Last updated: 2023-02-21
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Knit directory: DO_Opioid/
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Unstaged changes:
Modified: _workflowr.yml
Modified: analysis/marker_violin.Rmd
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Plot_DO_Fentanyl_Cohort2_mapping.Rmd
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File | Version | Author | Date | Message |
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Rmd | 2200f95 | xhyuo | 2023-02-21 | update with sex specific Fentanyl plot |
html | 8611541 | xhyuo | 2023-02-21 | Build site. |
Rmd | d1a7028 | xhyuo | 2023-02-21 | update with sex specific Fentanyl |
html | fd4dc88 | xhyuo | 2023-01-11 | Build site. |
Rmd | aff7cd2 | xhyuo | 2023-01-11 | update with histogram |
html | 6eeaa6a | xhyuo | 2023-01-06 | Build site. |
Rmd | 6d89b79 | xhyuo | 2023-01-06 | update with histogram and gcta h2, figure size |
html | fe30eec | xhyuo | 2023-01-06 | Build site. |
Rmd | 15a2b6f | xhyuo | 2023-01-06 | update with histogram and gcta h2 |
html | b77b34e | xhyuo | 2022-06-29 | Build site. |
Rmd | 3f07442 | xhyuo | 2022-06-29 | Plot_DO_Fentanyl_Cohort2 |
html | 7faf7f1 | xhyuo | 2022-06-29 | Build site. |
Rmd | e9fb99f | xhyuo | 2022-06-29 | Fentanyl_Cohort2 |
Last update: 2023-02-21
library(ggplot2)
library(gridExtra)
library(GGally)
library(parallel)
library(qtl2)
library(parallel)
library(survival)
library(regress)
library(abind)
library(openxlsx)
library(tidyverse)
rz.transform <- function(y) {
rankY=rank(y, ties.method="average", na.last="keep")
rzT=qnorm(rankY/(length(na.exclude(rankY))+1))
return(rzT)
}
load("code/do.colors.RData")
# Read phenotype data -----------------------------------------------------
#batch 20220502
do.pheno <- readxl::read_excel(path = "data/Composite Post Kevins Program Group 2 Fentanyl Prepped for Hao.xlsx",
col_types = c("text", "text", "text", "date", "numeric", "numeric", "date", rep("numeric", 9), "date"))
# Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
# Expecting date in D263 / R263C4: got 'BLANK'
# Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
# Expecting numeric in F263 / R263C6: got 'BLANK'
do.pheno <- do.pheno %>%
dplyr::rename_with(., ~str_remove_all(.x, " ")) %>%
dplyr::filter(!is.na(MouseID)) %>%
dplyr::mutate(Status_bin = case_when(
EarTag == "DEAD" ~ 0,
is.na(EarTag) ~ 1
)) %>%
as.data.frame()
# Read genotype data -----------------------------------------------------------
load("data/Jackson_Lab_Bubier_MURGIGV01/gm_DO765_qc_newid.RData")#gm_after_qc
overlap.id = intersect(ind_ids(gm_after_qc), as.character(do.pheno$MouseID))
#subset
gm = gm_after_qc[overlap.id, ]
do.pheno = do.pheno[do.pheno$MouseID %in% overlap.id, ]
rownames(do.pheno) = do.pheno$MouseID
do.pheno = do.pheno[overlap.id,]
all.equal(ind_ids(gm), do.pheno$MouseID)
# [1] TRUE
#covar
do.pheno$Sex <- as.factor(do.pheno$Sex)
#boxplot on the raw data------------
for(i in 8:16){
df <- do.pheno[,c(1, 3, i)]
df <- df[complete.cases(df), ]
p <- ggplot(df, aes(x=Sex, y=df[,3], group = Sex, fill = Sex, alpha = 0.9)) +
geom_boxplot(show.legend = F , outlier.size = 1.5, notchwidth = 0.85) +
geom_jitter(color="black", size=0.8, alpha=0.9) +
scale_fill_brewer(palette="Blues") +
ylab(paste0(colnames(df)[3])) +
xlab("Sex") +
labs(fill = "") +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
text = element_text(size=21),
axis.title=element_text(size=21)) +
guides(shape = guide_legend(override.aes = list(size = 12)))
print(p)
}
#histogram
for(i in c(8:10, 12:16)){
df <- do.pheno[,c(1, 3, i)]
p <- ggplot(data=df, aes(x=df[,3])) +
geom_histogram() +
ylab("Number of DO mice") +
xlab(paste0(colnames(df)[3])) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=21))
print(p)
