Last updated: 2022-02-10

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Rmd d199bd4 Belinda Cornes 2022-02-10 QC analysis

This script is running genotype QC on raw data (with some outcomes already seen in the project at a glace). Here, 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.

We will follow the steps by Karl Broman found here

Loading Project

gm <- get(load("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_hh/gm_serreze.192.RData"))
gm
Object of class cross2 (crosstype "bc")

Total individuals               192
No. genotyped individuals       192
No. phenotyped individuals      192
No. with both geno & pheno      192

No. phenotypes                    1
No. covariates                    6
No. phenotype covariates          0

No. chromosomes                  20
Total markers                133716

No. markers by chr:
    1     2     3     4     5     6     7     8     9    10    11    12    13 
10159 10172  7987  7736  7778  7911  7548  6561  6823  6472  7276  6226  6177 
   14    15    16    17    18    19     X 
 6082  5421  5075  5161  4682  3612  4857 
sample_file <- dir(path = filepaths, pattern = "^DODB_*", full.names = TRUE)
samples <- read.csv(sample_file)
all.equal(as.character(ind_ids(gm)), as.character(samples$Original.Mouse.ID))
[1] TRUE

Missing Data

percent_missing <- n_missing(gm, "ind", "prop")*100

#labels <- paste0(as.character(do.call(rbind.data.frame, strsplit(names(percent_missing), "_"))[,7]), " (", round(percent_missing,2), "%)")
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, 70)))
Set screen size to height=700 x width=1000
#save into pdf
pdf(file = "output/Percent_missing_genotype_data_4.batches.pdf", width = 20, height = 20)
#labels <- as.character(do.call(rbind.data.frame, strsplit(names(totxo), "V01_"))[,2])
#labels <- as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,7])
#labels <- paste0(names(percent_missing), " (", round(percent_missing,2), "%)")
labels <- ind_ids(gm)
labels[percent_missing < 10] = ""
# 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(as.character(samples$Unique.Sample.ID), "_"))[,6]))
                         #batch = factor(as.character(do.call(rbind.data.frame, strsplit(as.character(samples$Directory), "_"))[,5]))
                         ), 
        aes(x=Mouse, y=Percent_missing_genotype_data, color = batch)) +
        geom_point() +
        geom_hline(yintercept=10, 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 = 10))
p

dev.off()
quartz_off_screen 
                2 
p

save(percent_missing,file = "data/percent_missing_id_4.batches.RData")

gm.covar = data.frame(id=rownames(gm$covar),gm$covar)
qc_info_cr <- merge(gm.covar,
                  data.frame(id = names(percent_missing),percent_missing = percent_missing,stringsAsFactors = F),by = "id")
bad.sample.cr <- qc_info_cr[qc_info_cr$percent_missing >= 10,]
Sample_ID percent_missing
NG00826-EOI 27.7483622004846
NG01465-ICI 38.0582727571869
NG01495-ICI 34.8671811899847
NG01518-ICI 29.3622303987556

Sex

hdf5_filename <- dir(path = filepaths, pattern = "^hdf5_*", full.names = TRUE)
snps_file <- "/Users/corneb/Documents/MyJax/CS/Projects/support.files/MUGAarrays/UWisc/gm_uwisc_v1.csv"
snps <- read.csv(snps_file)

snps <- snps[snps$unique == TRUE, ]
#snps <- snps[snps$chr %in% c(1:19, "X"), ]
snps$chr <- sub("^chr", "", snps$chr)  ###remove prefix "chr"
colnames(snps)[colnames(snps)=="bp_mm10"] <- "pos" 
colnames(snps)[colnames(snps)=="cM_cox"] <- "cM"
snps <- snps %>% drop_na(chr, marker) 
snps$pos <- snps$pos * 1e-6
rownames(snps) <- snps$marker
colnames(snps)[1:4] <- c("marker", "chr", "pos", "pos") 

