Plot_herit_first_second_batch
Hao He
2021-11-01
Last updated: 2021-11-01
Checks: 7 0
Knit directory: DO_Opioid/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ebcefc3 | xhyuo | 2021-11-01 | herit_first_second_batch |
Last update: 2021-11-01
##loading libraries
library(ggplot2)
library(gridExtra)
library(GGally)
library(parallel)
library(qtl2)
library(parallel)
library(survival)
library(regress)
library(abind)
library(openxlsx)
library(tidyverse)
library(cowplot)
library(furrr)
library(patchwork)
library(qtl)
theme_set(theme_cowplot())
source("code/gemma_plot.R")
source("code/cfw/R/gemma.R")
rz.transform <- function(y) {
rankY=rank(y, ties.method="average", na.last="keep")
rzT=qnorm(rankY/(length(na.exclude(rankY))+1))
return(rzT)
}
Pvaltolod <- function(p){
if(p == 0) p = 1e-10
y = ifelse(p >= 0.5, 0, qchisq(1-2*p, df=1)/(2*log(10)))
y
}
load("code/do.colors.RData")
pheno.name <- c("Survival.Time", "Recovery.Time", "Min.depression", "Status_bin")###heritability
#plot heritability by qtl2 array for first batch--------------
load("output/hsq_1_first_batch_herit_qtl2.RData")
herit <- data.frame(Phenotype = names(unlist(hsq_1)),
Heritability = round(unlist(hsq_1),3))
herit <- herit %>%
arrange(desc(Heritability))
herit$Phenotype <- factor(herit$Phenotype, levels = herit$Phenotype)
#histgram
pdf(file = paste0("data/FinalReport/GCTA/DO_first_batch","_heritability_by_qtl2_array.pdf"), height = 10, width = 10)
p1 <- ggplot(data=herit, aes(x=Phenotype, y=Heritability)) + #, fill=Domain, color = Domain)) +
geom_bar(stat="identity", fill = "blue", color = "blue", show.legend = FALSE) +
scale_y_continuous(breaks=seq(0.0, 1.0, 0.1)) +
geom_text(aes(label = Heritability, y = Heritability + 0.005), position = position_dodge(0.9),vjust = 0) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("First batch Heritability by qtl2 array")
p1
dev.off()
# png
# 2
p1
#plot heritability by GCTA first batch
hsq.gcta <- list()
for (i in pheno.name){
hsq.gcta[[i]] <- read.table(file = paste0("data/FinalReport/GCTA/opioid_first_batch_",i,".out.hsq"),sep = "\t", header = FALSE,fill = TRUE, stringsAsFactors = FALSE)
}
h <- data.frame(
Phenotype = pheno.name,
Heritability = as.numeric(as.vector(unlist(lapply(hsq.gcta, FUN = function(x){x[5,2]})))),
Sample_size = as.numeric(as.vector(unlist(lapply(hsq.gcta, FUN = function(x){x[11,2]})))),
# Domain = sub("\\..*", "", phe.name),
stringsAsFactors = FALSE)
h <- h %>%
arrange(desc(Heritability))
h$Phenotype <- factor(h$Phenotype, levels = h$Phenotype)
h$Heritability <- round(h$Heritability,3)
#histgram
pdf(file = paste0("data/FinalReport/GCTA/DO_first_batch","_heritability_by_GCTA.pdf"), height = 10, width = 10)
p <- ggplot(data=h, aes(x=Phenotype, y=Heritability)) + #, fill=Domain, color = Domain)) +
geom_bar(stat="identity", fill = "blue", color = "blue", show.legend = FALSE) +
scale_y_continuous(breaks=seq(0.0, 1.0, 0.1)) +
geom_text(aes(label = Heritability, y = Heritability + 0.005), position = position_dodge(0.9),vjust = 0) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("First batch Heritability by GCTA")
p
dev.off()
# png
# 2
p
#plot heritability by qtl2 array for second batch--------------
load("output/hsq_2_second_batch_herit_qtl2.RData")
herit <- data.frame(Phenotype = names(unlist(hsq_2)),
Heritability = round(unlist(hsq_2),3))
herit <- herit %>%
arrange(desc(Heritability))
herit$Phenotype <- factor(herit$Phenotype, levels = herit$Phenotype)
#histgram
pdf(file = paste0("data/FinalReport/GCTA/DO_second_batch","_heritability_by_qtl2_array.pdf"), height = 10, width = 10)
p2 <- ggplot(data=herit, aes(x=Phenotype, y=Heritability)) + #, fill=Domain, color = Domain)) +
geom_bar(stat="identity", fill = "blue", color = "blue", show.legend = FALSE) +