}
# Warning: Removed 132 rows containing non-finite values (stat_bin).
# Warning: Removed 48 rows containing non-finite values (stat_bin).
# Warning: Removed 48 rows containing non-finite values (stat_bin).
# Warning: Removed 187 rows containing non-finite values (stat_bin).
# Warning: Removed 187 rows containing non-finite values (stat_bin).
# Warning: Removed 187 rows containing non-finite values (stat_bin).
# Warning: Removed 187 rows containing non-finite values (stat_bin).
# Warning: Removed 187 rows containing non-finite values (stat_bin).
#for "MinDepressionRR(%)"
i = 11
df <- do.pheno[,c(1, 3, i)]
p <- ggplot(data=df, aes(x=df[,3])) +
geom_histogram() +
ylab("Number of DO mice") +
xlab(paste0(colnames(df)[3])) +
xlim(0.4, 1) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=21))
print(p)
# Warning: Removed 48 rows containing non-finite values (stat_bin).
# Warning: Removed 2 rows containing missing values (geom_bar).
#boxplot on the rankz data------------
for(i in 8:16){
df <- do.pheno[,c(1, 3, i)]
df <- df[complete.cases(df), ]
p <- ggplot(df, aes(x=Sex, y= rz.transform(df[,3]), group = Sex, fill = Sex, alpha = 0.9)) +
geom_boxplot(show.legend = F , outlier.size = 1.5, notchwidth = 0.85) +
geom_jitter(color="black", size=0.8, alpha=0.9) +
scale_fill_brewer(palette="Blues") +
ylab(paste0("rz.transformed ", colnames(df)[3])) +
xlab("Sex") +
labs(fill = "") +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
text = element_text(size=21),
axis.title=element_text(size=21)) +
guides(shape = guide_legend(override.aes = list(size = 12)))
print(p)
}
#histogram
for(i in 8:16){
df <- do.pheno[,c(1, 3, i)]
p <- ggplot(data=df, aes(x=rz.transform(df[,3]))) +
geom_histogram() +
ylab("Number of DO mice") +
xlab(paste0("rz.transformed ", colnames(df)[3])) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=21))
print(p)