#  g <- h5read(hdf5_filename, "G")
#  g <- do.call(cbind, g)
x <- h5read(hdf5_filename, "X") # X channel intensities
x <- do.call(cbind, x)
y <- h5read(hdf5_filename, "Y") # Y channel intensities
y <- do.call(cbind, y)
rn <- h5read(hdf5_filename, "rownames")[[1]]  # markers 
cn <- h5read(hdf5_filename, "colnames")  # samples
cn <- do.call(c, cn)
# dimnames(g) <- list(rn, cn)
dimnames(x) <- list(rn, cn)
dimnames(y) <- list(rn, cn)
#cr <- colMeans(g != "--") # Call rate for each sample avg 0.95
#  sex <- determine_sex(x = x, y = y, markers = snps)$se

markers <- snps

chrx <- markers$marker[which(markers$chr == "X")]
chry <- markers$marker[which(markers$chr == "Y")]
#x[chrx,ind_ids(gm)]

chrx_int <- colMeans(x[chrx,as.character(ind_ids(gm))] + y[chrx,as.character(ind_ids(gm))], na.rm = T)
chry_int <- colMeans(x[chry,as.character(ind_ids(gm))] + y[chry,as.character(ind_ids(gm))], na.rm = T)

all.equal(as.character(ind_ids(gm)), as.character(samples$Original.Mouse.ID))
[1] TRUE
#sex order
samples$Sex <- 'F'
sex <- samples$Sex


point_colors <- as.character( brocolors("web")[c("green", "purple")] )
percent_missing <- n_missing(gm, summary="proportion")*100
labels <- paste0(names(chrx_int), " (", round(percent_missing), "%)")
iplot( chrx_int,  chry_int, group=sex, indID=labels,
      chartOpts=list(pointcolor=point_colors, pointsize=4,
                     xlab="Average X chr intensity", ylab="Average Y chr intensity"))

For figures above and below, those labelled as female in metadata given, are coloured green, with those labelled as male are coloured as purple. The above is an interactive scatterplot of the average SNP intensity on the Y chromosome versus the average SNP intensity on the X chromosome.

phetX <- rowSums(gm$geno$X == 2)/rowSums(gm$geno$X != 0)
phetX <- phetX[as.character(ind_ids(gm)) %in% names(chrx_int)]
names(phetX) <- as.character(ind_ids(gm))
iplot(chrx_int, phetX, group=sex, indID=labels,
      chartOpts=list(pointcolor=point_colors, pointsize=4,
                     xlab="Average X chr intensity", ylab="Proportion het on X chr"))

In the above scatterplot, we show the proportion of hets vs the average intensity for the X chromosome SNPs. In calculating the proportion of heterozygous genotypes for the individuals, we look at X chromosome genotypes equal to 2 which corresponds to the heterozygote) relative to not being 0 (which is used to encode missing genotypes). The genotypes are arranged with rows being individuals and columns being markers.

The following are the mice that have had sex incorrectly assigned:

Sample Duplicates

cg <- compare_geno(gm, cores=10)
summary.cg <- summary(cg)

Here is a histogram of the proportion of matching genotypes. The tick marks below the histogram indicate individual pairs.

save(summary.cg,file = "data/summary.cg_4.batches.RData")

pdf(file = "output/Proportion_matching_genotypes_before_removal_of_bad_samples_4.batches.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()
quartz_off_screen 
                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)])

cgsub <- cg[percent_missing < 10, percent_missing < 10]
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 [percent missing < 10%]")
rug(cgsub[upper.tri(cgsub)])

Array Intensities

#load the intensities.fst_4.batches.RData
#load("data/intensities.fst_4.batches.RData")

xn <- x[,as.character(ind_ids(gm))]
xn <- xn[snps$marker,]
xnm <- rownames(xn)

yn <- y[,as.character(ind_ids(gm))]
yn <- yn[snps$marker,]

# bring together in one matrix
result <- cbind(snp=rep(snps$marker, 2),
                channel=rep(c("x", "y"), each=length(snps$marker)),
                as.data.frame(rbind(xn, yn)))
rownames(result) <- 1:nrow(result)

# bring SNP rows together
result <- result[as.numeric(t(cbind(seq_along(snps$marker), seq_along(snps$marker)+length(snps$marker)))),]
rownames(result) <- 1:nrow(result)

