Last updated: 2023-04-13

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Knit directory: Serreze-T1D_Workflow/

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    Untracked:  data/sample_geno_AHB_7.batches_myo_orig.id.csv
    Untracked:  data/sample_geno_bc_4.batches_myo.csv
    Untracked:  data/sample_geno_bc_7.batches_myo.csv
    Untracked:  data/sample_geno_bc_7.batches_myo_orig.id.csv
    Untracked:  data/serreze_probs_4.batches_myo.rds
    Untracked:  data/serreze_probs_7.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_4.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_4.batches_myo_mis.rds
    Untracked:  data/serreze_probs_allqc_7.batches_myo.rds
    Untracked:  data/serreze_probs_allqc_7.batches_myo_mis.rds
    Untracked:  data/summary.cg_4.batches_myo.RData
    Untracked:  data/summary.cg_7.batches_myo.RData
    Untracked:  output/Percent_missing_genotype_data_4.batches_myo.pdf
    Untracked:  output/Percent_missing_genotype_data_7.batches_myo.pdf
    Untracked:  output/Percent_missing_genotype_data_per_marker_4.batches_myo.pdf
    Untracked:  output/Percent_missing_genotype_data_per_marker_7.batches_myo.pdf
    Untracked:  output/Proportion_matching_genotypes_before_removal_of_bad_samples_4.batches_myo.pdf
    Untracked:  output/Proportion_matching_genotypes_before_removal_of_bad_samples_7.batches_myo.pdf
    Untracked:  output/genotype_error_marker_4.batches_myo.pdf
    Untracked:  output/genotype_error_marker_7.batches_myo.pdf
    Untracked:  output/genotype_frequency_marker_4.batches_myo.pdf
    Untracked:  output/genotype_frequency_marker_7.batches_myo.pdf

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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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("data/gm_serreze_bc_7.batches_myo.RData"))
gm
Warning in check_cross2(object): 1249 invalid genotypes in cross
Object of class cross2 (crosstype "bc")

Total individuals               366
No. genotyped individuals       366
No. phenotyped individuals      366
No. with both geno & pheno      366

No. phenotypes                    1
No. covariates                   11
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  6471  7276  6226  6177 
   14    15    16    17    18    19     X 
 6082  5421  5075  5162  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$Original.Mouse.ID))
[1] "366 string mismatches"
all.equal(as.character(ind_ids(gm)), as.character(samples$Unique.Sample.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_7.batches_myo.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_7.batches_myo.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 Unique_ID batch.date percent_missing
D351-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220826_D351-ICI-Myo_C10 2022-08-26 12.6649017320291
D345-ICI-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221006_D345-ICI-SICK_H5 2022-10-06 12.5557150976697
D611-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20221006_D611-ICI-Myo_D6 2022-10-06 16.431092763768
D631-ICI-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221006_D631-ICI-SICK_A12 2022-10-06 10.1880104101229
7363-PBS-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221116_7363-PBS-SICK_C11 2022-11-16 12.3971701217506
7917-PBS-EOI The_Jackson_Lab_Serreze_MURGIGV01_20221116_7917-PBS-EOI_E5 2022-11-16 26.244428490233
8172-PBS-EOI The_Jackson_Lab_Serreze_MURGIGV01_20221116_8172-PBS-EOI_B12 2022-11-16 18.566214963056
D1016-PD1-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221116_D1016-PD1-SICK_F11 2022-11-16 13.9452271979419
D1223-PD1-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221116_D1223-PD1-SICK_E12 2022-11-16 16.256842860989

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)
#colnames(x) <- as.character(ind_ids(gm))
y <- h5read(hdf5_filename, "Y") # Y channel intensities
y <- do.call(cbind, y)
#colnames(y) <- as.character(ind_ids(gm))
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(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,7])] + y[chrx,as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,7])], na.rm = T)
chry_int <- colMeans(x[chry,as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,7])] + y[chry,as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,7])], na.rm = T)

