creating_merged

Last updated: 2019-12-17

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Knit directory: newlipids/

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File	Version	Author	Date	Message
Rmd	52c52c7	Sarah Urbut	2019-11-01	Update
Rmd	76cc40a	Sarah Urbut	2019-10-27	Update
html	76cc40a	Sarah Urbut	2019-10-27	Update
Rmd	3f85586	Sarah Urbut	2019-10-07	Update
html	3f85586	Sarah Urbut	2019-10-07	Update
Rmd	079589b	Sarah Urbut	2019-10-07	Update
html	079589b	Sarah Urbut	2019-10-07	Update
Rmd	7142cc6	Sarah Urbut	2019-10-04	Updated plots
html	7142cc6	Sarah Urbut	2019-10-04	Updated plots
Rmd	73da9ad	Sarah Urbut	2019-10-03	Update
html	73da9ad	Sarah Urbut	2019-10-03	Update
html	ca158bf	Sarah Urbut	2019-10-03	Update docs

R Markdown

Here we will try to merge based on common chr.position (per Hg18) names.

To run mashr, we need a matrix of maxes. The best way to do this is to choose the max effect across conditions per LD block, as described in Pickrell et al. Only then can we assume the maxes used to create covariance matrices are truly linearly independent. We will select SNPS falling within each of the 1700 LD blocks and choose SNP with maximum absolute effect acrtoss conditions.

ztab=read.table("~/lipids_mvp/data/merged_z_mvp_withnames.txt")
bed=read.table("~/Downloads/ld_chunk.bed")
head(bed)

    V1      V2      V3
1 chr1   10583 1892607
2 chr1 1892607 3582736
3 chr1 3582736 4380811
4 chr1 4380811 5913893
5 chr1 5913893 7247335
6 chr1 7247335 9365199

maxes=apply(ztab[,c("beta.x","beta.y","tgbeta","tcbeta")],1,function(x){max(abs(x))})
znew=cbind(ztab,maxes)
colnames(znew)[c(4:7)]=c("hdl","ldl","tg","tc")

max_block=data.frame(matrix(ncol = ncol(znew), nrow = nrow(bed)))
colnames(max_block)=colnames(znew)
for(i in 1:nrow(bed)){
  chr=bed[i,1]
  start=bed[i,2]
  stop=bed[i,3]
  in_chrom=znew[znew$hg19chrc==chr,]
  goodguys=in_chrom[in_chrom$bp>start&in_chrom$bp<stop,]
 if(nrow(goodguys)>0) {
    z.max=which.max(goodguys[,"maxes"])
    z_good=goodguys[z.max,]
    } else {
      z_good=rep(0,ncol(max_block))
    }
  z_good=data.table(z_good,stringsAsFactors = T)
  z_good$hg19chrc=as.character(z_good$hg19chrc)
  z_good$snpid.x=as.character(z_good$snpid.x)
  max_block[i,]=z_good
  print(i)
}

max_block=na.omit(max_block)
write.table(max_block,"~/lipids_mvp/data/max_ld_block.txt")

Now, we’re ready to mash!

ztab=read.table("~/lipids_mvp/data/merged_z_mvp_withnames.txt")
znew=read.table("~/lipids_mvp/data/merged_z_mvp.txt")
colnames(znew)=c("hdl","ldl","tg","tc")
library("mashr")
library("flashr")
max_block=read.table("~/lipids_mvp/data/max_ld_block.txt")
source('~/Dropbox/jointData/flashscript.R')
# identify a random subset of 20000 tests
random.subset = sample(1:nrow(znew),40000)
zmash=as.matrix(znew[,c("hdl","ldl","tg","tc")]);rownames(zmash)=ztab$snpid.x
data.temp = mash_set_data(zmash[random.subset,],alpha = 1)
Vhat = estimate_null_correlation_simple(data.temp)
saveRDS("~/lipids_mvp/data/MVPVhat.rds")
library("lattice")
clrs = colorRampPalette((c("#D73027","#FC8D59","#FEE090","#FFFFBF", "#E0F3F8","#91BFDB","#4575B4")))(64)

print(levelplot(Vhat,col.regions = clrs,xlab = "",ylab = "",colorkey = TRUE,main="VHAT"))

rm(data.temp)
data.random = mash_set_data(zmash[random.subset,],alpha = 1,V=Vhat)

zmax=apply(max_block[,c(4:7)],2,function(x){as.numeric(x)});rownames(zmax)=max_block$snpid.x
data.strong = mash_set_data(zmax,alpha = 1,V=Vhat)

