Test susieI using chr17 to 22
Last updated: 2020-08-19
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Knit directory: causal-TWAS/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 846fb96 | simingz | 2020-08-14 | susieI |
| html | 846fb96 | simingz | 2020-08-14 | susieI |
| Rmd | fd909a1 | simingz | 2020-08-14 | susieI |
Test susieI for ukb chr 17 to chr 22 combined. SNPs are downsampled to 1/10, eQTLs defined by FUSION-TWAS (Adipose/GTEx) lasso effect size > 0 were kept, p= 86k, n = 20k.
susieI is an iterative version of susie, learning parameters (prior weights) iteratively using the top region from mr.ash for genes only.
library(mr.ash.alpha)
library(data.table)
suppressMessages({library(plotly)})
library(tidyr)
library(plyr)
library(stringr)
library(kableExtra)
source("analysis/summarize_twas_plots.R")simdatadir <- "~/causalTWAS/simulations/simulation_ashtest_20200721/"
outputdir <- "~/causalTWAS/simulations/simulation_susieI_20200813/"
susiedir <- "~/causalTWAS/simulations/simulation_susieI_20200813/"
tag <- '20200813-1-1'
Niter <- 10MR.ASH pip distribution
- Use mr.ash for gene only:
rpipf <- paste0(outputdir, tag, "-mr.ash.rPIP.txt")
a <- read.table(rpipf, header = T)
a.c <- a[a$nCausal > 0, ]
ax <- pretty(0:max(a$rPIP), n = 30)
par(mfrow=c(2,1))
h1 <- hist(a$rPIP, breaks = 100, xlab = "PIP", main = "PIP distribution-all", col = "grey"); grid()
h2 <- hist(a.c$rPIP, breaks = h1$breaks, xlab = "PIP", main = "PIP distribution-causal", col = "salmon");grid()
| Version | Author | Date |
|---|---|---|
| 846fb96 | simingz | 2020-08-14 |
- MR.ash2s results for the same simulated data
rpipf <- "~/causalTWAS/simulations/simulation_ashtest_20200721/20200721-1-1-mr.ash2s.lassoes-es.rPIP.txt"
a <- read.table(rpipf, header = T)
a.c <- a[a$nCausal > 0 ,]
ax <- pretty(0:max(a$rPIP), n = 30) # Make a neat vector for the breakpoints
par(mfrow=c(2,1))
h1 <- hist(a$rPIP, breaks = 100, xlab = "PIP", main = "PIP distribution-all", col = "grey"); grid()
h2 <- hist(a.c$rPIP, breaks = h1$breaks, xlab = "PIP", main = "PIP distribution-causal", col = "salmon");grid()
| Version | Author | Date |
|---|---|---|
| 846fb96 | simingz | 2020-08-14 |
susieI genes
Regions: We use regional PIP of both SNP and gene from mr.ash2s > 0.3 , and also require at least one causal gene or SNP is present, also, at least one gene.
Prior change for each iteration:
load(paste0(outputdir, tag, ".susieIres.Rd"))
df <- cbind(prior.gene_rec, prior.SNP_rec)
rownames(df) <- paste("Iteration", 1:Niter)
colnames(df) <- c("Prior.gene", "Prior.SNP")
df Prior.gene Prior.SNP
Iteration 1 4.287261e-02 0.9571274
Iteration 2 6.954929e-03 0.9930451
Iteration 3 1.351683e-03 0.9986483
Iteration 4 2.713631e-04 0.9997286
Iteration 5 5.482880e-05 0.9999452
Iteration 6 1.109243e-05 0.9999889
