SexDEPlots
Yuxin Zou
6/5/2019
Last updated: 2019-06-11
Checks: 6 0
Knit directory: GTEx/
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library(mashr)
library(knitr)
library(kableExtra)
library(ggplot2)
library(gridExtra)
library(tidyverse)
library(dplyr)
library(ggrepel)m_mle_EZ = readRDS('output/sexde/sexde.EZ.FL_PC3.V_mle.mash_model.rds')
m_mle_EZ$result = readRDS('output/sexde/sexde.EZ.FL_PC3.V_mle.posterior.random.rds')
m_mle_EZ_mean = readRDS('output/sexde/sexde.EZ.FL_PC3.V_mle.mash_model.rds')
m_mle_EZ_mean$result = readRDS('output/sexde/sexde.EZ.FL_PC3.V_mle.posterior.random.mean.script.rds')sig_thresh = 0.01Number of significant:
numsig_EZ = c(length(get_significant_results(m_mle_EZ, thresh=sig_thresh)))
numsig_EZ_mean = c(length(get_significant_results(m_mle_EZ_mean, thresh = sig_thresh)))
tmp = cbind(numsig_EZ, numsig_EZ_mean)
row.names(tmp) = c('MLE')
colnames(tmp) = c('EZ', 'EZ mean')
tmp %>% kable() %>% kable_styling()| EZ | EZ mean | |
|---|---|---|
| MLE | 12077 | 4916 |
Plot 1
Barplot for number of tissues shared by significance:
sig = m_mle_EZ$result$lfsr < sig_thresh
num = table(rowSums(sig))
num = num[-1]
barplot(log10(num), las=2, cex.names=0.7, ylab='number of genes (log10)', xlab='number of tissues')
Plot 2
load('data/sexde/color_abb_codes.Robj')
geno = read.delim('data/sexde/genetypes.txt', header = FALSE)
geno$HUGO = gsub('.*.*_', '', geno$V2)
geno$V2 = gsub("_.*$", "", geno$V2)
colnames(geno) = c('chr', 'GENE', 'V3', 'HUGO')
geno=geno %>% distinct(GENE, .keep_all = TRUE)Volcano Plot using average posterior mean:
data = data.frame(GENE = rownames(m_mle_EZ_mean$result$PosteriorMean), PosteriorMean = m_mle_EZ_mean$result$PosteriorMean, PosteriorSD = m_mle_EZ_mean$result$PosteriorSD, lfsr = m_mle_EZ_mean$result$lfsr, sharing = rowSums(sig))
data$lfsr[data$lfsr < 0] = 0
data = inner_join(data, geno, by='GENE')Warning: Column `GENE` joining factor and character vector, coercing into
character vectordata$Significant <- ifelse(data$lfsr < sig_thresh, "lfsr < 0.01", "Not Sig")
data$position <- ifelse(data$chr %in% c('chrX', 'chrY'), 'x-linked', 'autosomal')p = ggplot(data, aes(x = PosteriorMean, y = -log10(lfsr+1e-8))) +
geom_point(aes(color = Significant, size = sharing, shape=position)) + xlim(-10,10) +
scale_color_manual(values = c("red", "grey")) +
geom_errorbarh(aes(xmin=PosteriorMean-2*PosteriorSD, xmax=PosteriorMean+2*PosteriorSD, color = Significant),cex=0.5)+
ylab('-log10(lfsr)') + xlab('effect size') +
theme_bw(base_size = 12) + theme(legend.position = "bottom")
p
| Version | Author | Date |
|---|---|---|
| d30ab3b | zouyuxin | 2019-06-05 |
Volcano Plot using max effect size:
a = apply(abs(m_mle_EZ$result$PosteriorMean), 1, which.max)
beta = sapply(1:nrow(m_mle_EZ$result$PosteriorMean), function(i) m_mle_EZ$result$PosteriorMean[i, a[i]])
se = sapply(1:nrow(m_mle_EZ$result$PosteriorSD), function(i) m_mle_EZ$result$PosteriorSD[i, a[i]])
lfsr = sapply(1:nrow(m_mle_EZ$result$lfsr), function(i) m_mle_EZ$result$lfsr[i, a[i]])
data = data.frame(GENE = rownames(m_mle_EZ$result$PosteriorMean), PosteriorMean = beta, PosteriorSD = se, lfsr = lfsr, sharing = rowSums(sig))
data$lfsr[data$lfsr < 0] = 0
data = inner_join(data, geno, by='GENE')Warning: Column `GENE` joining factor and character vector, coercing into
