Step 1: Sample QC [5 batches]
Belinda Cornes
2022-04-06
Last updated: 2022-04-06
Checks: 5 2
Knit directory: Serreze-T1D_Workflow/
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Unstaged changes:
Modified: analysis/4.1.1_qtl.analysis_binary_ici.vs.eoi_snpsqc_dis_no-x_updated.Rmd
Modified: analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated.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("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/gm_serreze.312_bc.RData"))
gmObject of class cross2 (crosstype "bc")
Total individuals 312
No. genotyped individuals 312
No. phenotyped individuals 312
No. with both geno & pheno 312
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$Original.Mouse.ID))[1] TRUEMissing Data
percent_missing <- n_missing(gm, "ind", "prop")*100
#labels <- paste0(as.character(do.call(rbind.data.frame, strsplit(names(percent_missing), "_"))[,7]), " (", round(percent_missing,2), "%)")
labels <- paste0(names(percent_missing), " (", round(percent_missing,2), "%)")
iplot(seq_along(percent_missing), percent_missing, indID=labels,
chartOpts=list(xlab="Mouse", ylab="Percent missing genotype data",
ylim=c(0, 70)))Set screen size to height=700 x width=1000#save into pdf
pdf(file = "output/Percent_missing_genotype_data_5.batches.pdf", width = 20, height = 20)
#labels <- as.character(do.call(rbind.data.frame, strsplit(names(totxo), "V01_"))[,2])
#labels <- as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm), "_"))[,7])
#labels <- paste0(names(percent_missing), " (", round(percent_missing,2), "%)")
labels <- ind_ids(gm)
labels[percent_missing < 10] = ""
# Change point shapes and colors
p <- ggplot(data = data.frame(Mouse=seq_along(percent_missing),
Percent_missing_genotype_data = percent_missing,
batch = factor(as.character(do.call(rbind.data.frame, strsplit(as.character(samples$Unique.Sample.ID), "_"))[,6]))
#batch = factor(as.character(do.call(rbind.data.frame, strsplit(as.character(samples$Directory), "_"))[,5]))
),
aes(x=Mouse, y=Percent_missing_genotype_data, color = batch)) +
geom_point() +
geom_hline(yintercept=10, linetype="solid", color = "red") +
geom_text_repel(aes(label=labels), vjust = 0, nudge_y = 0.01, show.legend = FALSE, size=3) +
theme(text = element_text(size = 10))
p
dev.off()quartz_off_screen
2 p
save(percent_missing,file = "data/percent_missing_id_5.batches.RData")
gm.covar = data.frame(id=rownames(gm$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$X != 0)
phetX <- phetX[as.character(ind_ids(gm)) %in% names(chrx_int)]
names(phetX) <- as.character(ind_ids(gm))
iplot(chrx_int, phetX, group=sex, indID=labels,
chartOpts=list(pointcolor=point_colors, pointsize=4,
xlab="Average X chr intensity", ylab="Proportion het on X chr"))In the above scatterplot, we show the proportion of hets vs the average intensity for the X chromosome SNPs. In calculating the proportion of heterozygous genotypes for the individuals, we look at X chromosome genotypes equal to 2 which corresponds to the heterozygote) relative to not being 0 (which is used to encode missing genotypes). The genotypes are arranged with rows being individuals and columns being markers.
The following are the mice that have had sex incorrectly assigned:
Sample Duplicates
cg <- compare_geno(gm, cores=10)
summary.cg <- summary(cg)Here is a histogram of the proportion of matching genotypes. The tick marks below the histogram indicate individual pairs.
