Last updated: 2018-08-31
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File | Version | Author | Date | Message |
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Rmd | ef3e8e3 | zouyuxin | 2018-08-31 | wflow_publish(“analysis/MashLowSignalGTEx3.5.Rmd”) |
html | 62d0375 | zouyuxin | 2018-08-30 | Build site. |
Rmd | 2b58dca | zouyuxin | 2018-08-30 | wflow_publish(“analysis/MashLowSignalGTEx3.5.Rmd”) |
html | a36b08e | zouyuxin | 2018-08-30 | Build site. |
Rmd | 5dfd60c | zouyuxin | 2018-08-30 | wflow_publish(“analysis/MashLowSignalGTEx3.5.Rmd”) |
html | e9f5a34 | zouyuxin | 2018-08-30 | Build site. |
Rmd | 8cb0635 | zouyuxin | 2018-08-30 | wflow_publish(“analysis/MashLowSignalGTEx3.5.Rmd”) |
library(mashr)
Loading required package: ashr
library(knitr)
library(kableExtra)
There are two random sets in the GTEx summary data set. We don’t know the null in the real data. If we have the individual level data, we can do a permutation to generate null. With the summary statistics, we select the null set using threshold on z scores.
Using qvalues 0.05 as the threshold, the corresponding non-significant |z| value is around 3.1. Since lfsr and lfdr are both more conservative than q values, we select the samples with \(\max_{r} |Z_{jr}| < 3.5\) from each data set as the null set. We estimate data driven covariance matrices from data 1, estimate noise correlation from data 2, fit mash model on data 2 and calculate posterior on data 1.
data = readRDS('../output/GTEx_3.5_nullData.rds')
model = readRDS('../output/GTEx_3.5_nullModel.rds')
Sample size:
samplesize = matrix(c(nrow(data$m.data1.null$Bhat), nrow(data$m.data2.null$Bhat)))
row.names(samplesize) = c('data 1', 'data 2')
samplesize %>% kable() %>% kable_styling()
data 1 | 18189 |
data 2 | 25718 |
barplot(get_estimated_pi(model), las=2, cex.names = 0.7)
The estimated weights \(\hat{\pi}\) on null part is not large. The weight on the other covariance structures may concentrate on the small grid (small \(\omega_{l}\)). So they are very close to null, but we cannot view it in the plot. I modified the get_estimated_pi
function to have a threshold for grid. The weights on the grid less than the threshold are merged into the null part.
get_estimated_pi = function(m, dimension = c("cov", "grid", "all"), thres = NULL){
dimension = match.arg(dimension)
if (dimension == "all") {
get_estimated_pi_no_collapse(m)
}
else {
g = get_fitted_g(m)
pihat = g$pi
pihat_names = NULL
pi_null = NULL
if (g$usepointmass) {
pihat_names = c("null", pihat_names)
pi_null = pihat[1]
pihat = pihat[-1]
}
pihat = matrix(pihat, nrow = length(g$Ulist))
if(!is.null(thres)){
pi_null = sum(pihat[, g$grid <= thres]) + pi_null
pihat = pihat[, g$grid > thres]
}
if (dimension == "cov"){
pihat = rowSums(pihat)
pihat_names = c(pihat_names, names(g$Ulist))
}
else if (dimension == "grid") {
pihat = colSums(pihat)
pihat_names = c(pihat_names, 1:length(g$grid))
}
pihat = c(pi_null, pihat)
names(pihat) = pihat_names
return(pihat)
}
}
barplot(get_estimated_pi(model, thres = 0.01), las=2, cex.names = 0.7)
Version | Author | Date |
---|---|---|
e9f5a34 | zouyuxin | 2018-08-30 |
The correlation for the ED_tPCA
is
corrplot::corrplot(cov2cor(model$fitted_g$Ulist[['ED_tPCA']]))
Version | Author | Date |
---|---|---|
e9f5a34 | zouyuxin | 2018-08-30 |
There are 483 significant samples in data 1.
mash
increases power, because it considers the sharing among conditions. We permute samples in each condition to break the sharing.
data = readRDS('../output/GTEx_3.5_nullPermData.rds')
model = readRDS('../output/GTEx_3.5_nullPermModel.rds')
Sample size:
samplesize = matrix(c(nrow(data$m.data1.p.null$Bhat), nrow(data$m.data2.p.null$Bhat)))
row.names(samplesize) = c('data 1', 'data 2')
samplesize %>% kable() %>% kable_styling()
data 1 | 18189 |
data 2 | 25718 |
barplot(get_estimated_pi(model), las=2, cex.names = 0.7, main = 'Estiamted pi')
Version | Author | Date |
---|---|---|
e9f5a34 | zouyuxin | 2018-08-30 |
barplot(get_estimated_pi(model, thres = 0.01), las=2, cex.names = 0.7, main='Estimated pi with threshold (0.01) in scale')
Version | Author | Date |
---|---|---|
e9f5a34 | zouyuxin | 2018-08-30 |
There are 44 significant samples in data 1.
There is no overfitting issue.
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
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] kableExtra_0.9.0 knitr_1.20 mashr_0.2-11 ashr_2.2-10
loaded via a namespace (and not attached):
[1] Rcpp_0.12.18 highr_0.7 pillar_1.3.0
[4] compiler_3.5.1 git2r_0.23.0 plyr_1.8.4
[7] workflowr_1.1.1 R.methodsS3_1.7.1 R.utils_2.6.0
[10] iterators_1.0.10 tools_3.5.1 corrplot_0.84
[13] digest_0.6.15 viridisLite_0.3.0 tibble_1.4.2
[16] evaluate_0.11 lattice_0.20-35 pkgconfig_2.0.2
[19] rlang_0.2.2 Matrix_1.2-14 foreach_1.4.4
[22] rstudioapi_0.7 yaml_2.2.0 parallel_3.5.1
[25] mvtnorm_1.0-8 xml2_1.2.0 httr_1.3.1
[28] stringr_1.3.1 hms_0.4.2 rprojroot_1.3-2
[31] grid_3.5.1 R6_2.2.2 rmarkdown_1.10
[34] rmeta_3.0 readr_1.1.1 magrittr_1.5
[37] whisker_0.3-2 scales_1.0.0 backports_1.1.2
[40] codetools_0.2-15 htmltools_0.3.6 MASS_7.3-50
[43] rvest_0.3.2 assertthat_0.2.0 colorspace_1.3-2
[46] stringi_1.2.4 munsell_0.5.0 doParallel_1.0.11
[49] pscl_1.5.2 truncnorm_1.0-8 SQUAREM_2017.10-1
[52] crayon_1.3.4 R.oo_1.22.0
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