scale_y_continuous(breaks=seq(0.0, 1.0, 0.1)) +
geom_text(aes(label = Heritability, y = Heritability + 0.005), position = position_dodge(0.9),vjust = 0) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("Second batch Heritability by qtl2 array")
p2
dev.off()
# png
# 2
p2
#plot heritability by GCTA second batch
hsq.gcta <- list()
for (i in pheno.name){
hsq.gcta[[i]] <- read.table(file = paste0("data/FinalReport/GCTA/opioid_second_batch_",i,".out.hsq"),sep = "\t", header = FALSE,fill = TRUE, stringsAsFactors = FALSE)
}
h <- data.frame(
Phenotype = pheno.name,
Heritability = as.numeric(as.vector(unlist(lapply(hsq.gcta, FUN = function(x){x[5,2]})))),
Sample_size = as.numeric(as.vector(unlist(lapply(hsq.gcta, FUN = function(x){x[11,2]})))),
# Domain = sub("\\..*", "", phe.name),
stringsAsFactors = FALSE)
h <- h %>%
arrange(desc(Heritability))
h$Phenotype <- factor(h$Phenotype, levels = h$Phenotype)
h$Heritability <- round(h$Heritability,3)
#histgram
pdf(file = paste0("data/FinalReport/GCTA/DO_second_batch","_heritability_by_GCTA.pdf"), height = 10, width = 10)
p <- ggplot(data=h, aes(x=Phenotype, y=Heritability)) + #, fill=Domain, color = Domain)) +
geom_bar(stat="identity", fill = "blue", color = "blue", show.legend = FALSE) +
scale_y_continuous(breaks=seq(0.0, 1.0, 0.1)) +
geom_text(aes(label = Heritability, y = Heritability + 0.005), position = position_dodge(0.9),vjust = 0) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("Second batch Heritability by GCTA")
p
dev.off()
# png
# 2
p
sessionInfo()
# R version 4.0.3 (2020-10-10)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Ubuntu 20.04.2 LTS
#
# Matrix products: default
# BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
#
# locale:
# [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
# [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
# [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
# [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
# [9] LC_ADDRESS=C LC_TELEPHONE=C
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#
# attached base packages:
# [1] parallel stats graphics grDevices utils datasets methods
# [8] base
#
# other attached packages:
# [1] qtl_1.47-9 patchwork_1.1.1 furrr_0.2.2 future_1.21.0
# [5] cowplot_1.1.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.4
# [9] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.6
# [13] tidyverse_1.3.0 openxlsx_4.2.3 abind_1.4-5 regress_1.3-21
# [17] survival_3.2-7 qtl2_0.24 GGally_2.1.0 gridExtra_2.3
# [21] ggplot2_3.3.3 workflowr_1.6.2
#
# loaded via a namespace (and not attached):
# [1] fs_1.5.0 lubridate_1.7.9.2 bit64_4.0.5 RColorBrewer_1.1-2
# [5] httr_1.4.2 rprojroot_2.0.2 tools_4.0.3 backports_1.2.1
# [9] R6_2.5.0 DBI_1.1.1 colorspace_2.0-0 withr_2.4.1
# [13] tidyselect_1.1.0 bit_4.0.4 compiler_4.0.3 git2r_0.28.0
# [17] cli_2.3.0 rvest_0.3.6 xml2_1.3.2 scales_1.1.1
# [21] digest_0.6.27 rmarkdown_2.6 pkgconfig_2.0.3 htmltools_0.5.1.1
# [25] parallelly_1.23.0 highr_0.8 dbplyr_2.1.0 fastmap_1.1.0
# [29] rlang_0.4.10 readxl_1.3.1 rstudioapi_0.13 RSQLite_2.2.3
# [33] farver_2.0.3 generics_0.1.0 jsonlite_1.7.2 zip_2.1.1
# [37] magrittr_2.0.1 Matrix_1.2-18 Rcpp_1.0.6 munsell_0.5.0
# [41] lifecycle_1.0.0 stringi_1.5.3 whisker_0.4 yaml_2.2.1
# [45] plyr_1.8.6 grid_4.0.3 blob_1.2.1 listenv_0.8.0
# [49] promises_1.2.0.1 crayon_1.4.1 lattice_0.20-41 haven_2.3.1
# [53] splines_4.0.3 hms_1.0.0 knitr_1.31 pillar_1.4.7
# [57] codetools_0.2-18 reprex_1.0.0 glue_1.4.2 evaluate_0.14
# [61] data.table_1.13.6 modelr_0.1.8 vctrs_0.3.6 httpuv_1.5.5
# [65] cellranger_1.1.0 gtable_0.3.0 reshape_0.8.8 assertthat_0.2.1
# [69] cachem_1.0.4 xfun_0.21 broom_0.7.4 later_1.1.0.1
# [73] memoise_2.0.0 globals_0.14.0 ellipsis_0.3.1This R Markdown site was created with workflowr