save(summary.cg,file = "data/summary.cg_5.batches.RData")
pdf(file = "output/Proportion_matching_genotypes_before_removal_of_bad_samples_5.batches.pdf", width = 20, height = 20)
par(mar=c(5.1,0.6,0.6, 0.6))
hist(cg[upper.tri(cg)], breaks=seq(0, 1, length=201),
main="", yaxt="n", ylab="", xlab="Proportion matching genotypes")
rug(cg[upper.tri(cg)])
dev.off()quartz_off_screen
2 par(mar=c(5.1,0.6,0.6, 0.6))
hist(cg[upper.tri(cg)], breaks=seq(0, 1, length=201),
main="", yaxt="n", ylab="", xlab="Proportion matching genotypes")
rug(cg[upper.tri(cg)])
cgsub <- cg[percent_missing < 10, percent_missing < 10]
par(mar=c(5.1,0.6,0.6, 0.6))
hist(cgsub[upper.tri(cgsub)], breaks=seq(0, 1, length=201),
main="", yaxt="n", ylab="", xlab="Proportion matching genotypes [percent missing < 10%]")
rug(cgsub[upper.tri(cgsub)])
Array Intensities
#load the intensities.fst_5.batches.RData
#load("data/intensities.fst_5.batches.RData")
xn <- x[,as.character(ind_ids(gm))]
xn <- xn[snps$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_5.batches.RData
#load("data/heh/intensities.fst_5.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)"))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")))Genotyping Error LOD Scores
load("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/e_bc.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_5.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$Original.Mouse.ID))[1] TRUEqc_info$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$genotype_erros >= 8,]
save(qc_info, bad.sample, file = "data/qc_info_bad_sample_5.batches.RData")
gm_samqc <- gm[paste0("-",as.character(bad.sample$id.1)),]
gm_samqcObject of class cross2 (crosstype "bc")
Total individuals 308
No. genotyped individuals 308
No. phenotyped individuals 308
No. with both geno & pheno 308
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_5.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_5.batches.RData")
save(e, file = "data/e_snpg_samqc_5.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:
- 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 |
|---|---|---|---|---|
| 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 | |||
| NG00873-EOI | XX | |||
| NG00874-ICI-LATE | XX | |||
| NG00877-ICI | XX | |||
| NG00878-EOI | XX | |||
| NG00882-EOI | XX | |||
| NG00883-EOI | XX | |||
| NG00884-EOI | XX | |||
| NG00888-EOI | XX | |||
| NG00901-PBS | XX | |||
| NG00915-EOI | XX | |||
| NG00920-ICI | XX | |||
| NG00922-ICI | XX | |||
| NG00923-ICI | XX | |||
| NG00924-EOI | XX | |||
| NG00925-EOI | XX | |||
| NG00927-ICI | XX | |||
| NG00929-ICI-LATE | XX | |||
| NG00932-EOI | XX | |||
| NG00933-EOI | XX | |||
| NG00947-PBS | XX | |||
| NG00959-ICI | XX | |||
| NG00960-EOI | XX | |||
| NG00961-EOI | XX | |||
| NG00963-EOI | XX | |||
| NG00970-EOI | XX | |||
| NG00971-ICI | XX | |||
| NG00973-EOI | XX | |||
| NG00974-ICI | XX | |||
| NG00980-EOI | XX | |||
| NG00981-EOI | XX | |||
| NG00985-ICI | XX | |||
| NG00986-EOI | XX | |||
| NG00987-EOI | XX | |||
| NG00992-EOI | XX | |||
| NG00993-ICI | XX | |||
| NG00998-PBS | XX | |||
| NG01012-PBS | XX | |||
| NG01018-ICI | XX | |||
| NG01019-EOI | XX | |||
| NG01020-ICI-LATE | XX | |||
| NG01021-ICI | XX | |||
| NG01022-ICI | XX | |||
| NG01024-ICI | XX | |||
| NG01026-ICI | XX | |||
| NG01028-EOI | XX | |||
| NG01034-ICI | XX | |||
| NG01036-ICI | XX | |||
| NG01040-EOI | XX | |||
| NG01043-PBS | XX | |||
| NG01050-ICI | XX | |||
| NG01055-PBS | XX | |||
| NG01063-PBS | XX | |||
| NG01102-EOI | XX | |||
| NG01104-EOI | XX | |||
| NG01105-ICI | XX | |||
| NG01106-EOI | XX | |||
| NG01107-EOI | XX | |||
| NG01127-EOI | XX | |||
| NG01129-ICI | XX | |||
| NG01130-EOI | XX | |||
| NG01131-ICI | XX | |||
| NG01151-PBS | XX | |||
| NG01154-PBS | XX | |||
| NG01156-PBS | XX | |||
| NG01180-PBS | XX | |||
| NG01182-PBS | XX | |||
| NG01190-ICI | XX | |||
