Plot_DO_fentanyl_alternate_metrics

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Last updated: 2021-12-20

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Knit directory: DO_Opioid/

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    Modified:   analysis/marker_violin.Rmd

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File	Version	Author	Date	Message
Rmd	344b6bc	xhyuo	2021-12-20	Plot_DO_fentanyl_alternate_metrics
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Rmd	a90242b	xhyuo	2021-12-20	Plot_DO_fentanyl_alternate_metrics.Rmd

---

Last update: 2021-12-20

##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())

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")

###Read phenotype data

do.pheno <- read.xlsx("data/Master Fentanyl DO Study Sheet.xlsx", sheet = 1,
                      rows = 1:399, na.strings = "NaN", detectDates = TRUE)
#second 31009 is really 31109
do.pheno[do.pheno$ID == 31009 & do.pheno$File == 20210519, "ID"] = 31109
#remove zombie mice
do.pheno <- do.pheno %>%
  dplyr::filter(is.na(Censor)) %>%
  dplyr::rename(.,
                Min.depression = 9,
                Survival.Time  = 10,
                Recovery.Time  = 11) %>%
  dplyr::mutate(Status_bin = ifelse(Survival == "ALIVE", 1, 0)) %>%
  dplyr::mutate(ID = as.character(ID)) %>%
  dplyr::arrange(ID, desc(File)) %>%
  dplyr::distinct(ID, .keep_all = TRUE)
#Fentanyl_alternate_metrics.xlsx
do.pheno.alt <- read.xlsx("data/Fentanyl_alternate_metrics.xlsx", sheet = 1,
                      na.strings = "NaN") %>%
  mutate(ID = as.character(Mouse.ID)) %>%
  filter(!(if_all(2:11, is.na))) %>%
  distinct(ID, .keep_all = TRUE)
colnames(do.pheno.alt) <- str_replace(colnames(do.pheno.alt), "\\(", "")
colnames(do.pheno.alt) <- str_replace(colnames(do.pheno.alt), "\\)", "")
colnames(do.pheno.alt) <- str_replace(colnames(do.pheno.alt), "\\%/", "")
colnames(do.pheno.alt) <- str_replace(colnames(do.pheno.alt), "\\.%", "")
#get sex and dob
do.pheno <- left_join(do.pheno.alt, do.pheno[, 2:4])
# Read genotype data -----------------------------------------------------------
load("data/Jackson_Lab_Bubier_MURGIGV01/gm_DO395_qc_newid.RData")#gm_after_qc
overlap.id  = intersect(ind_ids(gm_after_qc), do.pheno$ID)

#subset
gm = gm_after_qc[overlap.id, ]
do.pheno = do.pheno[do.pheno$ID %in% overlap.id, ]
rownames(do.pheno) = do.pheno$ID
do.pheno = do.pheno[overlap.id,]
all.equal(ind_ids(gm), do.pheno$ID)
# [1] TRUE

#covar
do.pheno$Sex <- as.factor(do.pheno$Sex)
do.pheno$dob <- as.factor(do.pheno$dob)
#addcovar
options(na.action='na.pass')
addcovar = model.matrix(~Sex + dob, data = do.pheno[, c("Sex", "dob")])[,-1]
colnames(addcovar) <- c("sex", "dob")

#histgram for raw phenotypes------------
do.pheno %>% 
     select(2:11) %>% 
     map2(.,.y = colnames(.), ~ ggplot(do.pheno, aes(x = .x)) + 
               geom_histogram() +
               xlab(.y)
     ) %>% 
     wrap_plots( ncol = 3)
# Warning: Removed 204 rows containing non-finite values (stat_bin).
# Warning: Removed 37 rows containing non-finite values (stat_bin).

# Warning: Removed 37 rows containing non-finite values (stat_bin).

# Warning: Removed 37 rows containing non-finite values (stat_bin).
# Warning: Removed 72 rows containing non-finite values (stat_bin).
# Warning: Removed 282 rows containing non-finite values (stat_bin).

# Warning: Removed 282 rows containing non-finite values (stat_bin).

# Warning: Removed 282 rows containing non-finite values (stat_bin).

# Warning: Removed 282 rows containing non-finite values (stat_bin).

# Warning: Removed 282 rows containing non-finite values (stat_bin).

Past versions of “Read phenotype data-1.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

#histgram for rankz phenotypes------------
do.pheno %>% 
     select(2:11) %>% 
     map2(.,.y = colnames(.), ~ ggplot(do.pheno, aes(x = rz.transform(.x))) + 
               geom_histogram() +
               xlab(.y)
     ) %>% 
     wrap_plots( ncol = 3)
# Warning: Removed 204 rows containing non-finite values (stat_bin).
# Warning: Removed 37 rows containing non-finite values (stat_bin).

