DO_Diversity_Report

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Last updated: 2021-09-06

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Knit directory: DO_Opioid/

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---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/batches_3_do_diversity_report.Rmd) and HTML (docs/batches_3_do_diversity_report.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	95af54d	xhyuo	2021-09-06	fentanyl
html	468ae22	xhyuo	2021-06-24	Build site.
Rmd	cd89ecf	xhyuo	2021-06-24	3batches diversity

---

Diversity report for diversity outbred mice

After finishing 06_final_pr_apr_69K.R, 07_do_diversity_report.R, all the output will be used plot DO Diversity Report for 3 batches of DO mice

library

# Load packages
library(qtl2)
library(table1)
library(tidyverse)
library(data.table)
library(foreach)
library(doParallel)
library(parallel)
library(abind)
library(gap)
library(regress)
library(lme4)
library(abind)
library(ggplot2)
library(vcd)
library(MASS)
#library(plotly)
library(colorspace)
library(HardyWeinberg)
options(stringsAsFactors = FALSE)
source("code/reconst_utils.R")

Summary

load("data/Jackson_Lab_Bubier_MURGIGV01/gm_DO395_qc_newid.RData")#gm_after_qc

# make dataset with a few variables to summarize
table1 <- gm_after_qc$covar %>% 
  dplyr::select(Name = name, 
                Sex  = sex, 
                Generation = ngen) %>%
  mutate(Sex = case_when(
    Sex == "F" ~ "Female",
    Sex == "M" ~ "Male"
  ))

# summarize the data 
table1(~ Generation | Sex, data=table1)

	Female (N=201)	Male (N=194)	Overall (N=395)
Generation
36	201 (100%)	194 (100%)	395 (100%)

Founder contributions

load("data/Jackson_Lab_Bubier_MURGIGV01/fp_DO395.RData") #fp and fp_summary object

#summarize per generation per chromosome
fp_summary = fp %>% group_by(chr, founder, gen) %>%
  summarize(mean = round(100*mean(prop), 2),
            sd   = round(100*sd(prop), 2))

`summarise()` has grouped output by 'chr', 'founder'. You can override using the `.groups` argument.

#Stackbar plot
#summarize per chromosome across generation
pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/stackbar_mean_prop_across_all_gen.pdf",width = 16)
p01 <- fp %>% group_by(chr, founder) %>%
  summarise(grand_mean = round(100*mean(prop), 2)) %>%
  ggplot(aes(x = chr, y = grand_mean, fill = founder)) +
    geom_bar(stat="identity",
             width=1) +
    geom_text(aes(label = paste0(grand_mean)), position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = CCcolors) +
    ylab("Mean percentage across generations") +
    xlab("Chromosome") +
    theme(panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
          axis.line = element_line(colour = "black"),
          plot.title = element_text(hjust = 0.5),
          text = element_text(size=16),
          axis.title=element_text(size=16),
          legend.title=element_blank())

`summarise()` has grouped output by 'chr'. You can override using the `.groups` argument.

p01
dev.off()

png 
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p01

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468ae22	xhyuo	2021-06-24

#Stackbar plot
#summarize per chromosome across generation
pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/stackbar_mean_prop_across_all_chr.pdf",width = 16)
p02 <- fp %>% group_by(gen, founder) %>%
  summarise(grand_mean = round(100*mean(prop), 2), 
            grand_sd   = round(100*sd(prop), 2)) %>%
  ggplot(aes(x = gen, y = grand_mean, fill = founder)) +
    geom_bar(stat="identity",
             width=0.99) +
    geom_text(aes(label = paste0(grand_mean, " ± ", grand_sd)), position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = CCcolors) +
    ylab("Mean percentage across all chromosomes") +
    xlab("Generation") +
    theme(panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
          axis.line = element_line(colour = "black"),
          plot.title = element_text(hjust = 0.5),
          text = element_text(size=16),
          axis.title=element_text(size=16),
          legend.title=element_blank())