Iteration 7 2.244698e-06 0.9999978
Iteration 8 4.542679e-07 0.9999995
Iteration 9 9.193288e-08 0.9999999
Iteration 10 1.860504e-08 1.0000000Results for each region:
regionlist <- readRDS(paste0(outputdir, tag, "_regionlist.rds"))
ng <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "gene", , drop = F ])[1]))
ng.c <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "gene" & x$ifcausal == 1, , drop = F])[1]))
ns.c <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "SNP" & x$ifcausal == 1, , drop = F])[1]))
ngdf <- data.frame("ngene" = ng, "ncausalgene" = ng.c, "ncausalSNP" = ns.c)
for (i in Niter){
resf <- paste0(outputdir, tag, ".", i, ".susieIres.Rd")
load(resf)
ngdf[, paste("Prior.gene", i)] <- unlist(gene.rpiplist)
}
ngdf ngene ncausalgene ncausalSNP Prior.gene 10
1 1 1 2 1.032077e-10
2 1 0 1 1.304694e-09
3 7 1 0 8.043942e-09
4 4 0 2 5.338542e-10
5 7 0 1 1.428272e-09
6 10 1 1 7.656938e-07
7 4 0 1 1.332010e-10
8 1 0 1 9.790813e-12
10 6 1 0 6.224828e-09
11 11 2 1 3.330455e-09
12 2 0 2 1.143951e-12
13 12 2 0 7.855562e-09
14 7 0 2 2.513161e-10
18 2 1 0 7.022337e-09
19 2 1 0 1.358569e-09
22 1 0 1 4.398703e-10
24 6 0 1 2.686760e-09
25 4 1 0 2.255832e-09
26 4 0 1 5.025149e-14
27 2 0 1 2.749126e-09
28 4 0 1 1.702461e-09
29 4 0 1 9.997704e-10
30 3 0 1 9.729382e-10
31 4 1 0 1.793812e-09
32 7 1 0 8.046979e-09
33 2 0 2 3.060114e-09
34 9 0 1 6.037589e-14
35 8 0 1 3.389231e-09
36 6 0 1 5.144224e-12
37 11 0 1 5.587082e-09
39 1 0 1 7.198771e-12
43 1 0 2 2.203294e-10
44 3 0 1 3.684302e-10
46 4 0 1 2.522228e-18
47 4 1 0 2.716907e-09
53 5 0 3 3.800506e-10
54 3 0 1 2.317636e-09
55 6 0 1 1.010353e-09
56 2 0 1 9.726544e-10
57 10 0 2 4.418013e-09
58 5 0 1 2.137974e-09
59 14 0 1 1.033099e-08
60 4 0 1 2.284047e-09
61 4 0 1 5.996312e-10
62 3 0 2 2.028332e-10
63 3 0 1 9.262371e-10
64 12 1 0 8.559334e-09susieI shuffle genes
Shuffle genes to different regions.
Prior change for each iteration:
tag <- '20200813-1-1-shuffleg'
load(paste0(outputdir, tag, ".susieIres.Rd"))
df <- cbind(prior.gene_rec, prior.SNP_rec)
rownames(df) <- paste("Iteration", 1:Niter)
colnames(df) <- c("Prior.gene", "Prior.SNP")
df Prior.gene Prior.SNP
Iteration 1 0.06123103 0.9387690
Iteration 2 0.02398287 0.9760171
Iteration 3 0.02227873 0.9777213
Iteration 4 0.02220286 0.9777971
Iteration 5 0.02219948 0.9778005
Iteration 6 0.02219933 0.9778007
Iteration 7 0.02219933 0.9778007
Iteration 8 0.02219933 0.9778007
Iteration 9 0.02219933 0.9778007
Iteration 10 0.02219933 0.9778007Results for each region:
regionlist <- readRDS(paste0(outputdir, tag, "_regionlist.rds"))
ng <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "gene", , drop = F ])[1]))
ng.c <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "gene" & x$ifcausal == 1, , drop = F])[1]))