U.pca = cov_pca(data.strong,3)

U.flash=cov_flash(data.strong, non_canonical = TRUE)
X.center = apply(data.strong$Bhat, 2, function(x) x - mean(x))
U.ed = cov_ed(data.strong, c(U.flash, U.pca, list("XX" = t(X.center) %*% X.center / nrow(X.center))))
saveRDS(U.ed,"~/lipids_mvp/data/EDcov.Rds")

U.ed=readRDS("~/lipids_mvp/data/EDcov.Rds")
U.c = cov_canonical(data.random)
m = mash(data.random, Ulist = c(U.ed,U.c),outputlevel = 1)
saveRDS(m,"~/lipids_mvp/data/mfitMVP.rds")

Now, let’s plot the patterns of sharing as the correlation matrix of the estimated covariance matrices..

zmash=read.table("~/lipids_mvp/data/merged_z_mvp.txt")
colnames(zmash)=c("hdl","ldl","tg","tc")

m=readRDS("~/lipids_mvp/data/mfitMVP.rds")

k=length(m$fitted_g$Ulist)
l=length(m$fitted_g$grid)
pimat=matrix(m$fitted_g$pi[-1],nrow=l,byrow=T)
colnames(pimat)=names(m$fitted_g$pi)[2:(k+1)]
barplot(colSums(pimat),las=2)

Version	Author	Date
76cc40a	Sarah Urbut	2019-10-27

library("lattice")
for(i in 1:8){
  z.num=as.matrix(cov2cor(m$fitted_g$Ulist[[i]]))
  colnames(z.num)=row.names(z.num)=colnames(zmash)
clrs = colorRampPalette(rev(c("#D73027","#FC8D59","#FEE090","#FFFFBF", "#E0F3F8","#91BFDB","#4575B4")))(64)
z.num[lower.tri(z.num)] = NA
print(levelplot(z.num,col.regions = clrs,xlab = "",ylab = "",colorkey = TRUE,main=paste0(names(m$fitted_g$Ulist)[[i]])))
}

Version	Author	Date
76cc40a	Sarah Urbut	2019-10-27

Version	Author	Date
76cc40a	Sarah Urbut	2019-10-27

Version	Author	Date
76cc40a	Sarah Urbut	2019-10-27

Version	Author	Date
76cc40a	Sarah Urbut	2019-10-27

Version	Author	Date
76cc40a	Sarah Urbut	2019-10-27

Version	Author	Date
76cc40a	Sarah Urbut	2019-10-27

Version	Author	Date
76cc40a	Sarah Urbut	2019-10-27

Version	Author	Date
76cc40a	Sarah Urbut	2019-10-27

Now we can compute posteriors,

mash.data=mash_set_data(zmash,V = Vhat,alpha = 1)

m$result=mash_compute_posterior_matrices(m, mash.data, algorithm.version = "Rcpp")
saveRDS(m,"~/lipids_mvp/data/mashresult_mvp.rds")

Let’s take a look:

m=readRDS("~/lipids_mvp/data/mashresult_mvp.rds")
head(m$result$PosteriorMean)

                   hdl         ldl         tg          tc
rs6678176    1.0629093  0.07148775 -0.8411636  0.04103405
rs76909621   0.3901428 -0.18927773 -0.4152670 -0.21156310
rs78642210   0.1458664  0.07404952 -0.1182961  0.05731219
rs77140576   0.3656539 -0.18871989 -0.3827297 -0.20549333
rs113470118 -0.1377051 -0.07017948  0.1158224 -0.05287451
rs75635821   0.3603703 -0.18449444 -0.3773584 -0.20126912

head(m$result$lfsr)

                  hdl       ldl        tg        tc
rs6678176   0.1123920 0.4741408 0.1205983 0.5169807
rs76909621  0.2961115 0.4053798 0.3025860 0.4295731
rs78642210  0.4989478 0.5404176 0.4937694 0.5447642
rs77140576  0.3110436 0.4119177 0.3205846 0.4379812
rs113470118 0.5039852 0.5445682 0.4971376 0.5495551
rs75635821  0.3143522 0.4154700 0.3236874 0.4409992

lfsr=m$result$lfsr
s=rowSums(lfsr<=0.05)

hist(s[s>1],freq=FALSE,main="Number of Conditions")

