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 | f125882 | simingz | 2020-08-19 | susieI |
| html | f125882 | simingz | 2020-08-19 | susieI |
| Rmd | d299f60 | simingz | 2020-08-19 | susieI |
| html | d299f60 | simingz | 2020-08-19 | susieI |
| 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$breaks, xlab = "PIP", main = "PIP distribution-causal", col = "salmon");grid()
- MR.ash2s results for the same simulated data
rpipf <- "~/causalTWAS/simulations/simulation_ashtest_20200721/20200721-1-1-mr.ash2s.lassoes-es.rPIP.txt"
a <- read.table(rpipf, header = T)
a.c <- a[a$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()
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 0.00853819 0.006018864
Iteration 2 0.01049847 0.005956966
Iteration 3 0.01202068 0.005908900
Iteration 4 0.01318421 0.005872160
Iteration 5 0.01405232 0.005844749
Iteration 6 0.01469379 0.005824494
Iteration 7 0.01516465 0.005809626
Iteration 8 0.01550865 0.005798763
Iteration 9 0.01575915 0.005790854
Iteration 10 0.01594113 0.005785108Results for each region:
regionlist <- readRDS(paste0(outputdir, tag, "_regionlist.rds"))
ng <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "gene", , drop = F ])[1]))
ns <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "SNP", , 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, "nsnp" = ns, "ncausalgene" = ng.c, "ncausalSNP" = ns.c)
cat("causal gene fraction:", sum(ngdf$ncausalgene)/sum(ngdf$ngene) )causal gene fraction: 0.06355932cat("causal snp fraction:", sum(ngdf$ncausalSNP)/sum(ngdf$ns))causal snp fraction: 0.006288467for (i in 1:Niter){
resf <- paste0(outputdir, tag, ".", i, ".susieIres.Rd")
load(resf)
ngdf[, paste("Prior.gene", i)] <- unlist(lapply(gene.rpiplist, "mean"))
ngdf[, paste("Prior.SNP", i)] <- unlist(lapply(snp.rpiplist, "mean"))
}
ngdf ngene nsnp ncausalgene ncausalSNP Prior.gene 1 Prior.SNP 1
1 1 177 1 2 1.121460e-03 0.0056433816
2 1 131 0 1 1.399491e-02 0.0075267564
3 7 116 1 0 1.149406e-02 0.0079270825
4 4 112 0 2 1.446824e-03 0.0088768991
5 7 99 0 1 2.187797e-03 0.0099463174
6 10 154 1 1 8.959135e-02 0.0006758864
7 4 89 0 1 3.618684e-04 0.0112196913
8 1 89 0 1 1.064940e-04 0.0112347585
10 6 145 1 0 1.057093e-02 0.0064591341
11 11 155 2 1 3.212361e-03 0.0062236389
12 2 173 0 2 6.221626e-06 0.0057802749
13 12 166 2 0 6.603292e-03 0.0055467500
14 7 154 0 2 3.895577e-04 0.0064757993
18 2 97 1 0 3.556363e-02 0.0095760076
19 2 158 1 0 7.285274e-03 0.0062368953
22 1 209 0 1 4.761905e-03 0.0047619048
24 6 191 0 1 4.803413e-03 0.0050847096
25 4 178 1 0 5.987876e-03 0.0054834185
26 4 108 0 1 1.366526e-07 0.0092592542
27 2 160 0 1 1.455019e-02 0.0060681226
28 4 216 0 1 4.545455e-03 0.0045454545
29 4 100 0 1 2.693962e-03 0.0098922415
30 3 274 0 1 3.491355e-03 0.0036114085
31 4 205 1 0 4.784689e-03 0.0047846890
32 7 192 1 0 1.149907e-02 0.0047890964
33 2 125 0 2 1.612216e-02 0.0077420454
34 9 133 0 1 7.297093e-08 0.0075187921
35 8 217 0 1 4.444444e-03 0.0044444444
36 6 236 0 1 9.325782e-06 0.0042370510
37 11 181 0 1 5.208333e-03 0.0052083333
39 1 90 0 1 7.830050e-05 0.0111102411
43 1 139 0 2 2.392592e-03 0.0071770317
44 3 159 0 1 1.331606e-03 0.0062641835
46 4 137 0 1 6.858883e-12 0.0072992701
47 4 182 1 0 7.264653e-03 0.0053348428
53 5 169 0 3 8.244458e-04 0.0058927679
54 3 119 0 1 8.196721e-03 0.0081967213
55 6 177 0 1 1.818031e-03 0.0055880894
56 2 138 0 1 5.244884e-03 0.0071703640
57 10 231 0 2 4.586726e-03 0.0041304448
58 5 215 0 1 4.545455e-03 0.0045454545
59 14 163 0 1 7.228010e-03 0.0055141587
60 4 161 0 1 6.060606e-03 0.0060606061
61 4 192 0 1 1.626941e-03 0.0051744387
62 3 123 0 2 7.342575e-04 0.0081121726
63 3 193 0 1 3.341177e-03 0.0051294118
64 12 146 1 0 7.098813e-03 0.0062658510
Prior.gene 2 Prior.SNP 2 Prior.gene 3 Prior.SNP 3 Prior.gene 4