ns.c <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "SNP" & x$ifcausal == 1, , drop = F])[1]))
ngdf <- data.frame("ngene" = ng, "ncausalgene" = ng.c, "ncausalSNP" = ns.c)
for (i in 1:Niter){
resf <- paste0(outputdir, tag, ".", i, ".susieIres.Rd")
load(resf)
ngdf[, paste("Prior.gene", i)] <- unlist(gene.rpiplist)
}
ngdf ngene ncausalgene ncausalSNP Prior.gene 1 Prior.gene 2 Prior.gene 3
1 7 1 2 3.640772e-06 2.374692e-07 8.946192e-08
2 2 0 1 5.172067e-01 5.068131e-02 1.901263e-02
3 12 1 0 5.134364e-02 3.488501e-03 1.316602e-03
4 12 2 2 3.972331e-02 2.676677e-03 1.009835e-03
5 4 1 1 1.072053e-03 6.997004e-05 2.636060e-05
6 7 0 1 3.428273e-04 2.236700e-05 8.426433e-06
7 1 0 1 1.048032e-04 6.836104e-06 2.575375e-06
8 4 0 1 4.568247e-04 2.980264e-05 1.122767e-05
10 4 0 0 8.644879e-03 5.665639e-04 2.134865e-04
11 3 0 1 4.677641e-02 3.190439e-03 1.204330e-03
12 5 0 2 6.213836e-05 4.053107e-06 1.526931e-06
13 6 0 0 3.045126e-02 2.040523e-03 7.696418e-04
14 1 1 2 4.180617e-02 2.837295e-03 1.070782e-03
18 8 0 0 2.025237e-03 1.322451e-04 4.982321e-05
19 9 0 0 2.453778e-01 1.945630e-02 7.386114e-03
22 3 0 1 1.415094e-02 9.353656e-04 3.525864e-04
24 6 0 1 2.882048e-02 1.904373e-03 7.178662e-04
25 2 1 0 1.978178e-02 1.295218e-03 4.880060e-04
26 6 1 1 8.219039e-06 5.360880e-07 2.019608e-07
27 6 0 1 1.841520e-02 1.211739e-03 4.566756e-04
28 4 0 1 1.818182e-02 1.206410e-03 4.548334e-04
29 4 1 1 7.903698e-03 5.184353e-04 1.953585e-04
30 11 2 1 2.700498e-02 1.800193e-03 6.788365e-04
31 2 0 0 9.661836e-03 6.359350e-04 2.396711e-04
32 4 0 0 6.902375e-03 4.524640e-04 1.704942e-04
33 14 0 2 2.722003e-03 1.778702e-04 6.701451e-05
34 4 0 1 6.669268e-09 4.350017e-10 1.638784e-10
35 1 0 1 4.587156e-03 3.004849e-04 1.132230e-04
36 1 0 1 5.745820e-05 3.747834e-06 1.411926e-06
37 1 0 1 5.494505e-03 3.602283e-04 1.357395e-04
39 4 0 1 6.290717e-05 4.103240e-06 1.545818e-06
43 4 0 2 1.703180e-02 1.127440e-03 4.250160e-04
44 3 0 1 4.462480e-04 2.911312e-05 1.096791e-05
46 3 0 1 5.181058e-11 3.379335e-12 1.273099e-12
47 1 0 0 6.821373e-03 4.476233e-04 1.686778e-04
53 3 0 3 1.090432e-03 7.116088e-05 2.680909e-05
54 2 0 1 1.652893e-02 1.095015e-03 4.128073e-04
55 5 0 1 7.515356e-03 4.922952e-04 1.854973e-04
56 4 1 1 6.822762e-03 4.463276e-04 1.681669e-04
57 11 0 2 2.857178e-02 1.902394e-03 7.173381e-04
58 10 1 1 9.999774e-01 9.996534e-01 9.990802e-01
59 7 1 1 1.749496e-01 1.230379e-02 4.641869e-03
60 2 1 1 2.687543e-01 8.095902e-04 3.051513e-04
61 2 0 1 2.204919e-02 1.467468e-03 5.533302e-04
62 10 0 2 8.621726e-03 5.659487e-04 2.132695e-04
63 7 0 1 1.519468e-01 9.621419e-03 3.600205e-03
64 4 0 0 1.757789e-02 1.151651e-03 4.339492e-04
Prior.gene 4 Prior.gene 5 Prior.gene 6 Prior.gene 7 Prior.gene 8
1 8.296021e-08 8.267128e-08 8.265842e-08 8.265785e-08 8.265782e-08