Version	Author	Date
76cc40a	Sarah Urbut	2019-10-27

#ash.z=apply(zmash,2,function(x){ashr::ash(x,sebetahat = rep(1,length(x)))})
# ash.pm=matrix(nrow=nrow(zmash),ncol=ncol(zmash))
# ash.lf=matrix(nrow=nrow(zmash),ncol=ncol(zmash))
# 
# for(i in 1:ncol(zmash))
#   
# {
#   x=zmash[,i]
#   a=ashr::ash(x,sebetahat = rep(1,length(x)))
#   ash.pm[,i]=a$result$PosteriorMean
#   ash.lf[,i]=a$result$lfsr
#   print(i)
# }
# 
# 
# colnames(ash.pm)=colnames(ash.lf)=colnames(zmash)
# rownames(ash.pm)=rownames(ash.lf)=rownames(zmash)
# 
# write.table(ash.pm,"~/lipids_mvp/data/ash_pm.txt")
# write.table(ash.lf,"~/lipids_mvp/data/ash_lf.txt")

ash.lf=read.table("~/lipids_mvp/data/ash_lf.txt")

ptab=apply(zmash,2,function(x){2*pnorm(-abs(x))})
#write.table(ptab,"~/lipids_mvp/data/merged_p.txt")
sum(ash.lf<0.05)

[1] 106052

sum(lfsr<0.05)

[1] 237730

Here 106052 SNPS x Conditions are less than 0.05 using a univariate appropach and 237730 are less than 0.05 with a joint approach, a roughly 250% increase. Furthermore, 102253 SNPS are significant in at least one condition wiht a juint approach, while 72875 with a univariate one.

p=read.table("~/lipids_mvp/data/merged_p.txt")
library(knitr)
library(kableExtra)
dt <- cbind(c(paste("Bonferroni=",sum(p<=5e-8)),paste("univariate_ash=",sum(ash.lf<0.05)),paste("mv_mash=",sum(lfsr<0.05))))

dt <- cbind(c(sum(p<=5e-8),sum(ash.lf<0.05),sum(lfsr<0.05)))
dt=cbind(dt,c(sum(rowSums(p<=5e-8)>0),sum(rowSums(ash.lf<0.05)>0),sum(rowSums(lfsr<0.05)>0)))
rownames(dt)=c("Bonferroni","UnivariateAsh","Mash")
colnames(dt)=c("Overall All Associations","Per Snp, in at least one")           
              
kable(dt)

	Overall All Associations	Per Snp, in at least one
Bonferroni	25260	16843
UnivariateAsh	106052	72875
Mash	237730	102253

dt %>%
  kable() %>%
  kable_styling()

	Overall All Associations	Per Snp, in at least one
Bonferroni	25260	16843
UnivariateAsh	106052	72875
Mash	237730	102253

sessionInfo()

R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] lattice_0.20-38   flashr_0.6-3      mashr_0.2.21.0631 ashr_2.2-37      
[5] reshape_0.8.8    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1        pillar_1.4.2      compiler_3.5.2   
 [4] git2r_0.26.1      plyr_1.8.4        highr_0.8        
 [7] workflowr_1.4.0   iterators_1.0.10  tools_3.5.2      
[10] digest_0.6.20     tibble_2.1.3      gtable_0.3.0     
[13] evaluate_0.14     pkgconfig_2.0.2   rlang_0.4.0      
[16] Matrix_1.2-17     foreach_1.4.4     rstudioapi_0.10  
[19] yaml_2.2.0        parallel_3.5.2    mvtnorm_1.0-11   
[22] xfun_0.8          dplyr_0.8.3       stringr_1.4.0    
[25] knitr_1.23        fs_1.3.1          tidyselect_0.2.5 
[28] rprojroot_1.3-2   grid_3.5.2        glue_1.3.1       
[31] R6_2.4.0          rmarkdown_1.14    mixsqp_0.1-97    
[34] rmeta_3.0         reshape2_1.4.3    purrr_0.3.2      
[37] ggplot2_3.2.0     magrittr_1.5      scales_1.0.0     
[40] backports_1.1.4   codetools_0.2-16  htmltools_0.3.6  
[43] MASS_7.3-51.4     abind_1.4-5       assertthat_0.2.1 
[46] softImpute_1.4    colorspace_1.4-1  stringi_1.4.3    
[49] lazyeval_0.2.2    munsell_0.5.0     doParallel_1.0.14
[52] pscl_1.5.2        truncnorm_1.0-8   SQUAREM_2017.10-1
[55] crayon_1.3.4

creating_merged_lipids

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