1 1.590171e-03 0.0056407335 1.974861e-03 0.0056385601 2.278944e-03
2 1.973815e-02 0.0074829149 2.440623e-02 0.0074472807 2.806747e-02
3 1.577395e-02 0.0076688135 1.908627e-02 0.0074689318 2.158642e-02
4 2.049515e-03 0.0088553745 2.543290e-03 0.0088377397 2.933042e-03
5 3.085123e-03 0.0098828701 3.814244e-03 0.0098313161 4.385959e-03
6 9.243588e-02 0.0004911768 9.382265e-02 0.0004011269 9.460502e-02
7 5.131257e-04 0.0112128932 6.372756e-04 0.0112073135 7.354159e-04
8 1.510661e-04 0.0112342577 1.876762e-04 0.0112338463 2.166329e-04
10 1.460856e-02 0.0062920597 1.777234e-02 0.0061611446 2.018323e-02
11 4.510406e-03 0.0061315196 5.556875e-03 0.0060572540 6.372283e-03
12 8.825798e-06 0.0057802448 1.096485e-05 0.0057802201 1.265679e-05
13 9.087448e-03 0.0053671724 1.101812e-02 0.0052276057 1.247967e-02
14 5.520417e-04 0.0064684137 6.852536e-04 0.0064623586 7.904603e-04
18 4.903323e-02 0.0092982839 5.954252e-02 0.0090815975 6.752394e-02
19 1.027432e-02 0.0061990592 1.270350e-02 0.0061683101 1.460854e-02
22 6.741666e-03 0.0047524322 8.361958e-03 0.0047446796 9.639830e-03
24 6.779515e-03 0.0050226330 8.743109e-03 0.0049609495 1.001120e-02
25 8.410108e-03 0.0054289863 1.036412e-02 0.0053850760 1.188750e-02
26 1.938515e-07 0.0092592521 2.408346e-07 0.0092592503 2.779971e-07
27 2.040942e-02 0.0059948822 2.512418e-02 0.0059359477 2.879231e-02
28 6.399352e-03 0.0045111231 7.901322e-03 0.0044833089 9.076289e-03
29 3.807033e-03 0.0098477187 4.714982e-03 0.0098114007 5.429140e-03
30 4.931460e-03 0.0035956410 6.105134e-03 0.0035827905 7.027649e-03
31 6.733483e-03 0.0047466637 8.311200e-03 0.0047158790 9.544715e-03
32 1.578110e-02 0.0046329808 1.909515e-02 0.0045121558 2.159664e-02
33 2.257433e-02 0.0076388107 2.775031e-02 0.0075559950 3.176772e-02
34 1.035145e-07 0.0075187900 1.286029e-07 0.0075187883 1.484473e-07
35 6.212308e-03 0.0043792697 7.626106e-03 0.0043271482 9.126148e-03
36 1.322913e-05 0.0042369518 1.643527e-05 0.0042368703 1.897120e-05
37 7.215365e-03 0.0050863590 8.794946e-03 0.0049903624 1.000275e-02
39 1.110724e-04 0.0111098770 1.379902e-04 0.0111095779 1.592808e-04
43 3.391669e-03 0.0071698441 4.211256e-03 0.0071639478 4.858858e-03
44 1.886459e-03 0.0062537146 2.341109e-03 0.0062451363 2.700021e-03
46 9.729817e-12 0.0072992701 1.208800e-11 0.0072992701 1.395327e-11
47 1.022575e-02 0.0052697638 1.262044e-02 0.0052171332 1.449008e-02
53 1.168143e-03 0.0058825993 1.449844e-03 0.0058742650 1.672273e-03
54 1.150918e-02 0.0081132140 1.417995e-02 0.0080458835 1.626128e-02
55 2.570981e-03 0.0055625656 3.185955e-03 0.0055417190 3.670166e-03
56 7.413731e-03 0.0071389314 9.183671e-03 0.0071132801 1.057633e-02
57 6.384775e-03 0.0040526071 7.812061e-03 0.0039908199 8.910723e-03
58 6.387291e-03 0.0045026211 7.874407e-03 0.0044680370 9.034596e-03
59 9.842294e-03 0.0052896190 1.183736e-02 0.0051182632 1.332712e-02
60 8.511041e-03 0.0059997257 1.048730e-02 0.0059506261 1.202771e-02
61 2.305777e-03 0.0051602963 2.862438e-03 0.0051486992 3.302152e-03
62 1.040897e-03 0.0081046936 1.292461e-03 0.0080985579 1.491246e-03
63 4.729615e-03 0.0051078298 5.865695e-03 0.0050901705 6.761535e-03
64 9.723897e-03 0.0060500906 1.174876e-02 0.0058836639 1.327322e-02
Prior.SNP 4 Prior.gene 5 Prior.SNP 5 Prior.gene 6 Prior.SNP 6
1 0.0056368421 2.514613e-03 0.0056355107 2.692307e-03 0.0056345067
2 0.0074193323 3.088770e-02 0.0073978038 3.300426e-02 0.0073816469
3 0.0073180611 2.345660e-02 0.0072052054 2.482953e-02 0.0071223561
4 0.0088238199 3.234767e-03 0.0088130440 3.462071e-03 0.0088049260
5 0.0097908918 4.826263e-03 0.0097597592 5.156655e-03 0.0097363981
6 0.0003503237 9.508746e-02 0.0003189958 9.539789e-02 0.0002988382
7 0.0112029027 8.114787e-04 0.0111994841 8.688311e-04 0.0111969065
8 0.0112335210 2.390856e-04 0.0112332687 2.560211e-04 0.0112330784