}
# Warning: Removed 132 rows containing non-finite values (stat_bin).
# Warning: Removed 48 rows containing non-finite values (stat_bin).
# Warning: Removed 48 rows containing non-finite values (stat_bin).
# Warning: Removed 48 rows containing non-finite values (stat_bin).
# Warning: Removed 187 rows containing non-finite values (stat_bin).
# Warning: Removed 187 rows containing non-finite values (stat_bin).
# Warning: Removed 187 rows containing non-finite values (stat_bin).
# Warning: Removed 187 rows containing non-finite values (stat_bin).
# Warning: Removed 187 rows containing non-finite values (stat_bin).
Plot by using its output qtl.do_Fentanyl_Cohort2.out.RData
load("output/Fentanyl/qtl.do_Fentanyl_Cohort2.out.RData")
#pmap and gmap
chr.names <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19", "X")
gmap <- list()
pmap <- list()
for (chr in chr.names){
gmap[[chr]] <- gm$gmap[[chr]]
pmap[[chr]] <- gm$pmap[[chr]]
}
attr(gmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
attr(pmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
#genome-wide plot
for(i in names(qtl.morphine.out)){
par(mar=c(5.1, 4.1, 1.1, 1.1))
ymx <- maxlod(qtl.morphine.out[[i]]) # overall maximum LOD score
plot(qtl.morphine.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, 10))
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
title(main = paste0("do_Fentanyl_Cohort2_",i))
}
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pdf(file = paste0("output/Fentanyl/do_Fentanyl_Cohort2.pdf"), width = 16, height =8)
#save genome-wide plo
for(i in names(qtl.morphine.out)){
par(mar=c(5.1, 4.1, 1.1, 1.1))
ymx <- maxlod(qtl.morphine.out[[i]]) # overall maximum LOD score
plot(qtl.morphine.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, 10))
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
title(main = paste0("do_Fentanyl_Cohort2_",i))
}
dev.off()
# png
# 2
#peaks coeff plot
for(i in names(qtl.morphine.out)){
print(i)
peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
if(nrow(peaks) > 0) {
print(peaks)
for(p in 1:dim(peaks)[1]) {
print(p)
chr <-peaks[p,3]
#coeff plot
par(mar=c(4.1, 4.1, 0.6, 0.6))
plot_coefCC(coef_c1[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
}
}
}
# [1] "TimetoDead(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 2 132.62489 7.067034 106.68530 133.3807
# 2 1 pheno1 3 100.24885 6.239943 80.21006 101.0701
# 3 1 pheno1 5 96.36969 6.019079 50.65575 147.9508
# 4 1 pheno1 13 91.52025 6.147871 48.80519 117.2887
# [1] 1
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# [1] "RRDepressionRate(%/Hr)SLOPE"
# [1] "TimetoSteadyRRDepression(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 5 120.68390 6.483622 118.27549 121.58096
# 2 1 pheno1 17 52.79771 7.779978 52.16947 53.54382
# 3 1 pheno1 18 63.05026 6.127351 49.68747 63.25613
# [1] 1
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# [1] "MinDepressionRR(%)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 8 14.56268 6.752336 14.38322 14.60657
# [1] 1
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# [1] "Steady-StateDepressionDuration(Hr)INTERVAL"
# [1] "TimetoThresholdRecovery(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 1 91.31201 6.658992 91.21626 153.19382
# 2 1 pheno1 8 92.31720 6.295785 14.56005 109.54470
# 3 1 pheno1 13 53.62165 7.712830 53.57538 54.08557
# 4 1 pheno1 15 89.24166 6.222582 60.56487 90.52322
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# [1] "TimetoProjectedRecovery(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 1 152.43451 6.138085 91.21626 153.19382
# 2 1 pheno1 8 92.31720 6.032551 14.55032 92.73082
# 3 1 pheno1 13 53.62165 6.969155 53.57538 53.97165
# 4 1 pheno1 18 66.14084 6.606701 65.31382 66.30142
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# [1] "StartofRecovery(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 13 53.62165 6.12149 53.57538 54.62068
# [1] 1
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
# [1] "RRRecoveryRate(%/Hr)SLOPE"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 6 115.70560 6.308902 115.43247 116.28985
# 2 1 pheno1 10 68.11376 6.195341 66.55149 68.20195
# 3 1 pheno1 15 90.42378 6.347611 88.92565 90.85895
# 4 1 pheno1 18 65.52797 6.640763 65.36123 65.58896
# [1] 1
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
# [1] 2
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
# [1] 3
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