| NG01204-PBS | XX | |||
| NG01213-ICI | XX | |||
| NG01222-PBS | XX | |||
| NG01225-ICI-LATE | XX | |||
| NG01227-EOI | XX | |||
| NG01228-ICI | XX | |||
| NG01229-ICI | XX | |||
| NG01235-PBS | XX | |||
| NG01243-EOI | XX | |||
| NG01244-EOI | XX | |||
| NG01245-ICI | XX | |||
| NG01272-PBS | XX | |||
| NG01283-EOI | XX | |||
| NG01285-EOI | XX | |||
| NG01287-ICI | XX | |||
| NG01288-EOI | XX | |||
| NG01289-EOI | XX | |||
| NG01290-ICI | XX | |||
| NG01293-EOI | XX | |||
| NG01294-ICI | XX | |||
| NG01299-ICI | XX | |||
| NG01300-EOI | XX | |||
| NG01301-EOI | XX | |||
| NG01302-EOI | XX | |||
| NG01303-ICI-LATE | XX | |||
| NG01310-ICI | XX | |||
| NG01311-ICI | XX | |||
| NG01312-EOI | XX | |||
| NG01317-EOI | XX | |||
| NG01326-EOI | XX | |||
| NG01329-EOI | XX | |||
| NG01332-ICI | XX | |||
| NG01336-EOI | XX | |||
| NG01340-EOI | XX | |||
| NG01343-ICI | XX | |||
| NG01346-EOI | XX | |||
| NG01351-EOI | XX | |||
| NG01353-EOI | XX | |||
| NG01356-EOI | XX | |||
| NG01358-ICI | XX | |||
| NG01360-EOI | XX | |||
| NG01363-EOI | XX | |||
| NG01368-EOI | XX | |||
| NG01369-ICI | XX | |||
| NG01370-ICI | XX | |||
| NG01371-EOI | XX | |||
| NG01372-EOI | XX | |||
| NG01374-ICI | XX | |||
| NG01375-EOI | XX | |||
| NG01377-ICI | XX | |||
| NG01379-EOI | XX | |||
| NG01382-EOI | XX | |||
| NG01383-ICI | XX | |||
| NG01385-EOI | XX | |||
| NG01386-ICI | XX | |||
| NG01387-EOI | XX | |||
| NG01388-EOI | XX | |||
| NG01389-ICI-LATE | XX | |||
| NG01391-ICI | XX | |||
| NG01393-EOI | XX | |||
| NG01396-EOI | XX | |||
| NG01397-ICI-LATE | XX | |||
| NG01398-ICI | XX | |||
| NG01411-EOI | XX | |||
| NG01413-EOI | XX | |||
| NG01418-EOI | XX | |||
| NG01420-EOI | XX | |||
| NG01421-ICI | XX | |||
| NG01422-ICI | XX | |||
| NG01424-EOI | XX | |||
| NG01426-EOI | XX | |||
| NG01427-EOI | XX | |||
| NG01429-EOI | XX | |||
| NG01431-EOI | XX | |||
| NG01434-EOI | XX | |||
| NG01435-ICI | XX | |||
| NG01437-EOI | XX | |||
| NG01439-ICI | XX | |||
| NG01444-ICI | XX | |||
| NG01445-EOI | XX | |||
| NG01446-EOI | XX | |||
| NG01447-EOI | XX | |||
| NG01451-EOI | XX | |||
| NG01454-EOI | XX | |||
| NG01455-ICI | XX | |||
| NG01458-EOI | XX | |||
| NG01460-EOI | XX | |||
| NG01461-EOI | XX | |||
| NG01464-ICI | XX | |||
| NG01465-ICI | XX | XX | ||
| NG01466-EOI | XX | |||
| NG01468-EOI | XX | |||
| NG01472-EOI | XX | |||
| NG01474-EOI | XX | |||
| NG01476-EOI | XX | |||
| NG01479-EOI | XX | |||
| NG01480-EOI | XX | |||
| NG01483-ICI | XX | |||
| NG01485-EOI | XX | |||
| NG01487-EOI | XX | |||
| NG01488-EOI | XX | |||
| NG01493-ICI | XX | |||
| NG01495-ICI | XX | XX | ||
| NG01500-EOI | XX | |||
| NG01501-EOI | XX | |||
| NG01502-EOI | XX | |||
| NG01506-ICI | XX | |||
| NG01507-ICI | XX | |||
| NG01513-ICI | XX | |||
| NG01518-ICI | XX | XX | ||
| NG01525-ICI | XX | |||
| NG01527-ICI | XX | |||
| NG01545-ICI | XX | |||
| NG01548-ICI-LATE | XX | |||
| NG01589-ICI | XX | |||
| NG01590-ICI | XX | |||
| NG01627-ICI | XX | |||
| NG01628-ICI | XX |
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_1.0.8 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_1.0.2 rstudioapi_0.13 blob_1.2.1
[17] rmarkdown_2.1 labeling_0.4.2 qtl_1.46-2 webshot_0.5.2
[21] readr_1.3.1 stringr_1.4.0 htmlwidgets_1.5.3 bit_4.0.4
[25] munsell_0.5.0 compiler_3.6.2 httpuv_1.5.2 xfun_0.24
[29] pkgconfig_2.0.3 htmltools_0.5.1.1 tidyselect_1.1.2 tibble_3.1.2
[33] fansi_0.5.0 viridisLite_0.4.0 crayon_1.4.1 withr_2.4.2
[37] later_1.0.0 grid_3.6.2 jsonlite_1.7.2 gtable_0.3.0
[41] lifecycle_1.0.1 DBI_1.1.1 git2r_0.26.1 magrittr_2.0.1
[45] scales_1.1.1 cli_3.0.0 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 generics_0.0.2 vctrs_0.3.8
[57] Rhdf5lib_1.6.3 tools_3.6.2 bit64_4.0.5 glue_1.4.2
[61] purrr_0.3.4 hms_0.5.3 parallel_3.6.2 fastmap_1.1.0
[65] yaml_2.2.1 colorspace_2.0-2 rvest_0.3.5 memoise_2.0.0