10 0.0060613837 2.199966e-02 0.0059862209 2.334030e-02 0.0059307464
11 0.0059993864 6.997198e-03 0.0059550375 7.464373e-03 0.0059218832
12 0.0057802005 1.396872e-05 0.0057801853 1.495829e-05 0.0057801739
13 0.0051219513 1.357519e-02 0.0050427573 1.438057e-02 0.0049845368
14 0.0064575765 8.719408e-04 0.0064538728 9.333443e-04 0.0064510818
18 0.0089170322 7.352182e-02 0.0087933645 7.793997e-02 0.0087022687
19 0.0061441957 1.607586e-02 0.0061256220 1.717700e-02 0.0061116836
22 0.0047385654 1.062844e-02 0.0047338352 1.137283e-02 0.0047302736
24 0.0049211143 1.098055e-02 0.0048906634 1.170382e-02 0.0048679428
25 0.0053508428 1.305549e-02 0.0053245956 1.392896e-02 0.0053049671
26 0.0092592490 3.068132e-07 0.0092592479 3.285489e-07 0.0092592471
27 0.0058900961 3.160013e-02 0.0058549984 3.369727e-02 0.0058287842
28 0.0044615502 9.979546e-03 0.0044448232 1.065639e-02 0.0044322890
29 0.0097828344 5.980485e-03 0.0097607806 6.394966e-03 0.0097442013
30 0.0035726900 7.739456e-03 0.0035648965 8.274345e-03 0.0035590400
31 0.0046918104 1.049256e-02 0.0046733159 1.120258e-02 0.0046594619
32 0.0044209558 2.346784e-02 0.0043527350 2.484153e-02 0.0043026527
33 0.0074917165 3.483736e-02 0.0074426022 3.712698e-02 0.0074059684
34 0.0075187869 1.638347e-07 0.0075187859 1.754413e-07 0.0075187851
35 0.0042718471 1.000810e-02 0.0042393328 1.066320e-02 0.0042151814
36 0.0042368058 2.093756e-05 0.0042367558 2.242074e-05 0.0042367181
37 0.0049169602 1.091510e-02 0.0048615132 1.158978e-02 0.0048205106
39 0.0111093413 1.757893e-04 0.0111091579 1.882412e-04 0.0111090195
43 0.0071592888 5.360605e-03 0.0071556791 5.738831e-03 0.0071529581
44 0.0062383644 2.977897e-03 0.0062331214 3.187249e-03 0.0062291714
46 0.0072992701 1.539961e-11 0.0072992701 1.649057e-11 0.0072992701
47 0.0051760423 1.592473e-02 0.0051445114 1.699810e-02 0.0051209208
53 0.0058676842 1.844509e-03 0.0058625885 1.974289e-03 0.0058587489
54 0.0079934131 1.785657e-02 0.0079531958 1.904929e-02 0.0079231271
55 0.0055253051 4.044287e-03 0.0055126231 4.325710e-03 0.0055030833
56 0.0070930966 1.165179e-02 0.0070775103 1.246045e-02 0.0070657906
57 0.0039432587 9.744873e-03 0.0039071484 1.036408e-02 0.0038803429
58 0.0044410559 9.924616e-03 0.0044203578 1.059048e-02 0.0044048726
59 0.0049903083 1.443252e-02 0.0048953666 1.523914e-02 0.0048260861
60 0.0059123550 1.320859e-02 0.0058830165 1.409158e-02 0.0058610787
61 0.0051395385 3.642754e-03 0.0051324426 3.899461e-03 0.0051270946
62 0.0080937094 1.645266e-03 0.0080899529 1.761373e-03 0.0080871210
63 0.0050762456 7.454504e-03 0.0050654740 7.976242e-03 0.0050573641
64 0.0057583658 1.441136e-02 0.0056648195 1.524570e-02 0.0055962436
Prior.gene 7 Prior.SNP 7 Prior.gene 8 Prior.SNP 8 Prior.gene 9
1 2.824646e-03 0.0056337591 2.922352e-03 0.0056332071 2.994041e-03
2 3.457510e-02 0.0073696557 3.573186e-02 0.0073608255 3.657898e-02
3 2.583198e-02 0.0070618630 2.656140e-02 0.0070178466 2.709091e-02
4 3.631250e-03 0.0087988839 3.756095e-03 0.0087944252 3.847665e-03
5 5.401833e-03 0.0097190623 5.582366e-03 0.0097062974 5.714567e-03
6 9.560480e-02 0.0002854026 9.574604e-02 0.0002762315 9.584404e-02
7 9.115459e-04 0.0111949867 9.430827e-04 0.0111935693 9.662223e-04
8 2.686375e-04 0.0112329367 2.779541e-04 0.0112328320 2.847909e-04
10 2.432302e-02 0.0058900817 2.504014e-02 0.0058604081 2.556181e-02
11 7.810089e-03 0.0058973485 8.064127e-03 0.0058793200 8.249873e-03
12 1.569550e-05 0.0057801654 1.623991e-05 0.0057801591 1.663941e-05
13 1.496922e-02 0.0049419843 1.539783e-02 0.0049110004 1.570913e-02
14 9.790571e-04 0.0064490039 1.012797e-03 0.0064474703 1.037548e-03
18 8.117393e-02 0.0086355890 8.353130e-02 0.0085869834 8.524487e-02
19 1.799418e-02 0.0061013395 1.859593e-02 0.0060937224 1.903660e-02
22 1.192665e-02 0.0047276237 1.233524e-02 0.0047256687 1.263485e-02