# Warning: Removed 37 rows containing non-finite values (stat_bin).

# Warning: Removed 37 rows containing non-finite values (stat_bin).
# Warning: Removed 72 rows containing non-finite values (stat_bin).
# Warning: Removed 282 rows containing non-finite values (stat_bin).

# Warning: Removed 282 rows containing non-finite values (stat_bin).

# Warning: Removed 282 rows containing non-finite values (stat_bin).

# Warning: Removed 282 rows containing non-finite values (stat_bin).

# Warning: Removed 282 rows containing non-finite values (stat_bin).

Past versions of “Read phenotype data-2.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

###Plot qtl mapping on array

load("output/Fentanyl/qtl.fentanyl.array.alternate.metrics.out.RData")

#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=gm$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_",i))
}

#save genome-wide plot
for(i in names(qtl.morphine.out)){
  png(file = paste0("output/Fentanyl/DO_fentanyl_array_alternate_metrics_", i, ".png"), width = 16, height =8, res=300, units = "in")
  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=gm$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_",i))
  dev.off()
}

#peaks coeff plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=gm$pmap, threshold=6, drop=1.5)
  print(peaks)
  if(dim(peaks)[1] != 0){
    for(p in 1:dim(peaks)[1]) {
      print(p)
      chr <-peaks[p,3]
      #coeff plot
      par(mar=c(4.1, 4.1, 0.6, 0.6))
      plot_coefCC(coef_c1[[i]][[p]], gm$pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
      plot_coefCC(coef_c2[[i]][[p]], gm$pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
      plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
    }
  }
}
# [1] "Time.to.Dead.Hr"
#   lodindex lodcolumn chr      pos      lod     ci_lo    ci_hi
# 1        1    pheno1   4 155.6289 7.273047  24.82559 155.8786
# 2        1    pheno1   5 130.9439 6.743946 130.42272 135.0373
# [1] 1

# [1] 2

# [1] "RR.Depression.Rate.Hr"
# [1] lodindex  lodcolumn chr       pos       lod       ci_lo     ci_hi    
# <0 rows> (or 0-length row.names)
# [1] "Time.to.Steady.RR.Depression.Hr"
# [1] lodindex  lodcolumn chr       pos       lod       ci_lo     ci_hi    
# <0 rows> (or 0-length row.names)
# [1] "Min.Depression.RR"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   6 110.23309 6.891327 109.74468 110.89903
# 2        1    pheno1  15  70.26574 6.600987  68.92851  82.99412
# [1] 1

# [1] 2

# [1] "Mean.Depression.BR"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   2 179.93697 6.667862 99.48509 180.36898
# 2        1    pheno1  15  28.43474 6.963138 27.40518  72.41165
# 3        1    pheno1  17  44.82475 6.772433 44.68776  45.38912
# [1] 1

# [1] 2

# [1] 3

# [1] "Steady-State.Depression.Duration.Hr"
#   lodindex lodcolumn chr      pos      lod     ci_lo    ci_hi
# 1        1    pheno1   4 135.9927 6.107796  32.37530 136.1999
# 2        1    pheno1   5 102.9928 7.087202 102.81969 103.3132
# 3        1    pheno1  11 100.0882 6.652753  99.89319 100.9355
# [1] 1

# [1] 2

# [1] 3

# [1] "Time.to.Threshold.Recovery.Hr"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   4 135.94204 6.415931 53.10764 136.06715
# 2        1    pheno1  14  20.50752 6.094258 20.26372  22.25158
# [1] 1

# [1] 2

# [1] "Time.to.Projected.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod   ci_lo    ci_hi
# 1        1    pheno1   5 103.1582 6.396816 102.756 103.3132
# [1] 1

# [1] "Start.of.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod     ci_lo    ci_hi
# 1        1    pheno1   5 102.9928 6.785795 102.81969 103.3132
# 2        1    pheno1  11 100.0882 6.838855  99.91068 100.9290
# [1] 1