2 1.762498e-02 1.756333e-02 1.756059e-02 1.756047e-02 1.756046e-02
3 1.221014e-03 1.216766e-03 1.216577e-03 1.216568e-03 1.216568e-03
4 9.365040e-04 9.332450e-04 9.331000e-04 9.330935e-04 9.330932e-04
5 2.444485e-05 2.435972e-05 2.435593e-05 2.435576e-05 2.435575e-05
6 7.814040e-06 7.786826e-06 7.785615e-06 7.785561e-06 7.785559e-06
7 2.388208e-06 2.379890e-06 2.379520e-06 2.379504e-06 2.379503e-06
8 1.041169e-05 1.037543e-05 1.037382e-05 1.037375e-05 1.037374e-05
10 1.979730e-04 1.972836e-04 1.972529e-04 1.972516e-04 1.972515e-04
11 1.116902e-03 1.113016e-03 1.112843e-03 1.112836e-03 1.112835e-03
12 1.415961e-06 1.411029e-06 1.410810e-06 1.410800e-06 1.410800e-06
13 7.137449e-04 7.112607e-04 7.111502e-04 7.111453e-04 7.111450e-04
14 9.930394e-04 9.895843e-04 9.894306e-04 9.894237e-04 9.894234e-04
18 4.620237e-05 4.604147e-05 4.603430e-05 4.603399e-05 4.603397e-05
19 6.851600e-03 6.827838e-03 6.826781e-03 6.826734e-03 6.826732e-03
22 3.269703e-04 3.258319e-04 3.257812e-04 3.257790e-04 3.257789e-04
24 6.657124e-04 6.633947e-04 6.632915e-04 6.632869e-04 6.632867e-04
25 4.525421e-04 4.509661e-04 4.508960e-04 4.508929e-04 4.508927e-04
26 1.872831e-07 1.866309e-07 1.866018e-07 1.866005e-07 1.866005e-07
27 4.234936e-04 4.220190e-04 4.219534e-04 4.219504e-04 4.219503e-04
28 4.217920e-04 4.203236e-04 4.202583e-04 4.202554e-04 4.202552e-04
29 1.811626e-04 1.805318e-04 1.805037e-04 1.805024e-04 1.805024e-04
30 6.295280e-04 6.273367e-04 6.272392e-04 6.272349e-04 6.272347e-04
31 2.222567e-04 2.214828e-04 2.214484e-04 2.214469e-04 2.214468e-04
32 1.581049e-04 1.575543e-04 1.575298e-04 1.575288e-04 1.575287e-04
33 6.214440e-05 6.192798e-05 6.191834e-05 6.191792e-05 6.191790e-05
34 1.519685e-10 1.514392e-10 1.514156e-10 1.514146e-10 1.514145e-10
35 1.049953e-04 1.046297e-04 1.046134e-04 1.046127e-04 1.046127e-04
36 1.309313e-06 1.304753e-06 1.304550e-06 1.304541e-06 1.304541e-06
37 1.258757e-04 1.254374e-04 1.254179e-04 1.254170e-04 1.254170e-04
39 1.433474e-06 1.428482e-06 1.428260e-06 1.428250e-06 1.428250e-06
43 3.941388e-04 3.927667e-04 3.927056e-04 3.927029e-04 3.927027e-04
44 1.017082e-05 1.013539e-05 1.013382e-05 1.013375e-05 1.013374e-05
46 1.180575e-12 1.176464e-12 1.176281e-12 1.176273e-12 1.176272e-12
47 1.564208e-04 1.558761e-04 1.558519e-04 1.558508e-04 1.558508e-04
53 2.486075e-05 2.477417e-05 2.477031e-05 2.477014e-05 2.477014e-05
54 3.828177e-04 3.814850e-04 3.814257e-04 3.814230e-04 3.814229e-04
55 1.720176e-04 1.714186e-04 1.713919e-04 1.713907e-04 1.713907e-04
56 1.559461e-04 1.554031e-04 1.553789e-04 1.553778e-04 1.553778e-04
57 6.652314e-04 6.629158e-04 6.628127e-04 6.628081e-04 6.628079e-04
58 9.990082e-01 9.990047e-01 9.990046e-01 9.990045e-01 9.990045e-01
59 4.304767e-03 4.289786e-03 4.289119e-03 4.289089e-03 4.289088e-03