24 1.223829e-02 0.0048511532 1.263061e-02 0.0048388289 1.291725e-02
25 1.457551e-02 0.0052904379 1.505070e-02 0.0052797596 1.539819e-02
26 3.447414e-07 0.0092592465 3.566990e-07 0.0092592460 3.654740e-07
27 3.524811e-02 0.0058093986 3.638712e-02 0.0057951610 3.721961e-02
28 1.115815e-02 0.0044229971 1.152734e-02 0.0044161604 1.179753e-02
29 6.702972e-03 0.0097318811 6.929999e-03 0.0097228000 7.096373e-03
30 8.671702e-03 0.0035546894 8.964521e-03 0.0035514833 9.179073e-03
31 1.172880e-02 0.0046491942 1.211590e-02 0.0046416410 1.239918e-02
32 2.584454e-02 0.0042660845 2.657436e-02 0.0042394765 2.710417e-02
33 3.881848e-02 0.0073789043 4.005989e-02 0.0073590418 4.096675e-02
34 1.840879e-07 0.0075187845 1.904731e-07 0.0075187841 1.951589e-07
35 1.114598e-02 0.0041973832 1.149972e-02 0.0041843423 1.175783e-02
36 2.352567e-05 0.0042366900 2.434161e-05 0.0042366693 2.494038e-05
37 1.208505e-02 0.0047904112 1.244684e-02 0.0047684242 1.271023e-02
39 1.975174e-04 0.0111089165 2.043675e-04 0.0111088404 2.093943e-04
43 6.020469e-03 0.0071509319 6.228376e-03 0.0071494361 6.380908e-03
44 3.343074e-03 0.0062262313 3.458069e-03 0.0062240616 3.542417e-03
46 1.730331e-11 0.0072992701 1.790348e-11 0.0072992701 1.834392e-11
47 1.779283e-02 0.0051034543 1.837700e-02 0.0050906153 1.880423e-02
53 2.070896e-03 0.0058558907 2.142194e-03 0.0058537812 2.194494e-03
54 1.993200e-02 0.0079008740 2.058067e-02 0.0078845210 2.105498e-02
55 4.534934e-03 0.0054959909 4.689203e-03 0.0054907615 4.802286e-03
56 1.306147e-02 0.0070570802 1.350452e-02 0.0070506591 1.382924e-02
57 1.081991e-02 0.0038606100 1.115357e-02 0.0038461657 1.139685e-02
58 1.108351e-02 0.0043934069 1.144594e-02 0.0043849781 1.171103e-02
59 1.582551e-02 0.0047757229 1.625080e-02 0.0047391948 1.655882e-02
60 1.474513e-02 0.0058448416 1.522542e-02 0.0058329088 1.557664e-02
61 4.090589e-03 0.0051231127 4.231667e-03 0.0051201736 4.335163e-03
62 1.847831e-03 0.0080850123 1.911656e-03 0.0080834555 1.958482e-03
63 8.364390e-03 0.0050513307 8.650731e-03 0.0050468798 8.860702e-03
64 1.585427e-02 0.0055462247 1.629673e-02 0.0055098575 1.661776e-02
Prior.SNP 9 Prior.gene 10 Prior.SNP 10
1 0.0056328020 3.046407e-03 0.0056325062
2 0.0073543589 3.719691e-02 0.0073496419
3 0.0069858931 2.747471e-02 0.0069627331
4 0.0087911548 3.914535e-03 0.0087887666
5 0.0096969498 5.810995e-03 0.0096901317
6 0.0002698676 9.591283e-02 0.0002654009
7 0.0111925293 9.831248e-04 0.0111917697
8 0.0112327552 2.897855e-04 0.0112326990
10 0.0058388216 2.594050e-02 0.0058231518
11 0.0058661380 8.385207e-03 0.0058565337
12 0.0057801545 1.693126e-05 0.0057801511
13 0.0048884966 1.593484e-02 0.0048721800
14 0.0064463452 1.055624e-03 0.0064455236
18 0.0085516522 8.648805e-02 0.0085260196
19 0.0060881443 1.935804e-02 0.0060840755
22 0.0047242351 1.285362e-02 0.0047231884
24 0.0048298246 1.312598e-02 0.0048232677
25 0.0052719507 1.565141e-02 0.0052662605
26 0.0092592457 3.718844e-07 0.0092592455
27 0.0057847548 3.782600e-02 0.0057771750
28 0.0044111568 1.199454e-02 0.0044075086
29 0.0097161451 7.217793e-03 0.0097112883
30 0.0035491342 9.335635e-03 0.0035474200
31 0.0046361137 1.260569e-02 0.0046320841
32 0.0042201605 2.748818e-02 0.0042061602
33 0.0073445319 4.162706e-02 0.0073339671
34 0.0075187838 1.985820e-07 0.0075187836
35 0.0041748265 1.194562e-02 0.0041679034
36 0.0042366541 2.537780e-05 0.0042366429
37 0.0047524172 1.290153e-02 0.0047407912
39 0.0111087845 2.130666e-04 0.0111087437
43 0.0071483388 6.492317e-03 0.0071475373
44 0.0062224701 3.604014e-03 0.0062213079
46 0.0072992701 1.866567e-11 0.0072992701
47 0.0050812256 1.911556e-02 0.0050743833
53 0.0058522339 2.232689e-03 0.0058511039
54 0.0078725635 2.140058e-02 0.0078638509
55 0.0054869282 4.884829e-03 0.0054841301
56 0.0070459531 1.406623e-02 0.0070425184
57 0.0038356340 1.157374e-02 0.0038279765