#load the intensities.fst_4.batches.RData
#load("data/heh/intensities.fst_4.batches.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)]

int <- result

#int <- result

#rm(result)
int <- int[seq(1, nrow(int), by=2),-(1:2)] + int[-seq(1, nrow(int), by=2),-(1:2)]
int <- int[,intersect(as.character(ind_ids(gm)), colnames(int))]
names(percent_missing) <- as.character(names(percent_missing))
n <- names(sort(percent_missing[intersect(as.character(ind_ids(gm)), colnames(int))], decreasing=TRUE))
iboxplot(log10(t(int[,n])+1), orderByMedian=FALSE, chartOpts=list(ylab="log10(SNP intensity + 1)"))

In the above plot, distributions of array intensities (after a log10(x+1) transformation) are displayed.

The arrays are sorted by the proportion of missing genotype data for the sample, and the curves connect various quantiles of the intensities.

qu <- apply(int, 2, quantile, c(0.01, 0.99), na.rm=TRUE)
group <- (percent_missing >= 19.97) + (percent_missing > 5) + (percent_missing > 2) + 1
labels <- paste0(colnames(qu), " (", round(percent_missing), "%)")
iplot(qu[1,], qu[2,], indID=labels, group=group,
      chartOpts=list(xlab="1 %ile of array intensities",
                     ylab="99 %ile of array intensities",
                     pointcolor=c("#ccc", "slateblue", "Orchid", "#ff851b")))

For this particular set of arrays, a plot of the 1 %ile vs the 99 %ile is quite revealing. In the following, the orange points are those with > 20% missing genotypes, the pink points are the samples with 5-20% missing genotypes, and the blue points are the samples with 2-5% missing genotypes.

Genotyping Error LOD Scores

load("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_hh/e.RData")
errors_ind <- rowSums(e>2)[rownames(gm$covar)]/n_typed(gm)*100
lab <- paste0(as.character(names(errors_ind)), " (", myround(percent_missing[as.character(rownames(gm$covar))],1), "%)")
iplot(seq_along(errors_ind), errors_ind, indID=lab,
      chartOpts=list(xlab="Mouse", ylab="Percent genotyping errors", ylim=c(0, 15),
                     axispos=list(xtitle=25, ytitle=50, xlabel=5, ylabel=5)))
save(errors_ind, file = "data/errors_ind_4.batches.RData")

Removing Samples

##percent missing
gm.covar = data.frame(id=as.character(rownames(gm$covar)),gm$covar)
qc_info <- merge(gm.covar,
                  data.frame(id = names(percent_missing),percent_missing = percent_missing,stringsAsFactors = F),by = "id")

#missing sex
#qc_info$sex.match <- ifelse(qc_info$sexp == qc_info$sex, TRUE, FALSE)
rownames(samples) <- as.character(samples$Original.Mouse.ID)
samples <- samples[as.character(qc_info$id),]
#samples$Unique.Sample.ID <- as.character(samples$Unique.Sample.ID)
all.equal(as.character(qc_info$id), as.character(samples$Original.Mouse.ID))
[1] TRUE
qc_info$sex.match <- ifelse(samples$Inferred.Sex == substring(samples$Sex, 1, 1), TRUE, FALSE)

#genotype errors
qc_info <- merge(qc_info,
                 data.frame(id = as.character(names(errors_ind)),
                            genotype_erros = errors_ind,stringsAsFactors = F),by = "id")

##duplicated id to be remove
qc_info$duplicate.id <- ifelse(qc_info$id %in% as.character(summary.cg$remove.id), TRUE,FALSE)

#bad.sample <- qc_info[qc_info$generation ==1 | qc_info$Number_crossovers <= 200 | qc_info$Number_crossovers >=1000 | qc_info$percent_missing >= 10 | qc_info$genotype_erros >= 1 | qc_info$remove.id.duplicated == TRUE,]
bad.sample <- qc_info[qc_info$percent_missing >= 10 | qc_info$genotype_erros >= 8,]

save(qc_info, bad.sample, file = "data/qc_info_bad_sample_4.batches.RData")

gm_samqc <- gm[paste0("-",as.character(bad.sample$id.1)),]

gm_samqc
Object of class cross2 (crosstype "bc")

