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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/05_after_diagnosis_qc_gigamuga_12_batches.Rmd) and HTML (docs/05_after_diagnosis_qc_gigamuga_12_batches.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
html 6610ab1 xhyuo 2020-11-05 Build site.
Rmd 8d3b7b7 xhyuo 2020-11-04 12_batches_before_afterQC
html 070d088 xhyuo 2020-11-04 Build site.
Rmd 1b61cb6 xhyuo 2020-11-04 12_batches_before_afterQC

After genotype diagnostics for diversity outbred mice

We first load the R/qtl2 package and the data. We’ll also load the R/broman package for some utilities and plotting functions, and R/qtlcharts for interactive graphs.

library

library(broman)
library(qtl2)
library(qtlcharts)
library(ggplot2)
library(ggrepel)
#library(DOQTL)
library(mclust)
library(tidyverse)
library(reshape2)
library(DT)
source("code/reconst_utils.R")
options(stringsAsFactors = F)

Missing data per sample

gm <- get(load("data/Jackson_Lab_12_batches/gm_DO3173_qc.RData"))
gm
Object of class cross2 (crosstype "do")

Total individuals              3173
No. genotyped individuals      3173
No. phenotyped individuals     3173
No. with both geno & pheno     3173

No. phenotypes                    1
No. covariates                    5
No. phenotype covariates          0

No. chromosomes                  20
Total markers                112470

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
8532 8649 6402 6603 6558 6429 6281 5660 5856 5438 6339 5151 5259 5027 4548 4356 
  17   18   19    X 
4322 3986 3103 3971 
percent_missing <- n_missing(gm, "ind", "prop")*100
setScreenSize(height=100, width=300)
Set screen size to height=100 x width=300
labels <- paste0(names(percent_missing), " (", round(percent_missing,2), "%)")
iplot(seq_along(percent_missing), percent_missing, indID=labels,
      chartOpts=list(xlab="Mouse", ylab="Percent missing genotype data",
                     ylim=c(0, 60)))
#save into pdf
pdf(file = "data/Jackson_Lab_12_batches/AfterQC_Percent_missing_genotype_data.pdf", width = 20, height = 20)
labels <- as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "V01_"))[,2])
labels[percent_missing < 5] = ""
# Change point shapes and colors
p <- ggplot(data = data.frame(Mouse=seq_along(percent_missing),  
                         Percent_missing_genotype_data = percent_missing,
                         batch = factor(as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,5]))), 
        aes(x=Mouse, y=Percent_missing_genotype_data, color = batch)) +
  geom_point() +
  geom_hline(yintercept=5, linetype="solid", color = "red") +
  geom_text_repel(aes(label=labels), vjust = 0, nudge_y = 0.01, show.legend = FALSE, size=3) +
  theme(text = element_text(size = 20))
p
dev.off()
png 
  2 
p

Version Author Date
070d088 xhyuo 2020-11-04

Sexes

xint <- read_csv_numer("data/Jackson_Lab_12_batches/Jackson_Lab_12_batches_qtl2_chrXint.csv", transpose=TRUE)
yint <- read_csv_numer("data/Jackson_Lab_12_batches/Jackson_Lab_12_batches_qtl2_chrYint.csv", transpose=TRUE)

#subset to gm subject name
xint <- xint[rownames(xint) %in% ind_ids(gm),]
yint <- yint[rownames(yint) %in% ind_ids(gm),]

# Gigamuga marker annotation file from UNC.
gm_marker_file = "http://csbio.unc.edu/MUGA/snps.gigamuga.Rdata" #FIXED
# Read in the UNC GigaMUGA SNPs and clusters.
load(url(gm_marker_file))
#subset down to gm
snps$marker = as.character(snps$marker)

#load the intensities.fst.RData
load("data/Jackson_Lab_12_batches/intensities.fst.RData")
#X and Y channel
X <- result[result$channel == "X",]
rownames(X) <- X$snp
X <- X[,c(-1,-2)]

Y <- result[result$channel == "Y",]
rownames(Y) <- Y$snp
Y <- Y[,c(-1,-2)]