# [1] 2

# [1] "RR.Recovery.Rate.Hr"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   3 123.39203 6.459037 122.42195 131.46627
# 2        1    pheno1  14  21.13855 8.271971  20.26372  21.51874
# 3        1    pheno1  15  68.04193 6.058226  68.00208  68.45217
# [1] 1

# [1] 2

# [1] 3

#save peaks coeff plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=gm$pmap, threshold=6, drop=1.5)
  fname <- paste("output/Fentanyl/DO_fentanyl_array_alternate_metrics_",i,"_coefplot.pdf",sep="")
  pdf(file = fname, width = 16, height =8)
  if(dim(peaks)[1] != 0){
    for(p in 1:dim(peaks)[1]) {
      print(p)
      chr <-peaks[p,3]
      #coeff plot
      par(mar=c(4.1, 4.1, 0.6, 0.6))
      plot_coefCC(coef_c1[[i]][[p]], gm$pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
      plot_coefCC(coef_c2[[i]][[p]], gm$pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
      plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
    }
  }
  dev.off()
}
# [1] "Time.to.Dead.Hr"
# [1] 1
# [1] 2
# [1] "RR.Depression.Rate.Hr"
# [1] "Time.to.Steady.RR.Depression.Hr"
# [1] "Min.Depression.RR"
# [1] 1
# [1] 2
# [1] "Mean.Depression.BR"
# [1] 1
# [1] 2
# [1] 3
# [1] "Steady-State.Depression.Duration.Hr"
# [1] 1
# [1] 2
# [1] 3
# [1] "Time.to.Threshold.Recovery.Hr"
# [1] 1
# [1] 2
# [1] "Time.to.Projected.Recovery.Hr"
# [1] 1
# [1] "Start.of.Recovery.Hr"
# [1] 1
# [1] 2
# [1] "RR.Recovery.Rate.Hr"
# [1] 1
# [1] 2
# [1] 3

#save peaks coeff blup plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=gm$pmap, threshold=6, drop=1.5)
  fname <- paste("output/Fentanyl/DO_fentanyl_array_alternate_metrics_",i,"_coefplot_blup.pdf",sep="")
  pdf(file = fname, width = 16, height =8)
    if(dim(peaks)[1] != 0){
      for(p in 1:dim(peaks)[1]) {
        print(p)
        chr <-peaks[p,3]
        #coeff plot
        par(mar=c(4.1, 4.1, 0.6, 0.6))
        plot_coefCC(coef_c1[[i]][[p]], gm$pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
        plot_coefCC(coef_c2[[i]][[p]], gm$pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
        plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
    }
  }
  dev.off()
}
# [1] "Time.to.Dead.Hr"
# [1] 1
# [1] 2
# [1] "RR.Depression.Rate.Hr"
# [1] "Time.to.Steady.RR.Depression.Hr"
# [1] "Min.Depression.RR"
# [1] 1
# [1] 2
# [1] "Mean.Depression.BR"
# [1] 1
# [1] 2
# [1] 3
# [1] "Steady-State.Depression.Duration.Hr"
# [1] 1
# [1] 2
# [1] 3
# [1] "Time.to.Threshold.Recovery.Hr"
# [1] 1
# [1] 2
# [1] "Time.to.Projected.Recovery.Hr"
# [1] 1
# [1] "Start.of.Recovery.Hr"
# [1] 1
# [1] 2
# [1] "RR.Recovery.Rate.Hr"
# [1] 1
# [1] 2
# [1] 3

###Plot qtl mapping on 69K

load("output/Fentanyl/qtl.fentanyl.69k.alternate.metrics.out.RData")
#pmap and gmap
load("data/69k_grid_pgmap.RData")

#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_",i))
}

Past versions of “Plot qtl mapping on 69K-1.png”

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f412738	xhyuo	2021-12-20

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#save genome-wide plot
for(i in names(qtl.morphine.out)){
  png(file = paste0("output/Fentanyl/DO_fentanyl_69k_alternate_metrics_", i, ".png"), width = 16, height =8, res=300, units = "in")
  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_",i))
  dev.off()
}

#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)
  print(peaks)
  if(dim(peaks)[1] != 0){
    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] "Time.to.Dead.Hr"
#   lodindex lodcolumn chr      pos      lod     ci_lo    ci_hi
# 1        1    pheno1   4 155.5686 6.872808  23.78901 155.8905
# 2        1    pheno1   5 130.9623 6.521364 130.41268 135.0559
# [1] 1