character vectordata$Significant <- ifelse(data$lfsr < sig_thresh, "lfsr < 0.01", "Not Sig")
data$position <- ifelse(data$chr %in% c('chrX', 'chrY'), 'x-linked', 'autosomal')
## Create a column to indicate which genes to label
data$genelabels <- ""
idx1 = which(data$lfsr<1e-8)
idx2 = which((nrow(data)+1 - rank(abs(data$PosteriorMean))) <= 20)
idx = intersect(idx1, idx2)
data$genelabels[idx] <- TRUEp = ggplot(data, aes(x = PosteriorMean, y = -log10(lfsr+1e-8))) +
geom_point(aes(color = Significant, size = sharing, shape=position)) + xlim(-12, 12) + ylim(0,8.5) +
scale_color_manual(values = c("red", "grey")) +
geom_errorbarh(aes(xmin=PosteriorMean-2*PosteriorSD, xmax=PosteriorMean+2*PosteriorSD, color = Significant),cex=0.5)+
ylab('-log10(lfsr)') + xlab('effect size') +
theme_bw(base_size = 12) + theme(legend.position = "bottom") +
geom_text_repel(aes(label = ifelse(genelabels == T, HUGO,"")))
p
Plot 3
Forest Plot using average posterior mean:
data = data.frame(GENE = rownames(m_mle_EZ_mean$result$PosteriorMean), PosteriorMean = m_mle_EZ_mean$result$PosteriorMean, PosteriorSD = m_mle_EZ_mean$result$PosteriorSD, lfsr = m_mle_EZ_mean$result$lfsr, sharing = rowSums(sig))
data$lfsr[data$lfsr < 0] = 0
data = inner_join(data, geno, by='GENE')Warning: Column `GENE` joining factor and character vector, coercing into
character vectordata$Significant <- ifelse(data$lfsr < sig_thresh, "lfsr < 0.01", "Not Sig")
data$position <- ifelse(data$chr %in% c('chrX', 'chrY'), 'x-linked', 'autosomal')
data = data %>% arrange(desc(lfsr))
data$GENE = factor(data$GENE, levels = data$GENE)
p = ggplot(data=data,
aes(x = GENE,y = PosteriorMean))+
geom_point(aes(shape=position, size=sharing))+
geom_hline(yintercept =0, linetype=2)+
xlab('')+ ylab("logFC")+
geom_errorbar(aes(ymin=PosteriorMean-2*PosteriorSD, ymax=PosteriorMean+2*PosteriorSD),width=0.5,cex=0.5)+
theme(plot.title=element_text(size=16,face="bold"),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x=element_text(face="bold"),
axis.title=element_text(size=12,face="bold"),
strip.text.y = element_text(hjust=0,vjust = 1,angle=180,face="bold"))+
coord_flip()
p
| Version | Author | Date |
|---|---|---|
| d30ab3b | zouyuxin | 2019-06-05 |
Forest Plot using max effect size:
a = apply(abs(m_mle_EZ$result$PosteriorMean), 1, which.max)
beta = sapply(1:nrow(m_mle_EZ$result$PosteriorMean), function(i) m_mle_EZ$result$PosteriorMean[i, a[i]])
se = sapply(1:nrow(m_mle_EZ$result$PosteriorSD), function(i) m_mle_EZ$result$PosteriorSD[i, a[i]])
lfsr = sapply(1:nrow(m_mle_EZ$result$lfsr), function(i) m_mle_EZ$result$lfsr[i, a[i]])
data = data.frame(GENE = rownames(m_mle_EZ$result$PosteriorMean), PosteriorMean = beta, PosteriorSD = se, lfsr = lfsr, sharing = rowSums(sig))
data$lfsr[data$lfsr < 0] = 0
data = inner_join(data, geno, by='GENE')Warning: Column `GENE` joining factor and character vector, coercing into
character vectordata$Significant <- ifelse(data$lfsr < sig_thresh, "lfsr < 0.01", "Not Sig")
data$position <- ifelse(data$chr %in% c('chrX', 'chrY'), 'x-linked', 'autosomal')
data = data %>% arrange(desc(lfsr))
data$GENE = factor(data$GENE, levels = data$GENE)
## Create a column to indicate which genes to label
data$genelabels <- ""
