6_26 1.336e-12 154.49 2.508e-15 5.29092 1
5338 PRDM5 4_78 0.000e+00 134.51 0.000e+00 0.06252 1
10287 FMNL1 17_27 0.000e+00 123.57 0.000e+00 0.66376 1
9925 ACBD4 17_27 0.000e+00 108.05 0.000e+00 0.26990 2
5014 NMT1 17_27 0.000e+00 100.35 0.000e+00 2.52018 2
10493 BTN3A2 6_20 1.836e-02 62.84 1.401e-05 8.94434 2
9090 DCAKD 17_27 0.000e+00 58.82 0.000e+00 -0.72756 1
2463 GOSR2 17_27 0.000e+00 56.03 0.000e+00 -3.44243 2
8482 TNXB 6_26 1.119e-13 55.82 7.589e-17 3.42145 1
6317 CNNM2 10_66 6.998e-01 48.44 4.117e-04 -8.90156 2
2871 PRSS16 6_21 5.667e-02 47.78 3.290e-05 -7.60149 1
13323 LINC01415 18_30 1.919e-01 46.63 1.087e-04 -5.32426 1
13051 RP11-490G2.2 1_60 1.276e-02 46.39 7.190e-06 7.32158 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3530 CRHR1 17_27 0.2970 3095.21 0.0111688 -3.362 1
6317 CNNM2 10_66 0.6998 48.44 0.0004117 -8.902 2
7629 THOC7 3_43 0.9813 34.05 0.0004059 -6.066 2
11134 ZNF823 19_10 0.9749 29.06 0.0003441 5.468 2
13483 RP11-230C9.4 6_102 0.9873 24.01 0.0002879 -4.866 2
9133 MAP3K11 11_36 0.8338 23.52 0.0002382 -4.544 1
9024 FUT9 6_65 0.6687 29.04 0.0002360 5.427 1
12304 AC012074.2 2_15 0.8746 21.96 0.0002333 4.623 1
10221 ACOT1 14_34 0.8412 22.58 0.0002308 4.284 3
1619 ZC3H7B 22_17 0.3965 45.66 0.0002200 5.015 3
6584 TADA1 1_82 0.7488 23.41 0.0002130 -4.174 2
6336 ARFGAP2 11_29 0.7316 23.92 0.0002126 4.740 1
108 ELAC2 17_11 0.8056 21.71 0.0002124 4.542 1
3758 BHLHE41 12_18 0.7421 22.88 0.0002063 4.024 1
4755 SOX5 12_17 0.6076 25.53 0.0001884 3.966 1
6470 PLBD2 12_68 0.7284 20.64 0.0001827 3.986 1
14019 ERICD 8_92 0.7082 21.16 0.0001821 -4.157 1
733 PPP2R5B 11_36 0.6117 24.40 0.0001813 -4.623 1
491 TRAPPC3 1_22 0.6176 23.44 0.0001759 4.907 1
748 ATP1B3 3_87 0.5394 26.72 0.0001751 3.663 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
10493 BTN3A2 6_20 1.836e-02 62.84 1.401e-05 8.944 2
6317 CNNM2 10_66 6.998e-01 48.44 4.117e-04 -8.902 2
11728 CLIC1 6_26 7.577e-13 363.20 3.343e-15 8.812 2
7067 ZSCAN12 6_22 1.489e-02 41.30 7.471e-06 -8.008 1
939 NT5C2 10_66 2.700e-01 37.11 1.217e-04 7.804 1
2871 PRSS16 6_21 5.667e-02 47.78 3.290e-05 -7.601 1
11464 MSH5 6_26 6.363e-13 260.67 2.015e-15 7.410 2
13051 RP11-490G2.2 1_60 1.276e-02 46.39 7.190e-06 7.322 1
11430 HLA-DOA 6_26 6.350e-14 565.12 4.360e-16 6.847 1
10634 ZSCAN23 6_22 8.161e-02 45.53 4.514e-05 -6.793 1
9986 ARL6IP4 12_75 7.424e-03 38.54 3.476e-06 6.491 1
12308 ZSCAN31 6_22 2.258e-02 29.34 8.050e-06 -6.446 2
6452 ABCB9 12_75 6.069e-03 37.31 2.751e-06 6.404 1
10988 ZSCAN26 6_22 1.391e-02 33.86 5.721e-06 6.349 3
6407 TAOK2 16_24 3.620e-01 37.85 1.665e-04 6.300 1
9343 ATG13 11_28 2.963e-01 35.09 1.263e-04 -6.169 1
11633 DNAJC19 3_111 2.203e-01 36.38 9.736e-05 6.158 1
11089 NMB 15_39 1.795e-01 40.21 8.768e-05 6.132 1
7629 THOC7 3_43 9.813e-01 34.05 4.059e-04 -6.066 2
8634 INO80E 16_24 1.278e-01 36.26 5.630e-05 6.051 2Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] 0.006431 genename region_tag susie_pip mu2 PVE z num_eqtl