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# [1] 2

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# [1] "RR.Depression.Rate.Hr"
# [1] lodindex  lodcolumn chr       pos       lod       ci_lo     ci_hi    
# <0 rows> (or 0-length row.names)
# [1] "Time.to.Steady.RR.Depression.Hr"
# [1] lodindex  lodcolumn chr       pos       lod       ci_lo     ci_hi    
# <0 rows> (or 0-length row.names)
# [1] "Min.Depression.RR"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   6 110.25926 6.917845 109.73966 110.89937
# 2        1    pheno1  15  70.24917 6.600987  68.92043  83.02844
# [1] 1

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# [1] 2

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# [1] "Mean.Depression.BR"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   2 179.93579 6.666801 99.18788 180.36934
# 2        1    pheno1  15  28.42395 6.928838 27.40287  72.41888
# 3        1    pheno1  17  45.00725 6.614011 44.60730  45.45418
# [1] 1

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# [1] 2

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f412738	xhyuo	2021-12-20

# [1] 3

Past versions of “Plot qtl mapping on 69K-29.png”

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f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-30.png”

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f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-31.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] "Steady-State.Depression.Duration.Hr"
#   lodindex lodcolumn chr      pos      lod     ci_lo    ci_hi
# 1        1    pheno1   4 135.9546 6.166319  32.37278 136.3792
# 2        1    pheno1   5 102.9451 6.909370 102.66155 103.3928
# 3        1    pheno1  11 100.0623 6.710383  99.90022 100.9337
# [1] 1

Past versions of “Plot qtl mapping on 69K-32.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-33.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-34.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] 2

Past versions of “Plot qtl mapping on 69K-35.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-36.png”

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f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-37.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] 3

Past versions of “Plot qtl mapping on 69K-38.png”

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f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-39.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-40.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] "Time.to.Threshold.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo     ci_hi
# 1        1    pheno1   4 135.9546 6.459910 53.10543 136.07892
# 2        1    pheno1  14  20.6426 6.085256 20.25692  22.26143
# [1] 1

Past versions of “Plot qtl mapping on 69K-41.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-42.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-43.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] 2

Past versions of “Plot qtl mapping on 69K-44.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-45.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-46.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] "Time.to.Projected.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   5 103.1294 6.407867 102.6616 103.3928
# [1] 1

Past versions of “Plot qtl mapping on 69K-47.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-48.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-49.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] "Start.of.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod     ci_lo    ci_hi
# 1        1    pheno1   5 102.9451 6.622891 102.66155 103.3928
# 2        1    pheno1  11 100.0623 6.910074  99.92203 100.8526
# [1] 1

Past versions of “Plot qtl mapping on 69K-50.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-51.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-52.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] 2

Past versions of “Plot qtl mapping on 69K-53.png”

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f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-54.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-55.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] "RR.Recovery.Rate.Hr"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   3 123.37631 6.139661 122.02872 131.78356
# 2        1    pheno1  14  21.17214 8.250728  20.25692  21.53839
# 3        1    pheno1  15  68.07208 6.071145  67.89315  68.61110
# [1] 1

Past versions of “Plot qtl mapping on 69K-56.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-57.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-58.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] 2

Past versions of “Plot qtl mapping on 69K-59.png”

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f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-60.png”

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f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-61.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

# [1] 3

Past versions of “Plot qtl mapping on 69K-62.png”

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f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-63.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

Past versions of “Plot qtl mapping on 69K-64.png”

Version	Author	Date
f412738	xhyuo	2021-12-20

#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)
  fname <- paste("output/Fentanyl/DO_fentanyl_69k_alternate_metrics_",i,"_coefplot.pdf",sep="")
  pdf(file = fname, width = 16, height =8)
  if(dim(peaks)[1] != 0){
    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] "Time.to.Dead.Hr"
# [1] 1
# [1] 2
# [1] "RR.Depression.Rate.Hr"
# [1] "Time.to.Steady.RR.Depression.Hr"
# [1] "Min.Depression.RR"
# [1] 1
# [1] 2
# [1] "Mean.Depression.BR"
# [1] 1
# [1] 2
# [1] 3
# [1] "Steady-State.Depression.Duration.Hr"
# [1] 1
# [1] 2
# [1] 3
# [1] "Time.to.Threshold.Recovery.Hr"
# [1] 1
# [1] 2
# [1] "Time.to.Projected.Recovery.Hr"
# [1] 1
# [1] "Start.of.Recovery.Hr"
# [1] 1
# [1] 2
# [1] "RR.Recovery.Rate.Hr"
# [1] 1
# [1] 2
# [1] 3