`summarise()` has grouped output by 'gen'. You can override using the `.groups` argument.

p02
dev.off()

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p02

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468ae22	xhyuo	2021-06-24

#stackbar_prop_across_gen
for(c in c(1:19, "X")){
  #print(c)
  p <- ggplot(data = fp_summary[fp_summary$chr == c,], aes(x = gen, y = mean, fill = founder)) +
    geom_bar(stat="identity",
             width=1) +
    geom_text(aes(label = paste0(mean," ± ", sd)), position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = CCcolors) +
    labs(title = paste0("Chr ", c)) +
    ylab("Percentage") +
    xlab("Generation") +
    theme(panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
          axis.line = element_line(colour = "black"),
          plot.title = element_text(hjust = 0.5),
          text = element_text(size=16),
          axis.title=element_text(size=16),
          legend.title=element_blank())
  print(p)
}

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pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/stackbar_prop_across_gen.pdf",width = 16)
for(c in c(1:19, "X")){
  #print(c)
  p <- ggplot(data = fp_summary[fp_summary$chr == c,], aes(x = gen, y = mean, fill = founder)) +
    geom_bar(stat="identity",
             width=1) +
    geom_text(aes(label = paste0(mean," ± ", sd)), position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = CCcolors) +
    labs(title = paste0("Chr ", c)) +
    ylab("Percentage") +
    xlab("Generation") +
    theme(panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
          axis.line = element_line(colour = "black"),
          plot.title = element_text(hjust = 0.5),
          text = element_text(size=16),
          axis.title=element_text(size=16),
          legend.title=element_blank())
  print(p)
}
dev.off()

png 
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#stackbar_prop_across_chr
for(g in levels(fp_summary$gen)){
  #print(g)
  p <- ggplot(data = fp_summary[fp_summary$gen == g,], aes(x = chr, y = mean, fill = founder)) +
    geom_bar(stat="identity",
             width=1) +
    geom_text(aes(label = paste0(mean)), position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = CCcolors) +
    labs(title = paste0("Generation ", g)) +
    ylab("Percentage") +
    theme(panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
          axis.title.x = element_blank(),
          axis.line = element_line(colour = "black"),
          plot.title = element_text(hjust = 0.5),
          text = element_text(size=16),
          axis.title=element_text(size=16),
          legend.title=element_blank())
  #print(p)
}

pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/stackbar_prop_across_chr.pdf", width = 12)
for(g in levels(fp_summary$gen)){
  #print(g)
  p <- ggplot(data = fp_summary[fp_summary$gen == g,], aes(x = chr, y = mean, fill = founder)) +
    geom_bar(stat="identity",
             width=1) +
    geom_text(aes(label = paste0(mean)), position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = CCcolors) +
    labs(title = paste0("Generation ", g)) +
    ylab("Percentage") +
    theme(panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
          axis.title.x = element_blank(),
          axis.line = element_line(colour = "black"),
          plot.title = element_text(hjust = 0.5),
          text = element_text(size=16),
          axis.title=element_text(size=16),
          legend.title=element_blank())
  print(p)
}
dev.off()

png 
  2 

#line plot
#plt <- htmltools::tagList()
for(c in unique(names(gm_after_qc$geno))){
  print(c)
  fp_subdata <- fp[fp$chr == c,]
  pp <- ggplot(data = fp_subdata,aes(pos, prop, group = gen, color = founder)) +
    geom_line(aes(linetype=gen)) +
    scale_linetype_manual(values=rep("solid",12)) +
    geom_hline(yintercept=0.125, linetype="dashed", color = "black", size = 0.25) +
    scale_color_manual(values = CCcolors) +
    facet_grid(founder~.) + 
    labs(title = paste0("Chr ", c)) + 
    theme(legend.position='none')
  print(pp)
  # Print an interactive plot
  # Add to list
  #plt[[c]] <- as_widget(ggplotly(pp, width = 1000, height = 1000))
}