all.equal(as.character(ind_ids(gm)), as.character(samples$Unique.Sample.ID))
[1] TRUE
#sex order
#samples$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(do.call(rbind.data.frame, strsplit(names(phetX), "_"))[,7]) %in% names(chrx_int)]
names(phetX) <- as.character(do.call(rbind.data.frame, strsplit(names(phetX), "_"))[,7])
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_ID Unique_ID batch.date Sex Inferred.Sex
D63-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220729_D63-ICI-Myo_B7 2022-07-29 M F
D38-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220826_D38-ICI-Myo_B9 2022-08-26 F M
D351-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220826_D351-ICI-Myo_C10 2022-08-26 F M
D320-ICI-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221006_D320-ICI-SICK_H3 2022-10-06 F M
D611-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20221006_D611-ICI-Myo_D6 2022-10-06 F M
7363-PBS-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221116_7363-PBS-SICK_C11 2022-11-16 F M
D1016-PD1-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221116_D1016-PD1-SICK_F11 2022-11-16 F M
8172-PBS-EOI The_Jackson_Lab_Serreze_MURGIGV01_20221116_8172-PBS-EOI_B12 2022-11-16 F M
D1223-PD1-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221116_D1223-PD1-SICK_E12 2022-11-16 F M
8626-PBS-EOI The_Jackson_Lab_Serreze_MURGIGV01_20221116_8626-PBS-EOI_H12 2022-11-16 F M
7917-PBS-EOI The_Jackson_Lab_Serreze_MURGIGV01_20221116_7917-PBS-EOI_E5 2022-11-16 F M
8144-PBS-EOI The_Jackson_Lab_Serreze_MURGIGV01_20221116_8144-PBS-EOI_E8 2022-11-16 F M
D981-PD1-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221116_D981-PD1-SICK_A9 2022-11-16 F M
7167-ICI-EOI The_Jackson_Lab_Serreze_MURGIGV01_20230225_7167-ICI-EOI_E11 2023-02-25 F M

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_7.batches_myo.RData")

pdf(file = "output/Proportion_matching_genotypes_before_removal_of_bad_samples_7.batches_myo.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_7.batches_myo.RData
#load("data/intensities.fst_7.batches_myo.RData")

xn <- x[,as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,7])]
xn <- xn[snps$marker,]
xnm <- rownames(xn)

yn <- y[,as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,7])]
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_7.batches_myo.RData
#load("data/heh/intensities.fst_7.batches_myo.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)]
names(int) <- gsub("\\.1","", names(int))
int <- int[,as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,7])]
names(int) <- gsub("\\.1","", names(int))
names(int) <- as.character(names(percent_missing))
names(percent_missing) <- as.character(names(percent_missing))
n <- names(sort(percent_missing[intersect(as.character(ind_ids(gm)), colnames(int))], decreasing=TRUE))
tn = int[,n]
iboxplot(log10(t(tn)+1), orderByMedian=FALSE, chartOpts=list(ylab="log10(SNP intensity + 1)"))
names(tn) <- as.character(do.call(rbind.data.frame, strsplit(names(tn), "_"))[,7])
iboxplot(log10(t(tn)+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(as.character(do.call(rbind.data.frame, strsplit(colnames(qu), "_"))[,7]), " (", 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("data/e_7.batches_myo.RData")
errors_ind <- rowSums(e>2)[rownames(gm$covar)]/n_typed(gm)*100
lab <- paste0(as.character(do.call(rbind.data.frame, strsplit(names(errors_ind), "_"))[,7]), " (", myround(percent_missing[as.character(rownames(gm$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_7.batches_myo.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$sex, TRUE, FALSE)
rownames(samples) <- as.character(samples$Unique.Sample.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$Unique.Sample.ID))
[1] TRUE
qc_info$sex.match <- ifelse((samples$Inferred.Sex == samples$Sex), 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_7.batches_myo.RData")

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

gm_samqc
Warning in check_cross2(object): 1249 invalid genotypes in cross
Object of class cross2 (crosstype "bc")

Total individuals               357
No. genotyped individuals       357
No. phenotyped individuals      357
No. with both geno & pheno      357

No. phenotypes                    1
No. covariates                   11
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  6471  7276  6226  6177 
   14    15    16    17    18    19     X 
 6082  5421  5075  5162  4682  3612  4857 
save(gm_samqc, file = "data/gm_samqc_7.batches_myo.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_7.batches_myo.RData")
save(e, file = "data/e_snpg_samqc_7.batches_myo.RData")

Here is the list of samples that were removed:

Sample_ID Unique_ID batch.date
D351-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220826_D351-ICI-Myo_C10 2022-08-26
D345-ICI-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221006_D345-ICI-SICK_H5 2022-10-06
D611-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20221006_D611-ICI-Myo_D6 2022-10-06
D631-ICI-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221006_D631-ICI-SICK_A12 2022-10-06
7363-PBS-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221116_7363-PBS-SICK_C11 2022-11-16
7917-PBS-EOI The_Jackson_Lab_Serreze_MURGIGV01_20221116_7917-PBS-EOI_E5 2022-11-16
8172-PBS-EOI The_Jackson_Lab_Serreze_MURGIGV01_20221116_8172-PBS-EOI_B12 2022-11-16
D1016-PD1-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221116_D1016-PD1-SICK_F11 2022-11-16
D1223-PD1-SICK The_Jackson_Lab_Serreze_MURGIGV01_20221116_D1223-PD1-SICK_E12 2022-11-16