58 0.0043788132 1.190422e-02 0.0043743205
59 0.0047127393 1.678170e-02 0.0046935967
60 0.0058241829 1.583255e-02 0.0058178248
61 0.0051180174 4.410752e-03 0.0051164427
62 0.0080823134 1.992684e-03 0.0080814792
63 0.0050436160 9.014011e-03 0.0050412330
64 0.0054834715 1.685035e-02 0.0054643546susieI 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 0.01183381 0.005914801
Iteration 2 0.01695869 0.005752977
Iteration 3 0.02082324 0.005630949
Iteration 4 0.02350238 0.005546352
Iteration 5 0.02527726 0.005490309
Iteration 6 0.02642318 0.005454125
Iteration 7 0.02715195 0.005431113
Iteration 8 0.02761127 0.005416610
Iteration 9 0.02789918 0.005407519
Iteration 10 0.02807905 0.005401839Results for each region:
regionlist <- readRDS(paste0(outputdir, tag, "_regionlist.rds"))
ng <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "gene", , drop = F ])[1]))
ns <- unlist(lapply(lapply(regionlist, '[[', "anno"), function(x) dim(x[x$type == "SNP", , 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, "nsnp" = ns, "ncausalgene" = ng.c, "ncausalSNP" = ns.c)
cat("causal gene fraction:", sum(ngdf$ncausalgene)/sum(ngdf$ngene) )causal gene fraction: 0.06355932cat("causal snp fraction:", sum(ngdf$ncausalSNP)/sum(ngdf$ns))causal snp fraction: 0.006288467for (i in 1:Niter){
resf <- paste0(outputdir, tag, ".", i, ".susieIres.Rd")
load(resf)
ngdf[, paste("Prior.gene", i)] <- unlist(lapply(gene.rpiplist, "mean"))
ngdf[, paste("Prior.SNP", i)] <- unlist(lapply(snp.rpiplist, "mean"))
}
ngdf ngene nsnp ncausalgene ncausalSNP Prior.gene 1 Prior.SNP 1 Prior.gene 2
1 2 177 1 2 1.891026e-05 0.005649504 3.783279e-05
2 5 131 0 1 2.605477e-03 0.007534142 5.169126e-03
3 4 116 1 0 2.095724e-03 0.008548423 4.175079e-03
4 3 112 0 2 1.257645e-03 0.008894885 2.511193e-03
5 1 99 0 1 7.138795e-04 0.010093799 1.427526e-03
6 6 154 0 1 4.163848e-07 0.006493490 8.330648e-07
7 4 89 1 1 2.711293e-04 0.011223769 5.420712e-04
8 1 89 0 1 1.150749e-04 0.011234662 2.302184e-04
10 3 145 0 0 2.246657e-03 0.006850069 4.476467e-03
11 9 155 0 1 3.115190e-02 0.004642793 4.984253e-02
12 3 173 0 2 1.724765e-05 0.005780048 3.450646e-05
13 8 166 0 0 5.296697e-03 0.005768834 1.018284e-02
14 4 154 0 2 2.374625e-06 0.006493445 4.750917e-06
18 2 97 0 0 1.114855e-03 0.010286292 2.226895e-03
19 2 158 0 0 2.032025e-03 0.006303392 4.056005e-03
22 1 209 0 1 4.761905e-03 0.004761905 9.482014e-03
24 4 191 1 1 6.915215e-03 0.005090781 1.375330e-02
25 11 178 2 0 5.117038e-03 0.005301756 9.715299e-03
26 4 108 0 1 1.804402e-07 0.009259253 3.610087e-07
27 7 160 0 1 8.837412e-02 0.002383632 1.106416e-01
28 4 216 0 1 4.580140e-03 0.004544812 1.041281e-02
29 12 100 1 1 1.396670e-03 0.009832400 2.762669e-03
30 1 274 0 1 8.912668e-03 0.003617107 1.769524e-02
31 4 205 0 0 4.784689e-03 0.004784689 9.392887e-03
32 4 192 0 0 1.725594e-03 0.005172383 3.434053e-03
33 2 125 0 2 1.612216e-02 0.007742045 3.127461e-02
34 10 133 1 1 5.237902e-03 0.007124970 9.960700e-03
35 6 217 0 1 4.484305e-03 0.004484305 8.736569e-03
36 6 236 1 1 7.692980e-05 0.004235332 1.538631e-04
37 1 181 1 1 9.470459e-01 0.000292564 9.729029e-01
39 1 90 0 1 1.020257e-05 0.011110998 2.041231e-05
43 12 139 2 2 2.915523e-03 0.006942545 5.662184e-03
44 7 159 0 1 2.327776e-03 0.006186827 4.588031e-03
46 4 137 0 1 7.516235e-12 0.007299270 1.503782e-11
47 14 182 0 0 8.471707e-03 0.004842836 1.577998e-02
53 3 169 0 3 3.533457e-03 0.005854436 7.007733e-03
54 7 119 1 1 9.502767e-03 0.007844375 1.796294e-02
55 5 177 0 1 2.551812e-03 0.005577632 5.053440e-03
56 2 138 1 1 8.133078e-03 0.007128506 1.603686e-02
57 7 231 1 2 2.015257e-02 0.003718320 3.591972e-02
58 4 215 0 1 4.566210e-03 0.004566210 8.971687e-03
59 6 163 0 1 2.426077e-03 0.006045666 4.814689e-03
60 2 161 0 1 2.698067e-03 0.006177664 5.376515e-03
61 10 192 0 1 5.509788e-03 0.004921365 1.046471e-02