Total individuals               188
No. genotyped individuals       188
No. phenotyped individuals      188
No. with both geno & pheno      188

No. phenotypes                    1
No. covariates                    6
No. phenotype covariates          0

No. chromosomes                  20
Total markers                133716

No. markers by chr:
    1     2     3     4     5     6     7     8     9    10    11    12    13 
10159 10172  7987  7736  7778  7911  7548  6561  6823  6472  7276  6226  6177 
   14    15    16    17    18    19     X 
 6082  5421  5075  5161  4682  3612  4857 
save(gm_samqc, file = "data/gm_samqc_4.batches.RData")

# update other stuff
e <- e[ind_ids(gm_samqc),]
#g <- g[ind_ids(gm_samqc),]
#snpg <- snpg[ind_ids(gm_samqc),]

#save(e,g,snpg, file = "data/e_g_snpg_samqc_4.batches.RData")
save(e, file = "data/e_snpg_samqc_4.batches.RData")

Here is the list of samples that were removed:

Sample_ID
NG00826-EOI
NG01465-ICI
NG01495-ICI
NG01518-ICI

Below is a table summarising the problematic samples found throughout QC. These include the following:

NB: For duplcate pairs, the one that was chosen to be removed was the one that had a higher missing rate

Sample_ID high_miss diff_sex high_geno.errors highly_concordant
DF06129 XX
LQ01806 XX
LQ01807 XX
ML00983 XX
ML00984 XX
NG00158 XX
NG00160 XX
NG00161 XX
NG00165 XX
NG00183 XX
NG00186 XX
NG00192 XX
NG00197 XX
NG00198-ICI-LATE XX
NG00203 XX
NG00205-PBS XX
NG00218-EOI XX
NG00239 XX
NG00241-ICI XX
NG00242-EOI XX
NG00246-PBS XX
NG00249-ICI XX
NG00261 XX
NG00269-EOI XX
NG00290-PBS XX
NG00292 XX
NG00293-EOI XX
NG00294-EOI XX
NG00295 XX
NG00296-EOI XX
NG00298-PBS XX
NG00302-EOI XX
NG00303 XX
NG00305-EOI XX
NG00309-EOI XX
NG00310-EOI XX
NG00316-EOI XX
NG00324-EOI XX
NG00327-EOI XX
NG00329-ICI-LATE XX
NG00334 XX
NG00343-ICI-LATE XX
NG00345 XX
NG00351-EOI XX
NG00386-ICI-LATE XX
NG00388-EOI XX
NG00389-EOI XX
NG00395 XX
NG00396-EOI XX
NG00432-PBS XX
NG00440-EOI XX
NG00442-ICI-LATE XX
NG00443-ICI-LATE XX
NG00446-EOI XX
NG00450-EOI XX
NG00451-EOI XX
NG00452-EOI XX
NG00453 XX
NG00485 XX
NG00494-ICI XX
NG00497-EOI XX
NG00498-ICI XX
NG00499-EOI XX
NG00507-PBS XX
NG00522-ICI-LATE XX
NG00524-ICI XX
NG00526-ICI-LATE XX
NG00527-EOI XX
NG00530-EOI XX
NG00532-EOI XX
NG00534-EOI XX
NG00538-PBS XX
NG00548-PBS XX
NG00551-ICI XX
NG00561-PBS XX
NG00565-EOI XX
NG00566-EOI XX
NG00567-EOI XX
NG00577-ICI XX
NG00578-EOI XX
NG00579-EOI XX
NG00586-PBS XX
NG00606-PBS XX
NG00617-EOI XX
NG00626-EOI XX
NG00628-EOI XX
NG00629-EOI XX
NG00630-ICI XX
NG00638-PBS XX
NG00647-PBS XX
NG00654-EOI XX
NG00657-EOI XX
NG00659-EOI XX
NG00662-ICI XX
NG00664-EOI XX
NG00665-EOI XX
NG00673-PBS XX
NG00674-PBS XX
NG00686-PBS XX
NG00695-EOI XX
