Step 1: Sample QC [4.batches_myo]

workflowr

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Last updated: 2023-01-11

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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File paths: absolute

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/Users/corneb/Documents/MyJax/CS/Projects/Serreze/qc/workflowr/Serreze-T1D_Workflow	.

Repository version: c9fc66b

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    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-JAX00105915_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-UNC6020011_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC8099452_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC8161950_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9678100_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9678931_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-UNC12162881_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-backupUNC060363218_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNC12719038_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNCHS022024_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNC14948439_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNCHS023592_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC16009822_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC17271730_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNC31358512_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNC18805053_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNCHS029427_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031753_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031802_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-JAX00326005_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
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    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-backupUNC060363218_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNC12719038_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
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    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC17271730_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNC31358512_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
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    Untracked:  data/ici.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
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    Untracked:  output/Proportion_matching_genotypes_before_removal_of_bad_samples_4.batches_myo.pdf
    Untracked:  output/genotype_error_marker_4.batches_myo.pdf
    Untracked:  output/genotype_frequency_marker_4.batches_myo.pdf

Unstaged changes:
    Modified:   analysis/index_5.batches.Rmd

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.

---

There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

---

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(paste0(filepaths,"/gm_serreze_bc_4.batches_myo_BC217.RData")))
gm

Warning in check_cross2(object): 1249 invalid genotypes in cross

Object of class cross2 (crosstype "bc")

Total individuals               217
No. genotyped individuals       217
No. phenotyped individuals      217
No. with both geno & pheno      217

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] 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_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_4.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	percent_missing
7363-PBS-SICK	12.3971701217506
7917-PBS-EOI	26.244428490233
8172-PBS-EOI	18.566214963056
D1016-PD1-SICK	13.9452271979419
D1223-PD1-SICK	16.256842860989
D345-ICI-SICK	12.5557150976697
D351-ICI-Myo	12.6649017320291
D611-ICI-Myo	16.431092763768
D631-ICI-SICK	10.1880104101229

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:

Neogen_Sample_ID	Sample_ID	Sex	Inferred.Sex
D63-ICI-Myo	The_Jackson_Lab_Serreze_MURGIGV01_20220729_D63-ICI-Myo_B7	M	F
D38-ICI-Myo	The_Jackson_Lab_Serreze_MURGIGV01_20220826_D38-ICI-Myo_B9	F	M
D351-ICI-Myo	The_Jackson_Lab_Serreze_MURGIGV01_20220826_D351-ICI-Myo_C10	F	M
D320-ICI-SICK	The_Jackson_Lab_Serreze_MURGIGV01_20221006_D320-ICI-SICK_H3	F	M
D611-ICI-Myo	The_Jackson_Lab_Serreze_MURGIGV01_20221006_D611-ICI-Myo_D6	F	M
7363-PBS-SICK	The_Jackson_Lab_Serreze_MURGIGV01_20221116_7363-PBS-SICK_C11	F	M
8172-PBS-EOI	The_Jackson_Lab_Serreze_MURGIGV01_20221116_8172-PBS-EOI_B12	F	M
D1223-PD1-SICK	The_Jackson_Lab_Serreze_MURGIGV01_20221116_D1223-PD1-SICK_E12	F	M
7917-PBS-EOI	The_Jackson_Lab_Serreze_MURGIGV01_20221116_7917-PBS-EOI_E5	F	M
8144-PBS-EOI	The_Jackson_Lab_Serreze_MURGIGV01_20221116_8144-PBS-EOI_E8	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_4.batches_myo.RData")

pdf(file = "output/Proportion_matching_genotypes_before_removal_of_bad_samples_4.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_4.batches_myo.RData
#load("data/intensities.fst_4.batches_myo.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_myo.RData
#load("data/heh/intensities.fst_4.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)]
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_4.batches_myo_corrected/e_bc_4.batches_myo_BC217.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_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$sex, TRUE, FALSE)
rownames(samples) <- as.character(samples$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 == 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_4.batches_myo.RData")

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

gm_samqc

Warning in check_cross2(object): 1249 invalid genotypes in cross

Object of class cross2 (crosstype "bc")

Total individuals               208
No. genotyped individuals       208
No. phenotyped individuals      208
No. with both geno & pheno      208

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_4.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_4.batches_myo.RData")
save(e, file = "data/e_snpg_samqc_4.batches_myo.RData")

Here is the list of samples that were removed:

Sample_ID
7363-PBS-SICK
7917-PBS-EOI
8172-PBS-EOI
D1016-PD1-SICK
D1223-PD1-SICK
D345-ICI-SICK
D351-ICI-Myo
D611-ICI-Myo
D631-ICI-SICK

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

• no_pheno == sample does not have a phenotype (if applicable)
• high_miss == sample has higher than 10% missing genotypes
• diff_sex == sample has sex mismatch
• high_geno_errors == Sample has geno errors above 10%
• dublicate_ID == sample was duplicated (or highly concordant)