62 3 123 0 2 5.992823e-04 0.008115465 1.197522e-03
63 4 193 0 1 3.213477e-03 0.005114747 6.390337e-03
64 11 146 0 0 6.211912e-03 0.006381294 1.165127e-02
Prior.SNP 2 Prior.gene 3 Prior.SNP 3 Prior.gene 4 Prior.SNP 4
1 0.0056492900 5.574048e-05 0.0056490877 6.992417e-05 5.648927e-03
2 0.0074362929 7.556183e-03 0.0073451839 9.420461e-03 7.274028e-03
3 0.0084767214 6.126882e-03 0.0084094179 7.661918e-03 8.356486e-03
4 0.0088613073 3.693021e-03 0.0088296512 4.626006e-03 8.804661e-03
5 0.0100865906 2.102259e-03 0.0100797752 2.636236e-03 1.007438e-02
6 0.0064934740 1.227421e-06 0.0064934587 1.539785e-06 6.493447e-03
7 0.0112115923 7.981483e-04 0.0112000832 1.000743e-03 1.119098e-02
8 0.0112333683 3.391812e-04 0.0112321440 4.254808e-04 1.123117e-02
10 0.0068039352 6.570046e-03 0.0067606197 8.216904e-03 6.726547e-03
11 0.0035575304 6.113702e-02 0.0029017212 6.754790e-02 2.529477e-03
12 0.0057797484 5.083961e-05 0.0057794652 6.377618e-05 5.779241e-03
13 0.0055333573 1.446625e-02 0.0053269279 1.764627e-02 5.173674e-03
14 0.0064933831 6.999883e-06 0.0064933247 8.781245e-06 6.493278e-03
18 0.0102633630 3.276048e-03 0.0102417310 4.104792e-03 1.022464e-02
19 0.0062777721 5.962880e-03 0.0062536344 7.467348e-03 6.234591e-03
22 0.0047393205 1.390820e-02 0.0047181426 1.738614e-02 4.701502e-03
24 0.0049475749 2.002025e-02 0.0048163298 2.481594e-02 4.715896e-03
25 0.0050175939 1.365096e-02 0.0047743789 1.651591e-02 4.597332e-03
26 0.0092592459 5.319034e-07 0.0092592396 6.672668e-07 9.259235e-03
27 0.0014094295 1.196544e-01 0.0010151178 1.238669e-01 8.308215e-04
28 0.0044367998 1.630170e-02 0.0043277462 2.113047e-02 4.238325e-03
29 0.0096684797 4.027235e-03 0.0095167318 5.009942e-03 9.398807e-03
30 0.0035850539 2.588343e-02 0.0035551700 3.228486e-02 3.531807e-03
31 0.0046947729 1.359747e-02 0.0046127323 1.682499e-02 4.549756e-03
32 0.0051367906 5.034307e-03 0.0051034519 6.290485e-03 5.077282e-03
33 0.0074996062 4.478833e-02 0.0072833867 5.496529e-02 7.120555e-03
34 0.0067698722 1.401870e-02 0.0064647597 1.698362e-02 6.241833e-03
35 0.0043667308 1.256062e-02 0.0042609968 1.546063e-02 4.180812e-03
36 0.0042333764 2.266276e-04 0.0042315264 2.842309e-04 4.230062e-03
37 0.0001497077 9.814677e-01 0.0001023884 9.851785e-01 8.188692e-05
39 0.0111108843 3.007498e-05 0.0111107769 3.772859e-05 1.111069e-02
43 0.0067054230 8.116482e-03 0.0064935411 9.967310e-03 6.333757e-03
44 0.0060873194 6.666070e-03 0.0059958334 8.271435e-03 5.925157e-03
46 0.0072992701 2.215645e-11 0.0072992701 2.779502e-11 7.299270e-03
47 0.0042806611 2.147816e-02 0.0038423393 2.528475e-02 3.549525e-03
53 0.0057927621 1.024036e-02 0.0057353783 1.276337e-02 5.690591e-03
54 0.0073467177 2.514138e-02 0.0069244567 3.032838e-02 6.619339e-03
55 0.0055069650 7.374428e-03 0.0054414003 9.181464e-03 5.390354e-03
56 0.0070139586 2.330899e-02 0.0069085653 2.893050e-02 6.827094e-03
57 0.0032405278 4.772986e-02 0.0028826449 5.545616e-02 2.648515e-03
58 0.0044842477 1.299791e-02 0.0044093412 1.609281e-02 4.351762e-03
59 0.0059577415 7.040301e-03 0.0058758172 8.779657e-03 5.811792e-03
60 0.0061443911 7.891838e-03 0.0061131449 9.870775e-03 6.088562e-03
61 0.0046632965 1.471046e-02 0.0044421635 1.780485e-02 4.280997e-03
62 0.0081008734 1.762364e-03 0.0080870968 2.208841e-03 8.076207e-03
63 0.0050489049 9.362637e-03 0.0049873029 1.169419e-02 4.938981e-03
64 0.0059714800 1.620422e-02 0.0056284489 1.946130e-02 5.383052e-03
Prior.gene 5 Prior.SNP 5 Prior.gene 6 Prior.SNP 6 Prior.gene 7
1 8.012310e-05 5.648812e-03 8.705256e-05 0.0056487339 9.160202e-05
2 1.074685e-02 7.223403e-03 1.164137e-02 0.0071892606 1.222576e-02
3 8.759840e-03 8.318626e-03 9.503034e-03 0.0082929988 9.989760e-03
4 5.295208e-03 8.786736e-03 5.749090e-03 0.0087745779 6.046733e-03