#determine predict.sex
predict.sex = determine_sex_chry_m(x = X, y = Y, markers = snps)$sex

gm$covar <- gm$covar %>%
  mutate(id = rownames(gm$covar)) %>%
  left_join(data.frame(id = names(predict.sex),
                       predict.sex = predict.sex,stringsAsFactors = F))
Joining, by = c("id", "predict.sex")
rownames(gm$covar) <- gm$covar$id

#sex order
sex <- gm$covar[rownames(xint),"sex"]

x_pval <- apply(xint, 2, function(a) t.test(a ~ sex)$p.value)
y_pval <- apply(yint, 2, function(a) t.test(a ~ sex)$p.value)

xint_ave <- rowMeans(xint[, x_pval < 0.05/length(x_pval)], na.rm=TRUE)
yint_ave <- rowMeans(yint[, y_pval < 0.05/length(y_pval)], na.rm=TRUE)

point_colors <- as.character( brocolors("web")[c("green", "purple")] )
labels <- paste0(names(xint_ave))
iplot(xint_ave, yint_ave, group=sex, indID=labels,
      chartOpts=list(pointcolor=point_colors, pointsize=4,
                     xlab="Average X chr intensity", ylab="Average Y chr intensity"))
phetX <- rowSums(gm$geno$X == 2)/rowSums(gm$geno$X != 0)
iplot(xint_ave, phetX, group=sex, indID=labels,
      chartOpts=list(pointcolor=point_colors, pointsize=4,
                     xlab="Average X chr intensity", ylab="Proportion het on X chr"))

Sample duplicates

cg <- compare_geno(gm, cores=10)
summary.cg <- summary(cg, threshold = 0.65)
#get the name and missing percentage
summary.cg$Name.ind1 <- str_split_fixed(summary.cg$ind1, "_",7)[,6]
summary.cg$Name.ind2 <- str_split_fixed(summary.cg$ind2, "_",7)[,6]
summary.cg$miss.ind1 <- percent_missing[match(summary.cg$ind1, names(percent_missing))]
summary.cg$miss.ind2 <- percent_missing[match(summary.cg$ind2, names(percent_missing))]
summary.cg$remove.id <- ifelse(summary.cg$miss.ind1 > summary.cg$miss.ind2, summary.cg$ind1, summary.cg$ind2)

#display summary.cg
DT::datatable(summary.cg, filter = list(position = 'top', clear = FALSE),
              options = list(pageLength = 40, scrollY = "300px", scrollX = "40px"))
pdf(file = "data/Jackson_Lab_12_batches/AfterQC_Proportion_matching_genotypes_before_removal_of_bad_samples.pdf", width = 20, height = 20) 
par(mar=c(5.1,0.6,0.6, 0.6))
hist(cg[upper.tri(cg)], breaks=seq(0, 1, length=201),
     main="", yaxt="n", ylab="", xlab="Proportion matching genotypes")
rug(cg[upper.tri(cg)])
dev.off()
png 
  2 
par(mar=c(5.1,0.6,0.6, 0.6))
hist(cg[upper.tri(cg)], breaks=seq(0, 1, length=201),
     main="", yaxt="n", ylab="", xlab="Proportion matching genotypes")
rug(cg[upper.tri(cg)])

Version Author Date
070d088 xhyuo 2020-11-04 
pdf(file = "data/Jackson_Lab_12_batches/AfterQC_Proportion_matching_genotypes_after_removal_of_bad_samples.pdf",width = 20, height = 20) 
cgsub <- cg[percent_missing < 5, percent_missing < 5]
par(mar=c(5.1,0.6,0.6, 0.6))
hist(cgsub[upper.tri(cgsub)], breaks=seq(0, 1, length=201),
     main="", yaxt="n", ylab="", xlab="Proportion matching genotypes")
rug(cgsub[upper.tri(cgsub)])
dev.off()
png 
  2 
cgsub <- cg[percent_missing < 5, percent_missing < 5]
par(mar=c(5.1,0.6,0.6, 0.6))
hist(cgsub[upper.tri(cgsub)], breaks=seq(0, 1, length=201),
     main="", yaxt="n", ylab="", xlab="Proportion matching genotypes")
rug(cgsub[upper.tri(cgsub)])