#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)
  fname <- paste("output/Fentanyl/DO_fentanyl_69k_alternate_metrics_",i,"_coefplot_blup.pdf",sep="")
  pdf(file = fname, width = 16, height =8)
  if(dim(peaks)[1] != 0){
    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] "Time.to.Dead.Hr"
# [1] 1
# [1] 2
# [1] "RR.Depression.Rate.Hr"
# [1] "Time.to.Steady.RR.Depression.Hr"
# [1] "Min.Depression.RR"
# [1] 1
# [1] 2
# [1] "Mean.Depression.BR"
# [1] 1
# [1] 2
# [1] 3
# [1] "Steady-State.Depression.Duration.Hr"
# [1] 1
# [1] 2
# [1] 3
# [1] "Time.to.Threshold.Recovery.Hr"
# [1] 1
# [1] 2
# [1] "Time.to.Projected.Recovery.Hr"
# [1] 1
# [1] "Start.of.Recovery.Hr"
# [1] 1
# [1] 2
# [1] "RR.Recovery.Rate.Hr"
# [1] 1
# [1] 2
# [1] 3

###heritability

#plot heritability by qtl2 array
load("output/Fentanyl/qtl.fentanyl.array.alternate.metrics.out.RData")
herit <- data.frame(Phenotype = names(unlist(qtl.morphine.hsq)),
                    Heritability = round(unlist(qtl.morphine.hsq),2))
herit <- herit %>%
  arrange(desc(Heritability))
herit$Phenotype <- factor(herit$Phenotype, levels = herit$Phenotype)
#histgram
pdf(file = paste0("data/FinalReport/GCTA/DO_Fentanyl_alternate_metrics_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("Heritability by qtl2 array")
p1
dev.off()
# png 
#   2
p1

Past versions of heritability-1.png

Version	Author	Date
f412738	xhyuo	2021-12-20

#plot heritability by qtl2 69k
load("output/Fentanyl/qtl.fentanyl.69k.alternate.metrics.out.RData")
herit <- data.frame(Phenotype = names(unlist(qtl.morphine.hsq)),
                    Heritability = round(unlist(qtl.morphine.hsq),2))
herit <- herit %>%
  arrange(desc(Heritability))
herit$Phenotype <- factor(herit$Phenotype, levels = herit$Phenotype)
#histgram
pdf(file = paste0("data/FinalReport/GCTA/DO_Fentanyl_alternate_metrics_heritability_by_qtl2_69k.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("Heritability by qtl2 69k")
p2
dev.off()
# png 
#   2
p2

Past versions of heritability-2.png

Version	Author	Date
f412738	xhyuo	2021-12-20

Session information

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         labeling_0.4.2    
# [21] scales_1.1.1       digest_0.6.27      rmarkdown_2.6      pkgconfig_2.0.3   
# [25] htmltools_0.5.1.1  parallelly_1.23.0  highr_0.8          dbplyr_2.1.0      
# [29] fastmap_1.1.0      rlang_0.4.10       readxl_1.3.1       rstudioapi_0.13   
# [33] RSQLite_2.2.3      farver_2.0.3       generics_0.1.0     jsonlite_1.7.2    
# [37] zip_2.1.1          magrittr_2.0.1     Matrix_1.2-18      Rcpp_1.0.6        
# [41] munsell_0.5.0      lifecycle_1.0.0    stringi_1.5.3      whisker_0.4       
# [45] yaml_2.2.1         plyr_1.8.6         grid_4.0.3         blob_1.2.1        
# [49] listenv_0.8.0      promises_1.2.0.1   crayon_1.4.1       lattice_0.20-41   
# [53] haven_2.3.1        splines_4.0.3      hms_1.0.0          knitr_1.31        
# [57] pillar_1.4.7       codetools_0.2-18   reprex_1.0.0       glue_1.4.2        
# [61] evaluate_0.14      data.table_1.13.6  modelr_0.1.8       vctrs_0.3.6       
# [65] httpuv_1.5.5       cellranger_1.1.0   gtable_0.3.0       reshape_0.8.8     
# [69] assertthat_0.2.1   cachem_1.0.4       xfun_0.21          broom_0.7.4       
# [73] later_1.1.0.1      memoise_2.0.0      globals_0.14.0     ellipsis_0.3.1

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