5 3.019957e-03 1.007051e-02 3.280554e-03 0.0100678732 3.451595e-03
6 1.764402e-06 6.493438e-03 1.917017e-06 0.0064934318 2.017217e-06
7 1.146295e-03 1.118444e-02 1.245128e-03 0.0111799943 1.309989e-03
8 4.875333e-04 1.123048e-02 5.296928e-04 0.0112300035 5.573716e-04
10 9.394954e-03 6.702173e-03 1.019245e-02 0.0066856735 1.071476e-02
11 7.123846e-02 2.315186e-03 7.341205e-02 0.0021889777 7.471582e-02
12 7.307833e-05 5.779080e-03 7.939849e-05 0.0057789700 8.354791e-05
13 1.982582e-02 5.068635e-03 2.125849e-02 0.0049995907 2.217873e-02
14 1.006220e-05 6.493245e-03 1.093253e-05 0.0064932225 1.150395e-05
18 4.699494e-03 1.021238e-02 5.102974e-03 0.0102040624 5.367620e-03
19 8.545967e-03 6.220937e-03 9.277302e-03 0.0062116797 9.756787e-03
22 1.987197e-02 4.689608e-03 2.155380e-02 0.0046815608 2.265488e-02
24 2.816818e-02 4.645692e-03 3.039951e-02 0.0045989632 3.184413e-02
25 1.845182e-02 4.477696e-03 1.971221e-02 0.0043998073 2.051674e-02
26 7.646051e-07 9.259231e-03 8.307417e-07 0.0092592285 8.741636e-07
27 1.260606e-01 7.348504e-04 1.272831e-01 0.0006813642 1.279935e-01
28 2.463901e-02 4.173352e-03 2.702548e-02 0.0041291577 2.858982e-02
29 5.706492e-03 9.315221e-03 6.175026e-03 0.0092589969 6.480582e-03
30 3.684253e-02 3.515173e-03 3.991770e-02 0.0035039500 4.192729e-02
31 1.909192e-02 4.505523e-03 2.060721e-02 0.0044759569 2.159130e-02
32 7.187647e-03 5.058591e-03 7.794317e-03 0.0050459517 8.191353e-03
33 6.201346e-02 7.007785e-03 6.667941e-02 0.0069331294 6.969046e-02
34 1.899296e-02 6.090755e-03 2.030387e-02 0.0059921904 2.114183e-02
35 1.747944e-02 4.124992e-03 1.882064e-02 0.0040879085 1.968817e-02
36 3.256350e-04 4.229009e-03 3.537585e-04 0.0042282943 3.722193e-04
37 9.870440e-01 7.158036e-05 9.880646e-01 0.0000659412 9.886516e-01
39 4.323218e-05 1.111063e-02 4.697158e-05 0.0111105892 4.942668e-05
43 1.125024e-02 6.223001e-03 1.209999e-02 0.0061496414 1.264852e-02
44 9.404322e-03 5.875281e-03 1.016402e-02 0.0058418354 1.065846e-02
46 3.184966e-11 7.299270e-03 3.460458e-11 0.0072992701 3.641333e-11
47 2.770083e-02 3.363672e-03 2.920934e-02 0.0032476333 3.014662e-02
53 1.455750e-02 5.658743e-03 1.576704e-02 0.0056372715 1.655702e-02
54 3.381399e-02 6.414303e-03 3.607456e-02 0.0062813287 3.751381e-02
55 1.046408e-02 5.354122e-03 1.132766e-02 0.0053297272 1.189120e-02
56 3.289931e-02 6.769575e-03 3.556157e-02 0.0067309918 3.729458e-02
57 6.030388e-02 2.501614e-03 6.331052e-02 0.0024105038 6.517126e-02
58 1.826882e-02 4.311278e-03 1.972435e-02 0.0042841981 2.067008e-02
59 1.001785e-02 5.766214e-03 1.085325e-02 0.0057354633 1.139917e-02
60 1.128654e-02 6.070975e-03 1.224502e-02 0.0060590680 1.287281e-02
61 1.989790e-02 4.171984e-03 2.126158e-02 0.0041009595 2.213247e-02
62 2.529391e-03 8.068389e-03 2.746948e-03 0.0080630826 2.889680e-03
63 1.335879e-02 4.904481e-03 1.448423e-02 0.0048811559 1.522074e-02
64 2.163572e-02 5.219226e-03 2.304016e-02 0.0051134128 2.393207e-02
Prior.SNP 7 Prior.gene 8 Prior.SNP 8 Prior.gene 9 Prior.SNP 9
1 5.648682e-03 9.452683e-05 0.0056486494 9.638298e-05 5.648628e-03
2 7.166956e-03 1.260026e-02 0.0071526620 1.283743e-02 7.143610e-03
3 8.276215e-03 1.030217e-02 0.0082654424 1.050023e-02 8.258613e-03
4 8.766605e-03 6.237941e-03 0.0087614837 6.359226e-03 8.758235e-03
5 1.006615e-02 3.561535e-03 0.0100650350 3.631297e-03 1.006433e-02
6 6.493428e-03 2.081636e-06 0.0064934254 2.122518e-06 6.493424e-03
7 1.117708e-02 1.351676e-03 0.0111752056 1.378128e-03 1.117402e-02
8 1.122969e-02 5.751659e-04 0.0112294925 5.864585e-04 1.122937e-02
10 6.674867e-03 1.105002e-02 0.0066679306 1.126257e-02 6.663533e-03
11 2.113275e-03 7.550782e-02 0.0020672878 7.599295e-02 2.039119e-03
12 5.778898e-03 8.621555e-05 0.0057788518 8.790848e-05 5.778822e-03
13 4.955242e-03 2.276205e-02 0.0049271302 2.312892e-02 4.909450e-03