# [1] 4
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
# [1] "Status_bin"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 11 56.51261 6.370706 28.86938 56.80903
# [1] 1
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
Version | Author | Date |
---|---|---|
7faf7f1 | xhyuo | 2022-06-29 |
#save peaks coeff plot
for(i in names(qtl.morphine.out)){
print(i)
peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
if(nrow(peaks) > 0){
fname <- paste("output/DO_Fentanyl_Cohort2_", str_replace_all(i, "[[:punct:]]", "") ,"_coefplot.pdf",sep="")
pdf(file = fname, width = 16, height =8)
for(p in 1:dim(peaks)[1]) {
print(p)
chr <-peaks[p,3]
#coeff plot
par(mar=c(4.1, 4.1, 0.6, 0.6))
plot_coefCC(coef_c1[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
}
dev.off()
}
}
# [1] "TimetoDead(Hr)"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] "RRDepressionRate(%/Hr)SLOPE"
# [1] "TimetoSteadyRRDepression(Hr)"
# [1] 1
# [1] 2
# [1] 3
# [1] "MinDepressionRR(%)"
# [1] 1
# [1] "Steady-StateDepressionDuration(Hr)INTERVAL"
# [1] "TimetoThresholdRecovery(Hr)"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] "TimetoProjectedRecovery(Hr)"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] "StartofRecovery(Hr)"
# [1] 1
# [1] "RRRecoveryRate(%/Hr)SLOPE"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] "Status_bin"
# [1] 1
#save peaks coeff blup plot
for(i in names(qtl.morphine.out)){
print(i)
peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
if(nrow(peaks) > 0) {
fname <- paste("output/DO_Fentanyl_Cohort2_", str_replace_all(i, "[[:punct:]]", "") ,"_coefplot_blup.pdf",sep="")
pdf(file = fname, width = 16, height =8)
for(p in 1:dim(peaks)[1]) {
print(p)
chr <-peaks[p,3]
#coeff plot
par(mar=c(4.1, 4.1, 0.6, 0.6))
plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
}
dev.off()
}
}
# [1] "TimetoDead(Hr)"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] "RRDepressionRate(%/Hr)SLOPE"
# [1] "TimetoSteadyRRDepression(Hr)"
# [1] 1
# [1] 2
# [1] 3
# [1] "MinDepressionRR(%)"
# [1] 1
# [1] "Steady-StateDepressionDuration(Hr)INTERVAL"
# [1] "TimetoThresholdRecovery(Hr)"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] "TimetoProjectedRecovery(Hr)"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] "StartofRecovery(Hr)"
# [1] 1
# [1] "RRRecoveryRate(%/Hr)SLOPE"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] "Status_bin"
# [1] 1
#plot heritability by qtl2 array --------------
herit <- data.frame(Phenotype = names(unlist(qtl.morphine.hsq)),
Heritability = round(unlist(qtl.morphine.hsq),3))
herit <- herit %>%
arrange(desc(Heritability))
herit$Phenotype <- factor(herit$Phenotype, levels = herit$Phenotype)
#histgram
p2 <- ggplot(data=herit, aes(x=Phenotype, y=Heritability)) + #, fill=Domain, color = Domain)) +
geom_bar(stat="identity", fill = "blue", color = "blue", show.legend = FALSE) +
scale_y_continuous(breaks=seq(0.0, 1.0, 0.1)) +
geom_text(aes(label = Heritability, y = Heritability + 0.005), position = position_dodge(0.9),vjust = 0) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("DO Fentanyl_Cohort2 Heritability by qtl2 array")
p2
#plot heritability by GCTA
h <- read.csv("data/FinalReport/GCTA/DO_Fentanyl_Cohort2/DO_Fentanyl_Cohort2_heritability_by_GCTA.csv", header = TRUE)
h <- h %>%
arrange(desc(Heritability))
h$Phenotype <- factor(h$Phenotype, levels = h$Phenotype)
h$Heritability <- round(h$Heritability,2)
#histgram
pdf(file = paste0("data/FinalReport/GCTA/DO_Fentanyl_Cohort2/DO_Fentanyl_Cohort2","_heritability_by_GCTA.pdf"), height = 10, width = 10)
p3<-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("Heritability by GCTA")
p3
dev.off()
# png
# 2
# png
# 2
p3
load("output/Fentanyl/qtl.do_Fentanyl_Cohort2.out.male.RData")
#pmap and gmap
chr.names <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19", "X")
gmap <- list()
pmap <- list()
for (chr in chr.names){
gmap[[chr]] <- gm$gmap[[chr]]
pmap[[chr]] <- gm$pmap[[chr]]
}
attr(gmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
attr(pmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
#genome-wide plot
for(i in names(qtl.morphine.out)){
par(mar=c(5.1, 4.1, 1.1, 1.1))
ymx <- maxlod(qtl.morphine.out[[i]]) # overall maximum LOD score
plot(qtl.morphine.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, max(sapply(qtl.morphine.out, maxlod)) +2))
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
title(main = paste0("do_Fentanyl_Cohort2_",i))
