GTEx sexDE Updated data

Flash model based on effects:

Flash model based on z scores:

Estimated null cor V

Results

Last updated: 2020-01-15

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Knit directory: GTEx/

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library(mashr)

Loading required package: ashr

library(knitr)
library(kableExtra)
library(ggplot2)
library(gridExtra)

sexde <- readRDS('data/sexde.data.rds')

missing.tissues <- c(7, 24, 25, 31, 40, 43, 49, 51, 52)
gtex.colors <- read.table("https://github.com/stephenslab/gtexresults/blob/master/data/GTExColors.txt?raw=TRUE", sep = '\t', comment.char = '')[-missing.tissues, 2]
gtex.colors <- as.character(gtex.colors)

The 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 1:13){
  barplot(factors[,k], col=gtex.colors, names.arg = FALSE, axes = FALSE, main=paste0("Factor ", k))
}

Version	Author	Date
9694106	zouyuxin	2020-01-15

Version	Author	Date
9694106	zouyuxin	2020-01-15

Version	Author	Date
9694106	zouyuxin	2020-01-15

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 1:17){
  barplot(factors[,k], col=gtex.colors, names.arg = FALSE, axes = FALSE, main=paste0("Factor ", k))
}

Version	Author	Date
9694106	zouyuxin	2020-01-15

Version	Author	Date
9694106	zouyuxin	2020-01-15

Version	Author	Date
9694106	zouyuxin	2020-01-15

# 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))

Version	Author	Date
9694106	zouyuxin	2020-01-15

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))

Version	Author	Date
9694106	zouyuxin	2020-01-15

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	1706731	1719887

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')

Version	Author	Date
9694106	zouyuxin	2020-01-15

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	17391	19556

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))

Version	Author	Date
9694106	zouyuxin	2020-01-15

meta_result = read.table('data/meta.ivw.sexde_logfc_matrix.sexde.svs.allgenes.v8.FINAL.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] 9707

There are 11212 significant genes from meta analysis, 9707 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('Row') + 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)

Version	Author	Date
9694106	zouyuxin	2020-01-15

sessionInfo()

R version 3.5.3 (2019-03-11)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.15.2

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_2.2-39      

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_1.4.7     truncnorm_1.0-8  
[10] R6_2.4.1          plyr_1.8.5        backports_1.1.5  
[13] evaluate_0.13     httr_1.4.0        highr_0.8        
[16] pillar_1.4.2      rlang_0.4.2       lazyeval_0.2.2   
[19] pscl_1.5.2        rstudioapi_0.10   whisker_0.3-2    
[22] Matrix_1.2-15     rmarkdown_1.13    labeling_0.3     
[25] webshot_0.5.1     readr_1.3.1       stringr_1.4.0    
[28] munsell_0.5.0     mixsqp_0.3-10     compiler_3.5.3   
[31] httpuv_1.5.1      xfun_0.7          pkgconfig_2.0.3  
[34] SQUAREM_2017.10-1 htmltools_0.3.6   tidyselect_0.2.5 
[37] tibble_2.1.3      workflowr_1.5.0   codetools_0.2-16 
[40] viridisLite_0.3.0 crayon_1.3.4      dplyr_0.8.1      
[43] withr_2.1.2       later_0.8.0       MASS_7.3-51.1    
[46] grid_3.5.3        gtable_0.3.0      git2r_0.26.1     
[49] magrittr_1.5      scales_1.0.0      stringi_1.4.3    
[52] fs_1.3.1          promises_1.0.1    doParallel_1.0.15
[55] xml2_1.2.0        vctrs_0.2.0       rmeta_3.0        
[58] iterators_1.0.12  tools_3.5.3       glue_1.3.1       
[61] purrr_0.3.2       hms_0.5.2         abind_1.4-5      
[64] parallel_3.5.3    yaml_2.2.0        colorspace_1.4-1 
[67] rvest_0.3.4       corrplot_0.84

GTEx sexDE Updated data

Yuxin Zou

2020-01-15

Flash model based on effects:

Flash model based on z scores:

Estimated null cor V

Results