14 6.493208e-03 1.187132e-05 0.0064931981 1.210446e-05 6.493192e-03
18 1.019861e-02 5.537653e-03 0.0101950999 5.645516e-03 1.019288e-02
19 6.205610e-03 1.006477e-02 0.0062017118 1.026011e-02 6.199239e-03
22 4.676292e-03 2.336145e-02 0.0046729117 2.380933e-02 4.670769e-03
24 4.568709e-03 3.276438e-02 0.0045494370 3.334497e-02 4.537278e-03
25 4.350089e-03 2.102468e-02 0.0043186997 2.134333e-02 4.299007e-03
26 9.259227e-03 9.020796e-07 0.0092592258 9.197959e-07 9.259225e-03
27 6.502862e-04 1.284169e-01 0.0006317599 1.286734e-01 6.205401e-04
28 4.100188e-03 2.959362e-02 0.0040815997 3.022966e-02 4.069821e-03
29 9.222330e-03 6.676169e-03 0.0091988597 6.799949e-03 9.184006e-03
30 3.496616e-03 4.321533e-02 0.0034919149 4.403116e-02 3.488937e-03
31 4.456755e-03 2.221953e-02 0.0044444970 2.261643e-02 4.436753e-03
32 5.037680e-03 8.446077e-03 0.0050323734 8.607517e-03 5.029010e-03
33 6.884953e-03 7.160480e-02 0.0068543232 7.281109e-02 6.835023e-03
34 5.929186e-03 2.167135e-02 0.0058893722 2.200375e-02 5.864380e-03
35 4.063922e-03 2.024055e-02 0.0040486484 2.058895e-02 4.039015e-03
36 4.227825e-03 3.840862e-04 0.0042275232 3.916167e-04 4.227332e-03
37 6.269822e-05 9.889994e-01 0.0000607766 9.892093e-01 5.961701e-05
39 1.111056e-02 5.100506e-05 0.0111105444 5.200675e-05 1.111053e-02
43 6.102286e-03 1.299733e-02 0.0060721729 1.321715e-02 6.053196e-03
44 5.820068e-03 1.097453e-02 0.0058061526 1.117439e-02 5.797354e-03
46 7.299270e-03 3.757618e-11 0.0072992701 3.831415e-11 7.299270e-03
47 3.175535e-03 3.072833e-02 0.0031307880 3.108937e-02 3.103016e-03
53 5.623248e-03 1.706318e-02 0.0056142630 1.738371e-02 5.608573e-03
54 6.196667e-03 3.842095e-02 0.0061433054 3.898944e-02 6.109865e-03
55 5.313808e-03 1.225207e-02 0.0053036140 1.248051e-02 5.297161e-03
56 6.705876e-03 3.840256e-02 0.0066898180 3.910322e-02 6.679663e-03
57 2.354118e-03 6.632336e-02 0.0023192056 6.703737e-02 2.297569e-03
58 4.266603e-03 2.127400e-02 0.0042553674 2.165562e-02 4.248268e-03
59 5.715368e-03 1.174907e-02 0.0057024881 1.197070e-02 5.694330e-03
60 6.051269e-03 1.327578e-02 0.0060462636 1.353126e-02 6.043090e-03
61 4.055600e-03 2.268249e-02 0.0040269538 2.302761e-02 4.008978e-03
62 8.059601e-03 2.981398e-03 0.0080573643 3.039586e-03 8.055945e-03
63 4.865891e-03 1.569325e-02 0.0048560985 1.599272e-02 4.849892e-03
64 5.046214e-03 2.449336e-02 0.0050039249 2.484478e-02 4.977448e-03
Prior.gene 10 Prior.SNP 10
1 9.755153e-05 5.648615e-03
2 1.298655e-02 7.137918e-03
3 1.062484e-02 8.254316e-03
4 6.435558e-03 8.756190e-03
5 3.675213e-03 1.006389e-02
6 2.148255e-06 6.493423e-03
7 1.394779e-03 1.117327e-02
8 5.935678e-04 1.122929e-02
10 1.139629e-02 6.660766e-03
11 7.629170e-02 2.021772e-03
12 8.897428e-05 5.778804e-03
13 2.335858e-02 4.898382e-03
14 1.225123e-05 6.493188e-03
18 5.713405e-03 1.019148e-02
19 1.038304e-02 6.197683e-03
22 2.409109e-02 4.669421e-03
24 3.370911e-02 4.529652e-03
25 2.154250e-02 4.286700e-03
26 9.309493e-07 9.259225e-03
27 1.288302e-01 6.136786e-04
28 3.062965e-02 4.062414e-03
29 6.877738e-03 9.174671e-03
30 4.454415e-02 3.487065e-03
31 2.286559e-02 4.431891e-03
32 8.709068e-03 5.026894e-03
33 7.356713e-02 6.822926e-03
34 2.221157e-02 5.848754e-03
35 2.080745e-02 4.032974e-03
36 3.963573e-04 4.227211e-03
37 9.893374e-01 5.890941e-05
39 5.263737e-05 1.111053e-02
43 1.335493e-02 6.041301e-03
44 1.129993e-02 5.791827e-03
46 3.877875e-11 7.299270e-03
47 3.131351e-02 3.085774e-03
53 1.758523e-02 5.604996e-03
54 3.934452e-02 6.088978e-03
55 1.262409e-02 5.293105e-03
56 3.954334e-02 6.673285e-03
57 6.748024e-02 2.284149e-03
58 2.189522e-02 4.243810e-03
59 1.211006e-02 5.689200e-03
60 1.369200e-02 6.041093e-03
61 2.324335e-02 3.997742e-03
62 3.076213e-03 8.055052e-03
63 1.618109e-02 4.845988e-03
64 2.506413e-02 4.960921e-03
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