GTEx sexDE Updated data
Yuxin Zou
2020-01-15
Last updated: 2020-02-20
Checks: 7 0
Knit directory: GTEx/
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library(mashr)Loading required package: ashrlibrary(knitr)
library(kableExtra)
library(ggplot2)
library(gridExtra)sexde <- readRDS('data/sexde/sexde.data.rds')
load("data/color_abb_codes.Robj")
# Tissue color palette
color_code$Tissue[18] = 'Brain_Spinal_cord_cervical_c.1'
color_code$Tissue[22] = 'Cells_EBV.transformed_lymphocytes'
gtex.colors <- color_code$colorThe results are from mashr_flashr_pipeline. We include the data driven covariance matrices based on the first three principal components and factors from flash.
Flash model based on effects:
factors = readRDS('output/sexde.final/outputEE/sexde.data.EE.flash.model.rds')$factors
par(mfrow = c(2, 3))
for(k in 1:10){
barplot(factors[,k], col=gtex.colors, names.arg = FALSE, axes = FALSE, main=paste0("Factor ", k))
}
Flash model based on z scores:
factors = readRDS('output/sexde.final/outputEZ/sexde.data.EZ.flash.model.rds')$factors
par(mfrow = c(2, 3))
for(k in 1:20){
barplot(factors[,k], col=gtex.colors, names.arg = FALSE, axes = FALSE, main=paste0("Factor ", k))
}
| Version | Author | Date |
|---|---|---|
| 9b98777 | zouyuxin | 2020-02-20 |
# read model
m_mle_EE = readRDS('output/sexde.final/outputEE/sexde.ee.fl_pc3.v_mle.mash_model.rds')
m_mle_EZ = readRDS('output/sexde.final/outputEZ/sexde.ez.fl_pc3.v_mle.mash_model.rds')Estimated null cor V
V.mle.EE = readRDS('output/sexde.final/outputEE/sexde.data.EE.FL_PC3.V_mle.rds')
corrplot::corrplot(V.mle.EE, method='color', type='upper', tl.col="black", tl.srt=45, tl.cex = 0.5, diag = FALSE, col=colorRampPalette(c("blue", "white", "red"))(200), cl.lim = c(-1,1), title = 'MLE EE', mar=c(0,0,5,0))
V.mle.EZ = readRDS('output/sexde.final/outputEZ/sexde.data.EZ.FL_PC3.V_mle.rds')
corrplot::corrplot(V.mle.EZ, method='color', type='upper', tl.col="black", tl.srt=45, tl.cex = 0.5, diag = FALSE, col=colorRampPalette(c("blue", "white", "red"))(200), cl.lim = c(-1,1), title = 'MLE EZ', mar=c(0,0,5,0))
Results
logliks = c(get_loglik(m_mle_EE))
logliks_EZ = c(get_loglik(m_mle_EZ))
tmp = cbind(logliks, logliks_EZ)
row.names(tmp) = c('MLE')
colnames(tmp) = c('EE', 'EZ')
tmp %>% kable() %>% kable_styling()| EE | EZ | |
|---|---|---|
| MLE | -5797557 | -5787241 |
par(mfrow=c(1,2))
barplot(get_estimated_pi(m_mle_EE), las=2, cex.names = 0.7, main = 'MLE EE')
barplot(get_estimated_pi(m_mle_EZ), las=2, cex.names = 0.7, main = 'MLE EZ')
Number of significant:
numsig_EE = c(length(get_significant_results(m_mle_EE)))
numsig_EZ = c(length(get_significant_results(m_mle_EZ)))
tmp = cbind(numsig_EE, numsig_EZ)
row.names(tmp) = c('MLE')
colnames(tmp) = c('EE', 'EZ')
tmp %>% kable() %>% kable_styling()| EE | EZ | |
|---|---|---|
| MLE | 15027 | 15984 |
The pairwise sharing by magnitude
par(mfrow = c(1,2))
clrs=colorRampPalette(rev(c('darkred', 'red','orange','yellow','cadetblue1', 'cyan', 'dodgerblue4', 'blue','darkorchid1','lightgreen','green', 'forestgreen','darkolivegreen')))(200)
x <- get_pairwise_sharing(m_mle_EE)
colnames(x) <- colnames(get_lfsr(m_mle_EE))
rownames(x) <- colnames(x)
corrplot::corrplot(x, method='color', type='upper', tl.col="black", tl.srt=45, tl.cex = 0.7, diag = FALSE, col=clrs, cl.lim = c(0,1), title = 'MLE EE', mar=c(0,0,5,0))
x <- get_pairwise_sharing(m_mle_EZ)
colnames(x) <- colnames(get_lfsr(m_mle_EZ))
rownames(x) <- colnames(x)
corrplot::corrplot(x, method='color', type='upper', tl.col="black", tl.srt=45, tl.cex = 0.7, diag = FALSE, col=clrs, cl.lim = c(0,1), title = 'MLE EZ', mar=c(0,0,5,0))
meta_result = read.table('data/sexde/meta.ivw.sexde_logfc_matrix.v8.MASH.txt', header = TRUE)
meta_gene = as.character(meta_result$gene[meta_result$padj < 0.05])
meta_gene = sapply(strsplit(meta_gene, '_', fixed = TRUE), function(x) x[1])
length(intersect(meta_gene, names(get_significant_results(m_mle_EZ))))[1] 8572There are 9405 significant genes from meta analysis, 8572 of them are significant in mash model (EZ) as well.