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#plt

Average haplotype block size

load("data/Jackson_Lab_Bubier_MURGIGV01/recom_block_size.RData")
#Create an appropriately sized vector of names
nameVector <- unlist(mapply(function(x,y){ rep(y, length(x)) }, pos_ind_gen, names(pos_ind_gen)))
#Create the result
recom_block <- cbind.data.frame(unlist(pos_ind_gen), nameVector)
colnames(recom_block) <- c("sizeblock",
                           "ngen")
#remove 0
recom_block <- recom_block[recom_block$sizeblock != 0,]
#mean
means <- aggregate(sizeblock~ngen, data= recom_block,mean)
means$sizeblock <- round(means$sizeblock, 2)

pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/boxplot_mean_recomb_block_size.pdf", height = 8, width = 10)
p1 <- ggplot(recom_block, aes(x=ngen, y=sizeblock, group = ngen, fill = ngen)) + 
  geom_boxplot(show.legend = F , outlier.size = 0.5, notchwidth = 3) +
  scale_fill_discrete_qualitative(palette = "warm")+
  geom_text(data = means, alpha = 0.85, aes(label = sizeblock, y = sizeblock + 0.15 )) + 
  ylab("Recombination Block Size (Mb)") +
  xlab("Generation") +
  labs(fill = "") +
  #ylim(c(0, 60)) +
  scale_y_continuous(breaks=c(0,5,10, 20, 40, 60), limits=c(0, 60)) +
  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=16), 
        axis.title=element_text(size=16)) +
  guides(shape = guide_legend(override.aes = list(size = 12)))
p1
dev.off()

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p1

Past versions of “Average haplotype block size-1.png”

Version	Author	Date
468ae22	xhyuo	2021-06-24

#block size distribution
for(g in unique(gm_after_qc$covar$ngen)){
  #plot for recom block size
  #png(paste0("data/Jackson_Lab_Bubier_MURGIGV01/DO_recom_block_size_G", g, ".png"))
  x <- pos_ind_gen[[g]][pos_ind_gen[[g]] != 0]
  # estimate the parameters
  fit1 <- fitdistr(x, "exponential") 
  
  # goodness of fit test
  ks.test(x, "pexp", fit1$estimate) # p-value > 0.05 -> distribution not refused
  
  # plot a graph
  hist(x, 
       freq = FALSE, 
       breaks = 200, 
       xlim = c(0, 5+quantile(x, 1)), 
       #ylim = c(0,0.3),
       xlab = "Recombination Block Size (Mb)", 
       main = paste0("Gen ", g))
  curve(dexp(x, rate = fit1$estimate), 
        from = 0, 
        to = 5+quantile(x, 1), 
        col = "red", 
        add = TRUE)
  #dev.off()
}

Warning in ks.test(x, "pexp", fit1$estimate): ties should not be present for the
Kolmogorov-Smirnov test

Past versions of “Average haplotype block size-2.png”

Version	Author	Date
468ae22	xhyuo	2021-06-24

Average heterozygosity value

load("data/Jackson_Lab_Bubier_MURGIGV01/dat_het_ind_pr.RData")

pdf(paste("data/Jackson_Lab_Bubier_MURGIGV01/DO_Heterozygosity_value_violin_genoprops.pdf"), width = 10, height =8)
p2 <- ggplot(dat_het_ind_pr, aes(x=ngen, y=het, group=ngen, fill=ngen)) +
  geom_violin(show.legend = FALSE) +
  geom_boxplot(show.legend = FALSE, width=0.35, color="black", alpha=0.6) +
  #scale_fill_discrete_qualitative(palette = "warm")+
  ylab("Heterozygosity from genotype props") +
  xlab("Generation") +
  ylim(c(0.65, 1)) +
  geom_hline(yintercept=0.875, linetype="dashed", color = "red") +
  #scale_y_continuous(breaks=c(0.55, 0.65, 0.75, 0.85, 0.95, 1), limits=c(0.55, 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=16),
        axis.title=element_text(size=16),
        legend.title=element_blank())
p2
dev.off()