60 2.829805e-04 2.819952e-04 2.819514e-04 2.819494e-04 2.819493e-04
61 5.131365e-04 5.113503e-04 5.112708e-04 5.112673e-04 5.112671e-04
62 1.977724e-04 1.970837e-04 1.970530e-04 1.970517e-04 1.970516e-04
63 3.337509e-03 3.325839e-03 3.325320e-03 3.325297e-03 3.325296e-03
64 4.024152e-04 4.010138e-04 4.009515e-04 4.009487e-04 4.009486e-04
Prior.gene 9 Prior.gene 10
1 8.265782e-08 8.265782e-08
2 1.756046e-02 1.756046e-02
3 1.216568e-03 1.216568e-03
4 9.330932e-04 9.330932e-04
5 2.435575e-05 2.435575e-05
6 7.785559e-06 7.785559e-06
7 2.379503e-06 2.379503e-06
8 1.037374e-05 1.037374e-05
10 1.972515e-04 1.972515e-04
11 1.112835e-03 1.112835e-03
12 1.410800e-06 1.410800e-06
13 7.111450e-04 7.111450e-04
14 9.894234e-04 9.894234e-04
18 4.603397e-05 4.603397e-05
19 6.826732e-03 6.826732e-03
22 3.257789e-04 3.257789e-04
24 6.632867e-04 6.632867e-04
25 4.508927e-04 4.508927e-04
26 1.866005e-07 1.866005e-07
27 4.219503e-04 4.219503e-04
28 4.202552e-04 4.202552e-04
29 1.805024e-04 1.805024e-04
30 6.272347e-04 6.272347e-04
31 2.214468e-04 2.214468e-04
32 1.575287e-04 1.575287e-04
33 6.191790e-05 6.191790e-05
34 1.514145e-10 1.514145e-10
35 1.046127e-04 1.046127e-04
36 1.304541e-06 1.304541e-06
37 1.254170e-04 1.254170e-04
39 1.428250e-06 1.428250e-06
43 3.927027e-04 3.927027e-04
44 1.013374e-05 1.013374e-05
46 1.176272e-12 1.176272e-12
47 1.558508e-04 1.558508e-04
53 2.477013e-05 2.477013e-05
54 3.814229e-04 3.814229e-04
55 1.713907e-04 1.713907e-04
56 1.553778e-04 1.553778e-04
57 6.628079e-04 6.628079e-04
58 9.990045e-01 9.990045e-01
59 4.289088e-03 4.289088e-03
60 2.819493e-04 2.819493e-04
61 5.112671e-04 5.112671e-04
62 1.970516e-04 1.970516e-04
63 3.325296e-03 3.325296e-03
64 4.009486e-04 4.009486e-04
sessionInfo()R version 3.5.1 (2018-07-02)
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] kableExtra_1.1.0 stringr_1.4.0 plyr_1.8.6
[4] tidyr_0.8.3 plotly_4.9.2.9000 ggplot2_3.3.1
[7] data.table_1.12.7 mr.ash.alpha_0.1-34
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 purrr_0.3.4 lattice_0.20-38
[4] colorspace_1.3-2 vctrs_0.3.1 generics_0.0.2
[7] htmltools_0.3.6 viridisLite_0.3.0 yaml_2.2.0
[10] rlang_0.4.6 later_0.7.5 pillar_1.4.4
[13] glue_1.4.1 withr_2.1.2 lifecycle_0.2.0
[16] munsell_0.5.0 gtable_0.2.0 workflowr_1.6.2
[19] rvest_0.3.2 htmlwidgets_1.3 evaluate_0.12
[22] knitr_1.20 httpuv_1.4.5 Rcpp_1.0.4.6
[25] readr_1.3.1 promises_1.0.1 scales_1.0.0
[28] backports_1.1.2 webshot_0.5.1 jsonlite_1.6.1
[31] fs_1.3.1 hms_0.4.2 digest_0.6.25
[34] stringi_1.3.1 dplyr_1.0.0 grid_3.5.1
[37] rprojroot_1.3-2 tools_3.5.1 magrittr_1.5
[40] lazyeval_0.2.1 tibble_3.0.1 crayon_1.3.4
[43] whisker_0.3-2 pkgconfig_2.0.2 ellipsis_0.3.1
[46] Matrix_1.2-15 xml2_1.2.0 rmarkdown_1.10
[49] httr_1.4.1 rstudioapi_0.11 R6_2.3.0
[52] git2r_0.26.1 compiler_3.5.1