Last updated: 2019-02-11

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    Rmd 2417cdf zouyuxin 2019-02-11 wflow_publish(“analysis/SexDEpipeline.Rmd”) 

library(mashr)
Loading required package: ashr
library(knitr)
library(kableExtra)
library(ggplot2)
library(gridExtra)
sexde <- readRDS('data/sexde/sexde.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/sexde.EE.flash.model.rds')$factors
par(mfrow = c(2, 3))
for(k in 1:7){
  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/sexde.EZ.flash.model.rds')$factors
par(mfrow = c(2, 3))
for(k in 1:16){
  barplot(factors[,k], col=gtex.colors, names.arg = FALSE, axes = FALSE, main=paste0("Factor ", k))
}

# read model
m_mle_EE = readRDS('output/sexde/sexde.EE.FL_PC3.V_mle.mash_model.rds')
m_mle_EE$result = readRDS('output/sexde/sexde.EE.FL_PC3.V_mle.posterior.random.script.rds')
m_mle_EZ = readRDS('output/sexde/sexde.EZ.FL_PC3.V_mle.mash_model.rds')
m_mle_EZ$result = readRDS('output/sexde/sexde.EZ.FL_PC3.V_mle.posterior.random.rds')

Estimated null cor V

V.mle.EE = readRDS('output/sexde/sexde.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/sexde.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 1696029 1709549 
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 17635 20023

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.sexde.svs.FE.allgenes.txt', header = TRUE)
meta_gene = as.character(meta_result$gene[meta_result$qval < 0.05])
length(intersect(meta_gene, names(get_significant_results(m_mle_EZ))))
[1] 9707

There are 11929 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(-1.3,1.1)) + 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(-1.3,1.1)) + 
  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)

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.2

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_3.1.0     kableExtra_1.0.1  knitr_1.20       
[5] mashr_0.2.19.0555 ashr_2.2-26      

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5  corrplot_0.84     purrr_0.2.5      
 [4] lattice_0.20-38   colorspace_1.4-0  htmltools_0.3.6  
 [7] viridisLite_0.3.0 yaml_2.2.0        rlang_0.3.1      
[10] R.oo_1.22.0       mixsqp_0.1-93     pillar_1.3.1     
[13] withr_2.1.2       glue_1.3.0        R.utils_2.7.0    
[16] bindrcpp_0.2.2    bindr_0.1.1       foreach_1.4.4    
[19] plyr_1.8.4        stringr_1.3.1     munsell_0.5.0    
[22] gtable_0.2.0      workflowr_1.1.1   rvest_0.3.2      
[25] R.methodsS3_1.7.1 mvtnorm_1.0-8     codetools_0.2-16 
[28] evaluate_0.12     labeling_0.3      pscl_1.5.2       
[31] doParallel_1.0.14 parallel_3.5.1    highr_0.7        
[34] Rcpp_1.0.0        readr_1.3.1       backports_1.1.3  
[37] scales_1.0.0      rmeta_3.0         webshot_0.5.1    
[40] truncnorm_1.0-8   abind_1.4-5       hms_0.4.2        
[43] digest_0.6.18     stringi_1.2.4     dplyr_0.7.8      
[46] grid_3.5.1        rprojroot_1.3-2   tools_3.5.1      
[49] magrittr_1.5      lazyeval_0.2.1    tibble_2.0.1     
[52] crayon_1.3.4      whisker_0.3-2     pkgconfig_2.0.2  
[55] MASS_7.3-51.1     Matrix_1.2-15     SQUAREM_2017.10-1
[58] xml2_1.2.0        assertthat_0.2.0  rmarkdown_1.11   
[61] httr_1.4.0        rstudioapi_0.9.0  iterators_1.0.10 
[64] R6_2.3.0          git2r_0.24.0      compiler_3.5.1

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