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p2

Past versions of “Average heterozygosity value-1.png”

Version	Author	Date
468ae22	xhyuo	2021-06-24

marker UNC13316610 allele frequency

#pull marker UNC13316610
gm.UNC13316610 <- pull_markers(gm_after_qc, "UNC13316610")
#genotype freq
gfreq <- calc_raw_geno_freq(gm.UNC13316610, by = c("marker"))
gfreq_tab <- gfreq %>%
  as.data.frame() %>%
  mutate(id = rownames(gfreq)) %>%
  mutate(C_freq = (2*AA + AB)/(2*(AA+AB+BB))) %>%
  mutate(T_freq = 1-C_freq) %>%
dplyr::select(CC = AA,
              CT = AB,
              TT = BB,
              C_freq,
              T_freq) %>%
  mutate(chisq = HWChisq(c(91, 215, 80))$chisq,
         df    = HWChisq(c(91, 215, 80))$df,
         p     = HWChisq(c(91, 215, 80))$p) 

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal)
Chi2 =  4.756968 DF =  1 p-value =  0.0291799 D =  11.07837 f =  -0.114895 
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal)
Chi2 =  4.756968 DF =  1 p-value =  0.0291799 D =  11.07837 f =  -0.114895 
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal)
Chi2 =  4.756968 DF =  1 p-value =  0.0291799 D =  11.07837 f =  -0.114895 

gfreq_tab

                  CC       CT        TT    C_freq    T_freq    chisq         p
UNC13316610 0.235443 0.556962 0.2075949 0.5139241 0.4860759 4.756968 0.4857513

#
#AA  AB  BB 
#91 215  80 

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] grid      parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] HardyWeinberg_1.7.1 Rsolnp_1.16         mice_3.13.0        
 [4] colorspace_2.0-0    MASS_7.3-53         vcd_1.4-8          
 [7] lme4_1.1-26         Matrix_1.2-18       regress_1.3-21     
[10] gap_1.2.2           abind_1.4-5         doParallel_1.0.16  
[13] iterators_1.0.13    foreach_1.5.1       data.table_1.13.6  
[16] forcats_0.5.1       stringr_1.4.0       dplyr_1.0.4        
[19] purrr_0.3.4         readr_1.4.0         tidyr_1.1.2        
[22] tibble_3.0.6        ggplot2_3.3.3       tidyverse_1.3.0    
[25] table1_1.2.1        qtl2_0.24           workflowr_1.6.2    

loaded via a namespace (and not attached):
 [1] nlme_3.1-152      fs_1.5.0          lubridate_1.7.9.2 bit64_4.0.5      
 [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         withr_2.4.1       tidyselect_1.1.0 
[13] bit_4.0.4         compiler_4.0.3    git2r_0.28.0      cli_2.3.0        
[17] rvest_0.3.6       xml2_1.3.2        labeling_0.4.2    scales_1.1.1     
[21] lmtest_0.9-38     digest_0.6.27     minqa_1.2.4       rmarkdown_2.6    
[25] pkgconfig_2.0.3   htmltools_0.5.1.1 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    zoo_1.8-8        
[37] jsonlite_1.7.2    magrittr_2.0.1    Formula_1.2-4     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        blob_1.2.1        promises_1.2.0.1  crayon_1.4.1     
[49] lattice_0.20-41   haven_2.3.1       splines_4.0.3     hms_1.0.0        
[53] knitr_1.31        pillar_1.4.7      boot_1.3-27       codetools_0.2-18 
[57] reprex_1.0.0      glue_1.4.2        evaluate_0.14     modelr_0.1.8     
[61] vctrs_0.3.6       nloptr_1.2.2.2    httpuv_1.5.5      cellranger_1.1.0 
[65] gtable_0.3.0      assertthat_0.2.1  cachem_1.0.4      xfun_0.21        
[69] broom_0.7.4       later_1.1.0.1     truncnorm_1.0-8   memoise_2.0.0    
[73] statmod_1.4.35    ellipsis_0.3.1   

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