10493 BTN3A2 6_20 1.836e-02 62.84 1.401e-05 8.944 2
6317 CNNM2 10_66 6.998e-01 48.44 4.117e-04 -8.902 2
11728 CLIC1 6_26 7.577e-13 363.20 3.343e-15 8.812 2
7067 ZSCAN12 6_22 1.489e-02 41.30 7.471e-06 -8.008 1
939 NT5C2 10_66 2.700e-01 37.11 1.217e-04 7.804 1
2871 PRSS16 6_21 5.667e-02 47.78 3.290e-05 -7.601 1
11464 MSH5 6_26 6.363e-13 260.67 2.015e-15 7.410 2
13051 RP11-490G2.2 1_60 1.276e-02 46.39 7.190e-06 7.322 1
11430 HLA-DOA 6_26 6.350e-14 565.12 4.360e-16 6.847 1
10634 ZSCAN23 6_22 8.161e-02 45.53 4.514e-05 -6.793 1
9986 ARL6IP4 12_75 7.424e-03 38.54 3.476e-06 6.491 1
12308 ZSCAN31 6_22 2.258e-02 29.34 8.050e-06 -6.446 2
6452 ABCB9 12_75 6.069e-03 37.31 2.751e-06 6.404 1
10988 ZSCAN26 6_22 1.391e-02 33.86 5.721e-06 6.349 3
6407 TAOK2 16_24 3.620e-01 37.85 1.665e-04 6.300 1
9343 ATG13 11_28 2.963e-01 35.09 1.263e-04 -6.169 1
11633 DNAJC19 3_111 2.203e-01 36.38 9.736e-05 6.158 1
11089 NMB 15_39 1.795e-01 40.21 8.768e-05 6.132 1
7629 THOC7 3_43 9.813e-01 34.05 4.059e-04 -6.066 2
8634 INO80E 16_24 1.278e-01 36.26 5.630e-05 6.051 2GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 28Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio BgRatio
21 Spasmophilia 0.0055 1/9 1/9703
24 Tetany 0.0055 1/9 1/9703
31 Tetany, Neonatal 0.0055 1/9 1/9703
56 Tetanilla 0.0055 1/9 1/9703
63 SENIOR-LOKEN SYNDROME 7 0.0055 1/9 1/9703
64 HYPOMAGNESEMIA 6, RENAL 0.0055 1/9 1/9703
67 PROSTATE CANCER, HEREDITARY, 2 0.0055 1/9 1/9703
68 SPASTIC PARAPLEGIA 53, AUTOSOMAL RECESSIVE 0.0055 1/9 1/9703
70 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.0055 1/9 1/9703
71 BARDET-BIEDL SYNDROME 16 0.0055 1/9 1/9703WebGestalt enrichment analysis for genes with PIP>0.5
Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!NULL
Sensitivity, specificity and precision for silver standard genes
#number of genes in known annotations
print(length(known_annotations))[1] 130#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 64#significance threshold for TWAS
print(sig_thresh)[1] 4.594#number of ctwas genes
length(ctwas_genes)[1] 7#number of TWAS genes
length(twas_genes)[1] 74#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
9133 MAP3K11 11_36 0.8338 23.52 0.0002382 -4.544 1
10221 ACOT1 14_34 0.8412 22.58 0.0002308 4.284 3
108 ELAC2 17_11 0.8056 21.71 0.0002124 4.542 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.06154 #specificity
print(specificity) ctwas TWAS
0.9997 0.9942 #precision / PPV
print(precision) ctwas TWAS
0.4286 0.1081 
Sensitivity, specificity and precision for silver standard genes - bystanders only
library(biomaRt)
library(GenomicRanges)Loading required package: stats4Loading required package: BiocGenericsLoading required package: parallel
Attaching package: 'BiocGenerics'The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLBThe following objects are masked from 'package:dplyr':
combine, intersect, setdiff, unionThe following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.minLoading required package: S4Vectors