idx1 = which(data$lfsr<1e-8)
idx2 = which((nrow(data)+1 - rank(abs(data$PosteriorMean))) <= 20)
idx = intersect(idx1, idx2)
data$genelabels[idx] <- TRUEp = ggplot(data=data,
aes(x = GENE,y = PosteriorMean))+
geom_point(aes(shape=position, size=sharing))+
geom_hline(yintercept =0, linetype=2)+
xlab('')+ ylab("logFC")+
geom_errorbar(aes(ymin=PosteriorMean-2*PosteriorSD, ymax=PosteriorMean+2*PosteriorSD),width=0.5,cex=0.5)+
theme(plot.title=element_text(size=16,face="bold"),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x=element_text(face="bold"),
axis.title=element_text(size=12,face="bold"),
strip.text.y = element_text(hjust=0,vjust = 1,angle=180,face="bold"))+
coord_flip() +
geom_text_repel(aes(label = ifelse(genelabels == T, HUGO,"")))
p
| Version | Author | Date |
|---|---|---|
| f5f3125 | zouyuxin | 2019-06-11 |
Plot 4
a = apply(abs(m_mle_EZ$result$PosteriorMean), 1, which.max)
beta = sapply(1:nrow(m_mle_EZ$result$PosteriorMean), function(i) m_mle_EZ$result$PosteriorMean[i, a[i]])
se = sapply(1:nrow(m_mle_EZ$result$PosteriorSD), function(i) m_mle_EZ$result$PosteriorSD[i, a[i]])
lfsr = sapply(1:nrow(m_mle_EZ$result$lfsr), function(i) m_mle_EZ$result$lfsr[i, a[i]])
data = data.frame(GENE = rownames(m_mle_EZ$result$PosteriorMean), PosteriorMean = beta, PosteriorSD = se, lfsr = lfsr, sharing = rowSums(sig))
data$lfsr[data$lfsr < 0] = 0
data = inner_join(data, geno, by='GENE')Warning: Column `GENE` joining factor and character vector, coercing into
character vectordata$Significant <- ifelse(data$lfsr < sig_thresh, "lfsr < 0.01", "Not Sig")
data$position <- ifelse(data$chr %in% c('chrX', 'chrY'), 'x-linked', 'autosomal')p = ggplot(data=data,
aes(x = position,y = PosteriorMean)) +
geom_violin() + geom_boxplot(width=0.1) +
ylab('effect size')
p
| Version | Author | Date |
|---|---|---|
| f5f3125 | zouyuxin | 2019-06-11 |
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.5
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] ggrepel_0.8.1 forcats_0.4.0 stringr_1.4.0
[4] dplyr_0.8.1 purrr_0.3.2 readr_1.3.1
[7] tidyr_0.8.3 tibble_2.1.1 tidyverse_1.2.1
[10] gridExtra_2.3 ggplot2_3.1.1 kableExtra_1.1.0
[13] knitr_1.23 mashr_0.2.21.0641 ashr_2.2-38
loaded via a namespace (and not attached):
[1] httr_1.4.0 jsonlite_1.6 viridisLite_0.3.0
[4] foreach_1.4.4 modelr_0.1.4 rmeta_3.0
[7] assertthat_0.2.1 highr_0.8 mixsqp_0.1-119
[10] cellranger_1.1.0 yaml_2.2.0 pillar_1.4.1
[13] backports_1.1.4 lattice_0.20-38 glue_1.3.1
[16] digest_0.6.19 rvest_0.3.4 colorspace_1.4-1
[19] htmltools_0.3.6 Matrix_1.2-15 plyr_1.8.4
[22] pkgconfig_2.0.2 broom_0.5.2 haven_2.1.0
[25] mvtnorm_1.0-10 scales_1.0.0 webshot_0.5.1
[28] whisker_0.3-2 git2r_0.25.2 generics_0.0.2
[31] withr_2.1.2 lazyeval_0.2.2 cli_1.1.0
[34] magrittr_1.5 crayon_1.3.4 readxl_1.3.1
[37] evaluate_0.13 fs_1.3.1 doParallel_1.0.14
[40] nlme_3.1-137 MASS_7.3-51.1 xml2_1.2.0
[43] truncnorm_1.0-8 tools_3.5.3 hms_0.4.2
[46] munsell_0.5.0 compiler_3.5.3 rlang_0.3.4
[49] grid_3.5.3 iterators_1.0.10 rstudioapi_0.10
[52] labeling_0.3 rmarkdown_1.13 gtable_0.3.0
[55] codetools_0.2-16 abind_1.4-5 R6_2.4.0
[58] lubridate_1.7.4 workflowr_1.3.0 rprojroot_1.3-2
[61] stringi_1.4.3 pscl_1.5.2 parallel_3.5.3
[64] SQUAREM_2017.10-1 Rcpp_1.0.1 tidyselect_0.2.5
[67] xfun_0.7