Version Author Date
070d088 xhyuo 2020-11-04 
#show top 20 samples with missing genotypes
percent_missing <- n_missing(gm, "ind", "prop")*100
round(sort(percent_missing, decreasing=TRUE)[1:20], 1)
Jackson_Lab_Bubier_MURGIGV01_20190425_18169_D12 
                                            9.7 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18196_E3 
                                            9.6 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18903_A3 
                                            8.8 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18927_A6 
                                            8.5 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18798_A2 
                                            8.1 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18842_E7 
                                            7.6 
 Jackson_Lab_Bubier_MURGIGV01_20190425_18799_B2 
                                            7.2 
 Jackson_Lab_Bubier_MURGIGV01_20160908_7689_A12 
                                            6.9 
 Jackson_Lab_Bubier_MURGIGV01_20190108_16961_G6 
                                            6.5 
 Jackson_Lab_Bubier_MURGIGV01_20181207_16256_G1 
                                            6.2 
 Jackson_Lab_Gagnon_MURGIGV01_20191011_20649_A1 
                                            6.2 
Jackson_Lab_Bubier_MURGIGV01_20190108_16866_G12 
                                            6.1 
 Jackson_Lab_Bubier_MURGIGV01_20190108_17142_E1 
                                            5.7 
Jackson_Lab_Bubier_MURGIGV01_20181207_15071_B12 
                                            5.6 
Jackson_Lab_Bubier_MURGIGV01_20190425_18256_A11 
                                            5.6 
 Jackson_Lab_Bubier_MURGIGV01_20190108_17143_F1 
                                            5.5 
 Jackson_Lab_Gagnon_MURGIGV01_20191011_20658_B1 
                                            5.5 
Jackson_Lab_Bubier_MURGIGV01_20190108_17018_A12 
                                            5.4 
 Jackson_Lab_Bubier_MURGIGV01_20181207_14695_F7 
                                            5.3 
 Jackson_Lab_Bubier_MURGIGV01_20181206_16219_H7 
                                            5.2

Array intensities and Genotype frequencies

int <- result[,c("snp","channel",ind_ids(gm))]
#rm(result)
int <- int[seq(1, nrow(int), by=2),-(1:2)] + int[-seq(1, nrow(int), by=2),-(1:2)]
int <- int[,intersect(ind_ids(gm), colnames(int))]
n <- names(sort(percent_missing[intersect(ind_ids(gm), colnames(int))], decreasing=TRUE))
iboxplot(log10(t(int[,n])+1), orderByMedian=FALSE, chartOpts=list(ylab="log10(SNP intensity + 1)"))
# Genotype frequencies
g <- do.call("cbind", gm$geno[1:19])
fg <- do.call("cbind", gm$founder_geno[1:19])
g <- g[,colSums(fg==0)==0]
fg <- fg[,colSums(fg==0)==0]
fgn <- colSums(fg==3)

gf_ind <- vector("list", 4)
for(i in 1:4) {
  gf_ind[[i]] <- t(apply(g[,fgn==i], 1, function(a) table(factor(a, 1:3))/sum(a != 0)))
}

par(mfrow=c(4,1), mar=c(0.6, 0.6, 2.6, 0.6))
for(i in 1:4) {
  triplot(c("AA", "AB", "BB"), main=paste0("MAF = ", i, "/8"))
  tripoints(gf_ind[[i]], pch=21, bg="lightblue")
  tripoints(c((1-i/8)^2, 2*i/8*(1-i/8), (i/8)^2), pch=21, bg="violetred")
  
  if(i>=3) { # label mouse with lowest het
    wh <- which(gf_ind[[i]][,2] == min(gf_ind[[i]][,2]))
    tritext(gf_ind[[i]][wh,,drop=FALSE] + c(0.02, -0.02, 0),
            names(wh), adj=c(0, 1))
  }
  
  # label other mice
  if(i==1) {
    lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.3]
  }
  else if(i==2) {
    lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.48]
  }
  else if(i==3) {
    lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.51]
  }
  else if(i==4) {
    lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.6]
  }
  
  for(ind in lab) {
    if(grepl("^F", ind) && i != 3) {
      tritext(gf_ind[[i]][ind,,drop=FALSE] + c(-0.01, 0, +0.01), ind, adj=c(1,0.5))
    } else {
      tritext(gf_ind[[i]][ind,,drop=FALSE] + c(0.01, 0, -0.01), ind, adj=c(0,0.5))
    }
  }
}