Attaching package: 'S4Vectors'The following objects are masked from 'package:dplyr':
first, renameThe following object is masked from 'package:tidyr':
expandThe following object is masked from 'package:base':
expand.gridLoading required package: IRanges
Attaching package: 'IRanges'The following objects are masked from 'package:dplyr':
collapse, desc, sliceThe following object is masked from 'package:purrr':
reduceLoading required package: GenomeInfoDb#
ensembl <- useEnsembl(biomart="ENSEMBL_MART_ENSEMBL", dataset="hsapiens_gene_ensembl")
G_list <- getBM(filters= "chromosome_name", attributes = c("hgnc_symbol","chromosome_name","start_position","end_position","gene_biotype"), values=1:22, mart=ensembl)
G_list <- G_list[G_list$hgnc_symbol!="",]
G_list <- G_list[G_list$gene_biotype %in% c("protein_coding","lncRNA"),]
G_list$start <- G_list$start_position
G_list$end <- G_list$end_position
G_list_granges <- makeGRangesFromDataFrame(G_list, keep.extra.columns=T)
#
# #remove genes without imputed expression from gene lists
known_annotations <- known_annotations[known_annotations %in% ctwas_gene_res$genename]
#
known_annotations_positions <- G_list[G_list$hgnc_symbol %in% known_annotations,]
half_window <- 1000000
known_annotations_positions$start <- known_annotations_positions$start_position - half_window
known_annotations_positions$end <- known_annotations_positions$end_position + half_window
known_annotations_positions$start[known_annotations_positions$start<1] <- 1
known_annotations_granges <- makeGRangesFromDataFrame(known_annotations_positions, keep.extra.columns=T)
#
bystanders <- findOverlaps(known_annotations_granges,G_list_granges)
bystanders <- unique(subjectHits(bystanders))
bystanders <- G_list$hgnc_symbol[bystanders]
bystanders <- unique(bystanders[!(bystanders %in% known_annotations)])
unrelated_genes <- bystanders
#
# #save gene lists
save(known_annotations, file=paste0(results_dir, "/known_annotations.Rd"))
save(unrelated_genes, file=paste0(results_dir, "/bystanders.Rd"))
load(paste0(results_dir, "/known_annotations.Rd"))
load(paste0(results_dir, "/bystanders.Rd"))
#remove genes without imputed expression from bystander list
unrelated_genes <- unrelated_genes[unrelated_genes %in% ctwas_gene_res$genename]
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 64#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$genename %in% c(known_annotations, unrelated_genes),]
#assign ctwas and TWAS genes
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
#significance threshold for TWAS
print(sig_thresh)[1] 4.594#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 4#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 25#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
0.04688 0.12500 #specificity / (1 - False Positive Rate)
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
0.9989 0.9807 #precision / PPV / (1 - False Discovery Rate)
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)