}
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
#peaks coeff plot
for(i in names(qtl.morphine.out)){
print(i)
peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
if(nrow(peaks) > 0) {
print(peaks)
for(p in 1:dim(peaks)[1]) {
print(p)
chr <-peaks[p,3]
#coeff plot
par(mar=c(4.1, 4.1, 0.6, 0.6))
plot_coefCC(coef_c1[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
}
}
}
# [1] "TimetoDead(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 2 132.62489 8.135472 131.88985 132.94007
# 2 1 pheno1 3 80.46385 6.635020 73.35207 81.19227
# 3 1 pheno1 5 13.42743 6.684415 10.86410 14.21411
# 4 1 pheno1 9 13.71424 6.527954 13.12685 15.44563
# [1] 1
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 2
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 3
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 4
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] "RRDepressionRate(%/Hr)SLOPE"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 3 32.58987 6.984733 32.379800 33.07485
# 2 1 pheno1 X 10.46006 7.122931 9.261486 53.15896
# [1] 1
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 2
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] "TimetoSteadyRRDepression(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 2 161.6875 6.026396 95.37519 161.87139
# 2 1 pheno1 17 52.1811 6.074600 36.63825 53.54382
# [1] 1
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 2
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] "MinDepressionRR(%)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 2 168.30118 6.308975 167.55191 171.64499
# 2 1 pheno1 13 89.64923 6.299108 52.44224 90.31856
# [1] 1
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 2
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] "Steady-StateDepressionDuration(Hr)INTERVAL"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 3 90.66061 8.142433 90.57609 90.70492
# 2 1 pheno1 4 100.75953 7.510841 99.32926 100.79381
# 3 1 pheno1 8 83.45834 6.695546 83.35490 84.22023
# 4 1 pheno1 9 123.42827 6.428620 116.93394 123.85184
# 5 1 pheno1 10 66.55650 6.997621 64.45014 69.65303
# 6 1 pheno1 12 75.98747 7.912513 75.95610 94.21761
# 7 1 pheno1 13 93.91029 6.097764 42.38805 95.63661
# [1] 1
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 2
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 3
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 4
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 5
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 6
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 7
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] "TimetoThresholdRecovery(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 2 80.18535 6.172261 65.91339 181.81225
# 2 1 pheno1 12 75.98747 7.498411 75.94321 75.99396
# [1] 1
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 2
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] "TimetoProjectedRecovery(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 2 80.18535 6.137005 65.91339 180.94469
# 2 1 pheno1 3 90.66241 7.109982 89.60391 104.52824
# 3 1 pheno1 4 14.49143 6.166787 13.87348 153.99966
# 4 1 pheno1 6 110.87154 6.105875 23.39815 111.51060
# 5 1 pheno1 10 66.55650 6.203671 33.86040 67.04043
# 6 1 pheno1 12 91.62691 6.136398 75.62742 94.37674
# 7 1 pheno1 15 83.10382 6.748883 83.06880 83.13036
# [1] 1
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 2
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 3
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 4
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 5
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 6
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 7
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] "StartofRecovery(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 3 90.66061 8.542034 90.57609 90.70492
# 2 1 pheno1 4 100.55380 7.820399 99.32926 100.79381
# 3 1 pheno1 8 83.45834 6.600689 70.15607 84.22023
# 4 1 pheno1 9 123.42827 6.126531 116.90024 123.85184
# 5 1 pheno1 10 65.71837 7.117583 64.45014 66.60381
# 6 1 pheno1 12 75.98747 8.039338 75.95610 94.18281
# 7 1 pheno1 13 95.62986 6.411262 43.57533 95.63661
# [1] 1
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 2
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 3
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 4
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 5