The gene significant in meta analysis, not in MLE EZ:
ind.name = setdiff(meta_gene, names(get_significant_results(m_mle_EZ)))[1]
ind = which(row.names(sexde$random.b) == ind.name)
stronggene = data.frame(sexde$random.b[ind,])
colnames(stronggene) = 'EffectSize'
stronggene$Group = row.names(stronggene)
stronggene$se = sexde$random.s[ind,]
p1 = ggplot(stronggene, aes(y = EffectSize, x = Group)) +
geom_point(show.legend = FALSE, color=gtex.colors) + coord_flip() + ggtitle('Raw') + ylim(c(-1.6,1.1)) + geom_errorbar(aes(ymin=EffectSize-1.96*se, ymax=EffectSize+1.96*se), width=0.4, show.legend = FALSE, color=gtex.colors) +
theme_bw(base_size=12) + theme(axis.text.y = element_text(colour = gtex.colors, size = 6))
stronggeneMLE = data.frame(m_mle_EZ$result$PosteriorMean[ind,])
colnames(stronggeneMLE) = 'EffectSize'
stronggeneMLE$Group = row.names(stronggeneMLE)
stronggeneMLE$se = m_mle_EZ$result$PosteriorSD[ind,]
p2 = ggplot(stronggeneMLE, aes(y = EffectSize, x = Group)) +
geom_point(show.legend = FALSE, color=gtex.colors) + coord_flip() + ggtitle('MLE EZ') + ylim(c(-1.6,1.1)) +
geom_errorbar(aes(ymin=EffectSize-1.96*se, ymax=EffectSize+1.96*se), width=0.4, show.legend = FALSE, color=gtex.colors) +
theme_bw(base_size=12) + theme(axis.text.y = element_text(colour = gtex.colors, size = 6))
grid.arrange(p1, p2, nrow = 1)
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15.3
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] gridExtra_2.3 ggplot2_3.2.0 kableExtra_1.1.0 knitr_1.23
[5] mashr_0.2.21.0641 ashr_2.2-41
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 mvtnorm_1.0-11 lattice_0.20-38
[4] assertthat_0.2.1 zeallot_0.1.0 rprojroot_1.3-2
[7] digest_0.6.23 foreach_1.4.8 truncnorm_1.0-8
[10] R6_2.4.1 plyr_1.8.5 backports_1.1.5
[13] evaluate_0.13 highr_0.8 httr_1.4.0
[16] pillar_1.4.2 rlang_0.4.2 lazyeval_0.2.2
[19] pscl_1.5.2 rstudioapi_0.10 irlba_2.3.3
[22] whisker_0.3-2 Matrix_1.2-15 rmarkdown_1.13
[25] labeling_0.3 webshot_0.5.1 readr_1.3.1
[28] stringr_1.4.0 munsell_0.5.0 mixsqp_0.3-18
[31] compiler_3.5.3 httpuv_1.5.1 xfun_0.7
[34] pkgconfig_2.0.3 SQUAREM_2020.1 htmltools_0.3.6
[37] tidyselect_0.2.5 tibble_2.1.3 workflowr_1.5.0
[40] codetools_0.2-16 viridisLite_0.3.0 crayon_1.3.4
[43] dplyr_0.8.1 withr_2.1.2 later_0.8.0
[46] MASS_7.3-51.1 grid_3.5.3 gtable_0.3.0
[49] git2r_0.26.1 magrittr_1.5 scales_1.0.0
[52] stringi_1.4.3 fs_1.3.1 promises_1.0.1
[55] doParallel_1.0.15 xml2_1.2.0 vctrs_0.2.0
[58] rmeta_3.0 iterators_1.0.12 tools_3.5.3
[61] glue_1.3.1 purrr_0.3.3 hms_0.5.2
[64] abind_1.4-5 parallel_3.5.3 yaml_2.2.0
[67] colorspace_1.4-1 rvest_0.3.4 corrplot_0.84