precisionctwas TWAS
0.75 0.32 #store sensitivity and specificity calculations for plots
sensitivity_plot <- sensitivity
specificity_plot <- specificity
#precision / PPV by PIP bin
pip_range <- c(0.2, 0.4, 0.6, 0.8, 1)
precision_range <- rep(NA, length(pip_range))
for (i in 1:length(pip_range)){
pip_upper <- pip_range[i]
if (i==1){
pip_lower <- 0
} else {
pip_lower <- pip_range[i-1]
}
#assign ctwas genes in PIP bin
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip_lower & ctwas_gene_res_subset$susie_pip<pip_upper]
precision_range[i] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
}
names(precision_range) <- paste(c(0, pip_range[-length(pip_range)]), pip_range,sep=" - ")
barplot(precision_range, ylim=c(0,1), main="Precision by PIP Range", xlab="PIP Range", ylab="Precision")
abline(h=0.2, lty=2)
abline(h=0.4, lty=2)
abline(h=0.6, lty=2)
abline(h=0.8, lty=2)
barplot(precision_range, add=T, col="darkgrey")
#precision / PPV by PIP threshold
#pip_range <- c(0.2, 0.4, 0.6, 0.8, 1)
pip_range <- c(0.5, 0.8, 1)
precision_range <- rep(NA, length(pip_range))
number_detected <- rep(NA, length(pip_range))
for (i in 1:length(pip_range)){
pip_upper <- pip_range[i]
if (i==1){
pip_lower <- 0
} else {
pip_lower <- pip_range[i-1]
}
#assign ctwas genes using PIP threshold
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip_lower]
number_detected[i] <- length(ctwas_genes)
precision_range[i] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
}
names(precision_range) <- paste0(">= ", c(0, pip_range[-length(pip_range)]))
precision_range <- precision_range*100
precision_range <- c(precision_range, precision["TWAS"]*100)
names(precision_range)[4] <- "TWAS Bonferroni"
number_detected <- c(number_detected, length(twas_genes))
barplot(precision_range, ylim=c(0,100), main="Precision for Distinguishing Silver Standard and Bystander Genes", xlab="PIP Threshold for Detection", ylab="% of Detected Genes in Silver Standard")
abline(h=20, lty=2)
abline(h=40, lty=2)
abline(h=60, lty=2)
abline(h=80, lty=2)
xx <- barplot(precision_range, add=T, col=c(rep("darkgrey",3), "white"))
text(x = xx, y = rep(0, length(number_detected)), label = paste0(number_detected, " detected"), pos = 3, cex=0.8)
#text(x = xx, y = precision_range, label = paste0(round(precision_range,1), "%"), pos = 3, cex=0.8, offset = 1.5)
#false discovery rate by PIP threshold
barplot(100-precision_range, ylim=c(0,100), main="False Discovery Rate for Distinguishing Silver Standard and Bystander Genes", xlab="PIP Threshold for Detection", ylab="% Bystanders in Detected Genes")
abline(h=20, lty=2)
abline(h=40, lty=2)
abline(h=60, lty=2)
abline(h=80, lty=2)
xx <- barplot(100-precision_range, add=T, col=c(rep("darkgrey",3), "white"))
text(x = xx, y = rep(0, length(number_detected)), label = paste0(number_detected, " detected"), pos = 3, cex=0.8)
#text(x = xx, y = precision_range, label = paste0(round(precision_range,1), "%"), pos = 3, cex=0.8, offset = 1.5)