Version Author Date
070d088 xhyuo 2020-11-04

Crossover counts and Genotyping error LOD scores

#load pre-caluated results
load("data/Jackson_Lab_12_batches/nxo.RData")

#crossover
totxo <- rowSums(nxo)[names(rowSums(nxo)) %in% ind_ids(gm)]
totxo <- totxo[ind_ids(gm)]
all.equal(ind_ids(gm), names(totxo))
[1] TRUE
iplot(seq_along(totxo),
      totxo,
      group=gm$covar$ngen,
      chartOpts=list(xlab="Mouse", ylab="Number of crossovers", 
                     margin=list(left=80,top=40,right=40,bottom=40,inner=5),
                     axispos=list(xtitle=25,ytitle=50,xlabel=5,ylabel=5)))
#save crossover into pdf
pdf(file = "data/Jackson_Lab_12_batches/AfterQC_number_crossover.pdf")
cross_over <- data.frame(Mouse = seq_along(totxo), Number_crossovers = totxo, generation = gm$covar$ngen)
names(totxo) <- as.character(do.call(rbind.data.frame, strsplit(names(totxo), "V01_"))[,2])
names(totxo)[totxo <= 800] = ""
# Change point shapes and colors
p <-ggplot(cross_over, aes(x=Mouse, y=Number_crossovers, fill = generation, color=generation)) +
  geom_point() +
  geom_text_repel(aes(label=names(totxo),hjust=0,vjust=0), show.legend = FALSE)
p
dev.off()
png 
  2 
p

Version Author Date
070d088 xhyuo 2020-11-04 

sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] DT_0.13           reshape2_1.4.4    forcats_0.5.0     stringr_1.4.0    
 [5] dplyr_1.0.0       purrr_0.3.4       readr_1.4.0       tidyr_1.1.0      
 [9] tibble_3.0.1      tidyverse_1.3.0   mclust_5.4.6      ggrepel_0.8.2    
[13] ggplot2_3.3.2     qtlcharts_0.12-10 qtl2_0.22-8       broman_0.70-4    
[17] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6      lubridate_1.7.9   assertthat_0.2.1  rprojroot_1.3-2  
 [5] digest_0.6.25     plyr_1.8.6        R6_2.4.1          cellranger_1.1.0 
 [9] backports_1.1.6   reprex_0.3.0      RSQLite_2.2.0     evaluate_0.14    
[13] httr_1.4.1        pillar_1.4.4      rlang_0.4.6       readxl_1.3.1     
[17] rstudioapi_0.11   data.table_1.12.8 whisker_0.4       blob_1.2.1       
[21] rmarkdown_2.5     labeling_0.4.2    qtl_1.46-2        htmlwidgets_1.5.1
[25] bit_1.1-15.2      munsell_0.5.0     broom_0.7.2       compiler_4.0.0   
[29] httpuv_1.5.4      modelr_0.1.8      xfun_0.13         pkgconfig_2.0.3  
[33] htmltools_0.4.0   tidyselect_1.1.0  fansi_0.4.1       crayon_1.3.4     
[37] dbplyr_2.0.0      withr_2.2.0       later_1.0.0       grid_4.0.0       
[41] jsonlite_1.6.1    gtable_0.3.0      lifecycle_0.2.0   DBI_1.1.0        
[45] git2r_0.27.1      magrittr_1.5      scales_1.1.1      cli_2.0.2        
[49] stringi_1.4.6     farver_2.0.3      fs_1.4.1          promises_1.1.0   
[53] xml2_1.3.2        ellipsis_0.3.0    generics_0.0.2    vctrs_0.3.1      
[57] tools_4.0.0       bit64_0.9-7       glue_1.4.0        crosstalk_1.1.0.1
[61] hms_0.5.3         parallel_4.0.0    yaml_2.2.1        colorspace_1.4-1 
[65] rvest_0.3.6       memoise_1.1.0     knitr_1.28        haven_2.3.1