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 Unique_ID batch.date high_miss diff_sex high_geno.errors highly_concordant
6411-PBS-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220729_6411-PBS-Myo_D11 19202 XX
6863-PBS-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220729_6863-PBS-Myo_D12 19202 XX
7169-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220729_7169-ICI-Myo_E5 19202 XX
7179-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220729_7179-ICI-Myo_E6 19202 XX
7269-PBS-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220729_7269-PBS-Myo_E7 19202 XX
7333-ICI-EOI The_Jackson_Lab_Serreze_MURGIGV01_20220729_7333-ICI-EOI_E8 19202 XX
7782-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220729_7782-ICI-Myo_E9 19202 XX
7789-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20220729_7789-ICI-Myo_E10 19202 XX
7924-ICI-EOI The_Jackson_Lab_Serreze_MURGIGV01_20220729_7924-ICI-EOI_E11 19202 XX
7937-ICI-EOI The_Jackson_Lab_Serreze_MURGIGV01_20220729_7937-ICI-EOI_E12 19202 XX
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D928-ICI-Myo The_Jackson_Lab_Serreze_MURGIGV01_20230313_D928-ICI-Myo_G1 19429 XX
D947-PBS-EOI The_Jackson_Lab_Serreze_MURGIGV01_20230313_D947-PBS-EOI_G2 19429 XX
D948-PBS-EOI The_Jackson_Lab_Serreze_MURGIGV01_20230313_D948-PBS-EOI_G3 19429 XX 

sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

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

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

other attached packages:
 [1] lubridate_1.9.1   readxl_1.4.1      cluster_2.1.4     dplyr_1.0.10     
 [5] optparse_1.7.3    rhdf5_2.40.0      tidyr_1.2.1       data.table_1.14.6
 [9] fst_0.9.8         knitr_1.41        kableExtra_1.3.4  mclust_6.0.0     
[13] ggrepel_0.9.2     ggplot2_3.4.0     qtlcharts_0.16    qtl2_0.30        
[17] broman_0.80       workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] httr_1.4.4         sass_0.4.4         bit64_4.0.5        jsonlite_1.8.4    
 [5] viridisLite_0.4.1  bslib_0.4.1        assertthat_0.2.1   getPass_0.2-2     
 [9] highr_0.9          blob_1.2.3         cellranger_1.1.0   yaml_2.3.6        
[13] pillar_1.8.1       RSQLite_2.2.19     glue_1.6.2         digest_0.6.30     
[17] promises_1.2.0.1   rvest_1.0.3        colorspace_2.0-3   htmltools_0.5.3   
[21] httpuv_1.6.6       pkgconfig_2.0.3    purrr_0.3.5        scales_1.2.1      
[25] webshot_0.5.4      processx_3.8.0     svglite_2.1.0      qtl_1.54          
[29] whisker_0.4.1      getopt_1.20.3      later_1.3.0        timechange_0.1.1  
[33] git2r_0.30.1       tibble_3.1.8       farver_2.1.1       generics_0.1.3    
[37] ellipsis_0.3.2     cachem_1.0.6       withr_2.5.0        cli_3.4.1         
[41] magrittr_2.0.3     memoise_2.0.1      evaluate_0.18      ps_1.7.2          
[45] fs_1.5.2           fansi_1.0.3        xml2_1.3.3         tools_4.2.2       
[49] lifecycle_1.0.3    stringr_1.5.0      Rhdf5lib_1.18.2    munsell_0.5.0     
[53] callr_3.7.3        compiler_4.2.2     jquerylib_0.1.4    systemfonts_1.0.4 
[57] rlang_1.0.6        grid_4.2.2         fstcore_0.9.12     rhdf5filters_1.8.0
[61] rstudioapi_0.14    htmlwidgets_1.5.4  labeling_0.4.2     rmarkdown_2.18    
[65] gtable_0.3.1       DBI_1.1.3          R6_2.5.1           fastmap_1.1.0     
[69] bit_4.0.5          utf8_1.2.2         rprojroot_2.0.3    stringi_1.7.8     
[73] parallel_4.2.2     Rcpp_1.0.9         vctrs_0.5.1        tidyselect_1.2.0  
[77] xfun_0.35