#ROC curves
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip]
sensitivity[index] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
}
plot(1-specificity, sensitivity, type="l", xlim=c(0,1), ylim=c(0,1), main="", xlab="1 - Specificity", ylab="Sensitivity")
title(expression("ROC Curve for cTWAS (black) and TWAS (" * phantom("red") * ")"))
title(expression(phantom("ROC Curve for cTWAS (black) and TWAS (") * "red" * phantom(")")), col.main="red")
sig_thresh_range <- seq(from=0, to=max(abs(ctwas_gene_res_subset$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_subset$genename[abs(ctwas_gene_res_subset$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=1)
abline(a=0,b=1,lty=3)
#add previously computed points from the analysis
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
points(1-specificity_plot["ctwas"], sensitivity_plot["ctwas"], pch=21, bg="black")
points(1-specificity_plot["TWAS"], sensitivity_plot["TWAS"], pch=21, bg="red")
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0 IRanges_2.18.1
[4] S4Vectors_0.22.1 BiocGenerics_0.30.0 biomaRt_2.40.1
[7] readxl_1.3.1 forcats_0.5.1 stringr_1.4.0
[10] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
[13] tidyr_1.1.4 tidyverse_1.3.1 tibble_3.1.6
[16] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0
[19] cowplot_1.0.0 ggplot2_3.3.5 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] ggbeeswarm_0.6.0 colorspace_2.0-2 rjson_0.2.20
[4] ellipsis_0.3.2 rprojroot_2.0.2 XVector_0.24.0
[7] fs_1.5.2 rstudioapi_0.13 farver_2.1.0
[10] ggrepel_0.9.1 bit64_4.0.5 AnnotationDbi_1.46.0
[13] fansi_1.0.2 lubridate_1.8.0 xml2_1.3.3
[16] codetools_0.2-16 doParallel_1.0.17 cachem_1.0.6
[19] knitr_1.36 jsonlite_1.7.2 apcluster_1.4.8
[22] Cairo_1.5-12.2 broom_0.7.10 dbplyr_2.1.1
[25] compiler_3.6.1 httr_1.4.2 backports_1.4.1
[28] assertthat_0.2.1 Matrix_1.2-18 fastmap_1.1.0
[31] cli_3.1.0 later_0.8.0 prettyunits_1.1.1
[34] htmltools_0.5.2 tools_3.6.1 igraph_1.2.10
[37] GenomeInfoDbData_1.2.1 gtable_0.3.0 glue_1.6.2
[40] reshape2_1.4.4 doRNG_1.8.2 Rcpp_1.0.8
[43] Biobase_2.44.0 cellranger_1.1.0 jquerylib_0.1.4
[46] vctrs_0.3.8 svglite_1.2.2 iterators_1.0.14
[49] xfun_0.29 rvest_1.0.2 lifecycle_1.0.1
[52] rngtools_1.5.2 XML_3.99-0.3 zlibbioc_1.30.0
[55] scales_1.1.1 vroom_1.5.7 hms_1.1.1
[58] promises_1.0.1 yaml_2.2.1 curl_4.3.2
[61] memoise_2.0.1 ggrastr_1.0.1 gdtools_0.1.9
[64] stringi_1.7.6 RSQLite_2.2.8 highr_0.9
[67] foreach_1.5.2 rlang_1.0.1 pkgconfig_2.0.3
[70] bitops_1.0-7 evaluate_0.14 lattice_0.20-38
[73] labeling_0.4.2 bit_4.0.4 tidyselect_1.1.1
[76] plyr_1.8.6 magrittr_2.0.2 R6_2.5.1
[79] generics_0.1.1 DBI_1.1.2 pillar_1.6.4
[82] haven_2.4.3 whisker_0.3-2 withr_2.4.3
[85] RCurl_1.98-1.5 modelr_0.1.8 crayon_1.5.0
[88] utf8_1.2.2 tzdb_0.2.0 rmarkdown_2.11
[91] progress_1.2.2 grid_3.6.1 data.table_1.14.2
[94] blob_1.2.2 git2r_0.26.1 reprex_2.0.1
[97] digest_0.6.29 httpuv_1.5.1 munsell_0.5.0
[100] beeswarm_0.2.3 vipor_0.4.5