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
# [1] 6
Version | Author | Date |
---|---|---|
8611541 | xhyuo | 2023-02-21 |
Version | Author | Date |
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8611541 | xhyuo | 2023-02-21 |
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# [1] 7
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# [1] "RRRecoveryRate(%/Hr)SLOPE"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 1 192.12423 6.708234 191.931595 193.43806
# 2 1 pheno1 2 30.42672 7.319624 30.269862 30.50527
# 3 1 pheno1 3 152.71526 6.460920 50.440226 153.14209
# 4 1 pheno1 9 54.54239 6.189928 6.239837 55.29730
# 5 1 pheno1 15 87.57068 6.600476 84.049458 90.51963
# [1] 1
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# [1] 2
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# [1] 3
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# [1] 4
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8611541 | xhyuo | 2023-02-21 |
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# [1] 5
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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# [1] "Status_bin"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 7 49.25215 9.875931 46.83042 49.37829
# 2 1 pheno1 9 53.68682 6.144273 52.14281 119.66040
# 3 1 pheno1 19 53.55175 6.642364 53.11032 53.65224
# [1] 1
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# [1] 2
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# [1] 3
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load("output/Fentanyl/qtl.do_Fentanyl_Cohort2.out.female.RData")
#pmap and gmap
chr.names <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19", "X")
gmap <- list()
pmap <- list()
for (chr in chr.names){
gmap[[chr]] <- gm$gmap[[chr]]
pmap[[chr]] <- gm$pmap[[chr]]
}
attr(gmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
attr(pmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
#genome-wide plot
for(i in names(qtl.morphine.out)){
par(mar=c(5.1, 4.1, 1.1, 1.1))
ymx <- maxlod(qtl.morphine.out[[i]]) # overall maximum LOD score
plot(qtl.morphine.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, max(sapply(qtl.morphine.out, maxlod)) +2))
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
title(main = paste0("do_Fentanyl_Cohort2_",i))
}
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#peaks coeff plot
for(i in names(qtl.morphine.out)){
print(i)
peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
if(nrow(peaks) > 0) {
print(peaks)
for(p in 1:dim(peaks)[1]) {
print(p)
chr <-peaks[p,3]
#coeff plot
par(mar=c(4.1, 4.1, 0.6, 0.6))
plot_coefCC(coef_c1[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
}
}
}
# [1] "TimetoDead(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 2 35.93828 6.395693 14.71230 53.73635
# 2 1 pheno1 7 29.33386 7.380186 28.72162 107.32907
# 3 1 pheno1 9 29.84406 6.460520 28.55111 64.47467
# 4 1 pheno1 10 93.92129 6.138204 26.38250 94.50163
# 5 1 pheno1 X 45.99921 6.955609 44.92816 136.45503
# [1] 1
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# [1] 2
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# [1] 3
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] 4
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8611541 | xhyuo | 2023-02-21 |
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# [1] 5
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# [1] "RRDepressionRate(%/Hr)SLOPE"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 5 34.10212 6.494372 33.30631 34.38771
# 2 1 pheno1 12 110.17735 7.475173 109.51204 112.34613
# 3 1 pheno1 15 99.39531 6.125827 26.44955 101.12575
# [1] 1
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# [1] 2
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] 3
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] "TimetoSteadyRRDepression(Hr)"
# [1] "MinDepressionRR(%)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 5 147.27839 6.273736 67.23641 147.43417
# 2 1 pheno1 6 52.83155 6.076547 13.21830 53.95921
# 3 1 pheno1 12 110.14878 6.256748 90.07443 110.17562
# 4 1 pheno1 18 41.80133 6.871462 41.74898 43.29028
# [1] 1
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# [1] 2
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# [1] 3
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# [1] 4
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] "Steady-StateDepressionDuration(Hr)INTERVAL"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 6 126.017905 6.048217 125.696341 136.70757