NG00696-EOI XX
NG00698-ICI XX
NG00699-EOI XX
NG00736-EOI XX
NG00738-EOI XX
NG00777-ICI XX
NG00779-ICI XX
NG00781-EOI XX
NG00783-EOI XX
NG00790-ICI-LATE XX
NG00791-EOI XX
NG00792-EOI XX
NG00793-EOI XX
NG00794-EOI XX
NG00795-EOI XX
NG00796-ICI XX
NG00797-ICI XX
NG00798-EOI XX
NG00815-PBS XX
NG00826-EOI XX XX
NG00833-EOI XX
NG00835-EOI XX
NG00836-EOI XX
NG00852-ICI XX
NG00864-PBS XX
NG00872-ICI XX
NG00874-ICI-LATE XX
NG00877-ICI XX
NG00878-EOI XX
NG00920-ICI XX
NG00922-ICI XX
NG00923-ICI XX
NG00927-ICI XX
NG00929-ICI-LATE XX
NG00959-ICI XX
NG00971-ICI XX
NG00974-ICI XX
NG00985-ICI XX
NG00993-ICI XX
NG01018-ICI XX
NG01021-ICI XX
NG01022-ICI XX
NG01024-ICI XX
NG01026-ICI XX
NG01034-ICI XX
NG01036-ICI XX
NG01050-ICI XX
NG01105-ICI XX
NG01129-ICI XX
NG01131-ICI XX
NG01154-PBS XX
NG01182-PBS XX
NG01190-ICI XX
NG01204-PBS XX
NG01213-ICI XX
NG01225-ICI-LATE XX
NG01228-ICI XX
NG01229-ICI XX
NG01245-ICI XX
NG01287-ICI XX
NG01290-ICI XX
NG01294-ICI XX
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sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] readxl_1.3.1      cluster_2.1.0     dplyr_0.8.5       optparse_1.6.6   
 [5] rhdf5_2.28.1      tidyr_1.0.2       data.table_1.14.0 fst_0.9.2        
 [9] knitr_1.33        kableExtra_1.1.0  mclust_5.4.6      ggrepel_0.8.2    
[13] ggplot2_3.3.5     qtlcharts_0.11-6  qtl2_0.22         broman_0.70-4    
[17] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7        assertthat_0.2.1  rprojroot_1.3-2   digest_0.6.27    
 [5] utf8_1.2.1        cellranger_1.1.0  R6_2.5.0          backports_1.2.1  
 [9] RSQLite_2.2.7     evaluate_0.14     httr_1.4.1        highr_0.9        
[13] pillar_1.6.1      rlang_0.4.11      rstudioapi_0.13   whisker_0.4      
[17] blob_1.2.1        rmarkdown_2.1     labeling_0.4.2    qtl_1.46-2       
[21] webshot_0.5.2     readr_1.3.1       stringr_1.4.0     htmlwidgets_1.5.3
[25] bit_4.0.4         munsell_0.5.0     compiler_3.6.2    httpuv_1.5.2     
[29] xfun_0.24         pkgconfig_2.0.3   htmltools_0.5.1.1 tidyselect_1.0.0 
[33] tibble_3.1.2      fansi_0.5.0       viridisLite_0.4.0 crayon_1.4.1     
[37] withr_2.4.2       later_1.0.0       grid_3.6.2        jsonlite_1.7.2   
[41] gtable_0.3.0      lifecycle_1.0.0   DBI_1.1.1         git2r_0.26.1     
[45] magrittr_2.0.1    scales_1.1.1      stringi_1.7.2     cachem_1.0.5     
[49] farver_2.1.0      fs_1.4.1          promises_1.1.0    getopt_1.20.3    
[53] xml2_1.3.1        ellipsis_0.3.2    vctrs_0.3.8       Rhdf5lib_1.6.3   
[57] tools_3.6.2       bit64_4.0.5       glue_1.4.2        purrr_0.3.4      
[61] hms_0.5.3         parallel_3.6.2    fastmap_1.1.0     yaml_2.2.1       
[65] colorspace_2.0-2  rvest_0.3.5       memoise_2.0.0