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
6404-PBS-EOI				XX
6411-PBS-Myo				XX
6603-PBS-EOI				XX
6614-PBS-EOI				XX
6704-PBS-EOI				XX
6705-PBS-EOI				XX
6707-PBS-EOI				XX
6708-PBS-EOI				XX
6863-PBS-Myo				XX
6872-PBS-Myo				XX
6874-PBS-Myo				XX
6890-PBS-EOI				XX
6892-PBS-EOI				XX
6894-PBS-EOI				XX
6964-PBS-EOI				XX
7168-ICI-Myo				XX
7169-ICI-Myo				XX
7175-PD1-EOI				XX
7176-ICI-Myo				XX
7179-ICI-Myo				XX
7181-PBS-EOI				XX
7182-PBS-Myo				XX
7269-PBS-Myo				XX
7276-PBS-EOI				XX
7329-ICI-Myo				XX
7333-ICI-EOI				XX
7338-PD1-EOI				XX
7340-PD1-EOI				XX
7346-PBS-Myo				XX
7348-PBS-EOI				XX
7351-PBS-EOI				XX
7357-PBS-EOI				XX
7358-PBS-EOI				XX
7359-PBS-Myo				XX
7360-PBS-SICK				XX
7363-PBS-SICK	XX	XX
7364-PBS-EOI				XX
7782-ICI-Myo				XX
7786-ICI-Myo				XX
7788-ICI-Myo				XX
7789-ICI-Myo				XX
7792-ICI-Myo				XX
7805-PBS-EOI				XX
7904-PBS-EOI				XX
7911-PBS-EOI				XX
7912-PBS-EOI				XX
7915-PBS-EOI				XX
7917-PBS-EOI	XX	XX	XX
7921-PBS-EOI				XX
7924-ICI-EOI				XX
7927-ICI-Myo				XX
7937-ICI-EOI				XX
8144-PBS-EOI		XX		XX
8149-PBS-EOI				XX
8153-PBS-EOI				XX
8169-PBS-EOI				XX
8172-PBS-EOI	XX	XX
8604-PBS-Myo				XX
8608-PBS-EOI				XX
8615-PBS-EOI				XX
8626-PBS-EOI				XX
8829-PBS-EOI				XX
8839-PBS-Myo				XX
8840-PBS-EOI				XX
8851-PBS-SICK				XX
D1-PBS-Myo				XX
D100-ICI-EOI				XX
D1014-PD1-SICK				XX
D1016-PD1-SICK	XX
D1062-PBS-SICK				XX
D1086-PD1-SICK				XX
D1088-PD1-SICK				XX
D110-PBS-EOI				XX
D1104-PD1-SICK				XX
D1108-PD1-SICK				XX
D1109-PD1-SICK				XX
D1110-PD1-SICK				XX
D113-PBS-EOI				XX
D114-PBS-EOI				XX
D1142-PD1-SICK				XX
D115-ICI-EOI				XX
D1154-PD1-SICK				XX
D118-ICI-EOI				XX
D12-ICI-EOI				XX
D1206-PD1-SICK				XX
D1208-PD1-SICK				XX
D121-ICI-Myo				XX
D1223-PD1-SICK	XX	XX
D1262-PD1-SICK				XX
D127-PBS-EOI				XX
D128-PBS-Myo				XX
D1280-PD1-SICK				XX
D1281-PD1-SICK				XX
D1283-PD1-SICK				XX
D1285-PD1-SICK				XX
D1290-PD1-SICK				XX
D13-ICI-EOI				XX
D132-PBS-EOI				XX
D137-PBS-EOI				XX
D1422-PD1-SICK				XX
D1452-PD1-SICK				XX
D1454-PD1-SICK				XX
D158-PBS-EOI				XX
D159-PBS-EOI				XX
D162-PBS-EOI				XX
D183-PBS-EOI				XX
D19-PBS-EOI				XX
D2-PBS-EOI				XX
D20-PBS-EOI				XX
D206-PBS-EOI				XX
D21-PBS-EOI				XX
D24-ICI-EOI				XX
D250-PBS-EOI				XX
D251-PBS-Myo				XX
D30-ICI-Myo				XX
D300-ICI-SICK				XX
D304-ICI-Myo				XX
D309-ICI-Myo				XX
D31-PBS-Myo				XX
D313-ICI-Myo				XX
D315-PD1-SICK				XX
D318-ICI-Myo				XX
D320-ICI-SICK		XX		XX
D329-PBS-SICK				XX
D343-ICI-SICK				XX
D344-ICI-Myo				XX
D345-ICI-SICK	XX
D35-PBS-EOI				XX
D351-ICI-Myo	XX	XX
D357-PD1-SICK				XX
D362-ICI-Myo				XX
D37-PBS-EOI				XX
D373-ICI-Myo				XX
D374-ICI-Myo				XX
D38-ICI-Myo		XX		XX
D386-ICI-Myo				XX
D398-ICI-Myo				XX
D4-PBS-EOI				XX
D40-ICI-Myo				XX
D402-ICI-Myo				XX
D408-ICI-Myo				XX
D410-ICI-Myo				XX
D436-PBS-Myo				XX
D463-ICI-SICK				XX
D467-ICI-Myo				XX
D491-PBS-SICK				XX
D50-PBS-EOI				XX
D503-ICI-Myo				XX
D506-ICI-Myo				XX
D509-ICI-Myo				XX
D517-ICI-Myo				XX
D529-ICI-Myo				XX
D532-ICI-SICK				XX
D538-PD1-SICK				XX
D548-ICI-Myo				XX
D551-ICI-SICK				XX
D552-ICI-Myo				XX
D558-ICI-SICK				XX
D563-ICI-Myo				XX
D6-PBS-Myo				XX
D60-PBS-EOI				XX
D61-ICI-EOI				XX
D611-ICI-Myo	XX	XX		XX
D62-ICI-Myo				XX
D629-PD1-SICK				XX
D63-ICI-Myo		XX		XX
D631-ICI-SICK	XX
D66-ICI-Myo				XX
D667-ICI-Myo				XX
D678-ICI-Myo				XX
D679-ICI-SICK				XX
D682-ICI-Myo				XX
D69-PBS-EOI				XX
D699-ICI-Myo				XX
D70-PBS-EOI				XX
D704-PD1-SICK				XX
D727-PBS-Myo				XX
D74-ICI-EOI				XX
D743-ICI-Myo				XX
D75-ICI-EOI				XX
D752-PD1-SICK				XX
D755-ICI-SICK				XX
D761-PD1-SICK				XX
D767-ICI-Myo				XX
D8-PBS-Myo				XX
D82-PBS-EOI				XX
D821-ICI-Myo				XX
D83-PBS-EOI				XX
D857-PD1-SICK				XX
D86-ICI-Myo				XX
D868-ICI-Myo				XX
D870-ICI-Myo				XX
D879-ICI-Myo				XX
D882-ICI-Myo				XX
D89-ICI-Myo				XX
D90-ICI-EOI				XX
D927-ICI-Myo				XX
D931-ICI-Myo				XX
D934-ICI-Myo				XX
D964-PD1-SICK				XX
D969-PD1-SICK				XX
D975-PD1-SICK				XX
D976-PD1-SICK				XX
D981-PD1-SICK				XX
D985-ICI-Myo				XX
D993-PBS-Myo				XX
NM00155-ICI-Myo				XX
NM00161-ICI-EOI				XX
NM00170-PBS-EOI				XX
NM00174-PBS-Myo				XX
NM00175-ICI-EOI				XX
NM00180-PBS-Myo				XX
NM00181-PBS-Myo				XX
NM00183-PBS-Myo				XX
NM00184-ICI-Myo				XX
NM00185-ICI-Myo				XX