# 2 1 pheno1 8 92.203842 6.030778 16.080653 124.33745
# 3 1 pheno1 10 5.635019 6.925830 3.400722 115.81149
# 4 1 pheno1 18 65.434900 6.977069 65.313821 65.52767
# [1] 1
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# [1] 2
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# [1] 3
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# [1] 4
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] "TimetoThresholdRecovery(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 1 91.312012 6.278565 91.216258 152.86541
# 2 1 pheno1 6 126.017905 6.903066 125.696341 126.63864
# 3 1 pheno1 10 5.635019 6.436675 3.174515 115.81149
# 4 1 pheno1 18 65.438329 7.680994 65.313821 65.52797
# [1] 1
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# [1] 2
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8611541 | xhyuo | 2023-02-21 |
# [1] 3
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8611541 | xhyuo | 2023-02-21 |
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# [1] 4
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] "TimetoProjectedRecovery(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 1 91.31201 6.270337 91.209902 152.86541
# 2 1 pheno1 6 126.01790 6.709756 125.696341 126.61414
# 3 1 pheno1 10 115.79817 6.081346 3.174515 115.81494
# 4 1 pheno1 11 35.91853 6.293340 35.844802 35.99807
# 5 1 pheno1 18 65.43490 7.710831 65.313821 65.52767
# [1] 1
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# [1] 2
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] 3
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] 4
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] 5
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] "StartofRecovery(Hr)"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 10 5.635019 6.852134 3.400722 115.81149
# 2 1 pheno1 18 65.434900 6.749689 65.313821 65.52767
# [1] 1
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] 2
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] "RRRecoveryRate(%/Hr)SLOPE"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 4 152.69816 6.100750 68.40926 152.83582
# 2 1 pheno1 5 13.65223 6.943854 13.42743 54.23998
# 3 1 pheno1 9 122.67218 7.020565 122.31448 123.93203
# 4 1 pheno1 12 113.10511 6.093534 112.53083 113.14986
# 5 1 pheno1 16 33.09290 7.445865 32.54616 92.27653
# [1] 1
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] 2
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] 3
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] 4
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] 5
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
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8611541 | xhyuo | 2023-02-21 |
# [1] "Status_bin"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 1 149.3413 6.686633 145.0415 150.4074
# [1] 1
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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] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4
# [5] readr_1.4.0 tidyr_1.1.2 tibble_3.0.6 tidyverse_1.3.0
# [9] openxlsx_4.2.3 abind_1.4-5 regress_1.3-21 survival_3.2-7
# [13] qtl2_0.24 GGally_2.1.0 gridExtra_2.3 ggplot2_3.3.3
# [17] workflowr_1.6.2
#
# loaded via a namespace (and not attached):
# [1] httr_1.4.2 bit64_4.0.5 jsonlite_1.7.2 splines_4.0.3
# [5] modelr_0.1.8 assertthat_0.2.1 highr_0.8 blob_1.2.1
# [9] cellranger_1.1.0 yaml_2.2.1 pillar_1.4.7 RSQLite_2.2.3
# [13] backports_1.2.1 lattice_0.20-41 glue_1.4.2 digest_0.6.27
# [17] RColorBrewer_1.1-2 promises_1.2.0.1 rvest_0.3.6 colorspace_2.0-0
# [21] htmltools_0.5.1.1 httpuv_1.5.5 Matrix_1.3-2 plyr_1.8.6
# [25] pkgconfig_2.0.3 broom_0.7.4 haven_2.3.1 scales_1.1.1
# [29] whisker_0.4 later_1.1.0.1 git2r_0.28.0 farver_2.0.3
# [33] generics_0.1.0 ellipsis_0.3.1 cachem_1.0.4 withr_2.4.1
# [37] cli_2.3.0 magrittr_2.0.1 crayon_1.4.1 readxl_1.3.1
# [41] memoise_2.0.0 evaluate_0.14 fs_1.5.0 xml2_1.3.2
# [45] tools_4.0.3 data.table_1.13.6 hms_1.0.0 lifecycle_1.0.0
# [49] reprex_1.0.0 munsell_0.5.0 zip_2.1.1 compiler_4.0.3
# [53] rlang_1.0.2 grid_4.0.3 rstudioapi_0.13 labeling_0.4.2
# [57] rmarkdown_2.6 gtable_0.3.0 DBI_1.1.1 reshape_0.8.8
# [61] R6_2.5.0 lubridate_1.7.9.2 knitr_1.31 fastmap_1.1.0
# [65] bit_4.0.4 rprojroot_2.0.2 stringi_1.5.3 Rcpp_1.0.6
# [69] vctrs_0.3.6 dbplyr_2.1.0 tidyselect_1.1.0 xfun_0.21
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