Session information

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] readxl_1.4.1      cluster_2.1.4     dplyr_1.0.10      optparse_1.7.3   
 [5] rhdf5_2.40.0      tidyr_1.2.1       data.table_1.14.6 fst_0.9.8        
 [9] knitr_1.41        kableExtra_1.3.4  mclust_6.0.0      ggrepel_0.9.2    
[13] ggplot2_3.4.0     qtlcharts_0.16    qtl2_0.30         broman_0.80      
[17] 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        git2r_0.30.1      
[33] tibble_3.1.8       farver_2.1.1       generics_0.1.3     ellipsis_0.3.2    
[37] cachem_1.0.6       withr_2.5.0        cli_3.4.1          magrittr_2.0.3    
[41] memoise_2.0.1      evaluate_0.18      ps_1.7.2           fs_1.5.2          
[45] fansi_1.0.3        xml2_1.3.3         tools_4.2.2        lifecycle_1.0.3   
[49] stringr_1.5.0      Rhdf5lib_1.18.2    munsell_0.5.0      callr_3.7.3       
[53] compiler_4.2.2     jquerylib_0.1.4    systemfonts_1.0.4  rlang_1.0.6       
[57] grid_4.2.2         fstcore_0.9.12     rhdf5filters_1.8.0 rstudioapi_0.14   
[61] htmlwidgets_1.5.4  labeling_0.4.2     rmarkdown_2.18     gtable_0.3.1      
[65] DBI_1.1.3          R6_2.5.1           fastmap_1.1.0      bit_4.0.5         
[69] utf8_1.2.2         rprojroot_2.0.3    stringi_1.7.8      parallel_4.2.2    
[73] Rcpp_1.0.9         vctrs_0.5.1        tidyselect_1.2.0   xfun_0.35