diagnosis_qc_gigamuga_3_batches_Jackson_Lab_Bubier
Hao He
2021-08-24
Last updated: 2021-08-24
Checks: 7 0
Knit directory: DO_Opioid/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 33c0521 | xhyuo | 2021-08-24 | add batch 20210809 |
| html | 3268005 | xhyuo | 2021-07-09 | Build site. |
| Rmd | 5b3aaa6 | xhyuo | 2021-07-09 | update one batch |
| html | f5b6ad7 | xhyuo | 2021-06-01 | Build site. |
| Rmd | b07104a | xhyuo | 2021-06-01 | 3batches screensize |
| html | 11cf315 | xhyuo | 2021-06-01 | Build site. |
| Rmd | 1f46505 | xhyuo | 2021-06-01 | 3batches |
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)Generate json file for 3 batches
#total sample id
#load json file for the 3 batches
gm <- get(load("data/Jackson_Lab_Bubier_MURGIGV01/gm_3batches.RData"))
gmObject of class cross2 (crosstype "do")
Total individuals 412
No. genotyped individuals 412
No. phenotyped individuals 412
No. with both geno & pheno 412
No. phenotypes 1
No. covariates 4
No. phenotype covariates 0
No. chromosomes 20
Total markers 112728
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
8555 8666 6420 6615 6571 6443 6294 5677 5870 5447 6352 5167 5274 5039 4555 4369
17 18 19 X
4330 4002 3108 3974 Missing data per sample
percent_missing <- n_missing(gm, "ind", "prop")*100
miss_dat <- data.frame(Mouse=seq_along(percent_missing),
id = names(percent_missing),
Percent_missing_genotype_data = percent_missing,
batch = as.character(do.call(rbind.data.frame,
strsplit(ind_ids(gm), "_"))[,5]),
labels = as.character(do.call(rbind.data.frame,
strsplit(ind_ids(gm), "V01_"))[,2]))
miss_dat <- miss_dat %>%
mutate(labels2 = case_when(
percent_missing <= 10 ~ "",
TRUE ~ labels
))
#iplot
setScreenSize(height = 300, width = 400)Set screen size to height=300 x width=400iplot(miss_dat$Mouse,
miss_dat$Percent_missing_genotype_data,
indID=paste0(miss_dat$labels, " (", round(miss_dat$Percent_missing_genotype_data,2), "%)"),
chartOpts=list(xlab="Mouse",
ylab="Percent missing genotype data",
ylim=c(0, 100)))#save into pdf
pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/Percent_missing_genotype_data.pdf", width = 10, height = 10)
# Change point shapes and colors
p <- ggplot(data = miss_dat,
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=labels2), vjust = 0, nudge_y = 0.01, show.legend = FALSE, size=3) +
theme(text = element_text(size = 20))
p
dev.off()png
2 p
save(percent_missing,
file = "data/Jackson_Lab_Bubier_MURGIGV01/percent_missing_id.RData")Sexes
xint <- read_csv_numer("data/Jackson_Lab_Bubier_MURGIGV01/Jackson_Lab_Bubier_MURGIGV01_3_batches_qtl2_chrXint.csv", transpose=TRUE)
yint <- read_csv_numer("data/Jackson_Lab_Bubier_MURGIGV01/Jackson_Lab_Bubier_MURGIGV01_3_batches_qtl2_chrYint.csv", transpose=TRUE)
# 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_Bubier_MURGIGV01/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 = "id"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)
#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$ind2)
#filter prop_match>=0.85 or same name for Name.ind1 and Name.ind2
filtered.summary.cg <- summary.cg %>%
mutate(same.sample = case_when(
Name.ind1 == Name.ind2 ~ TRUE,
Name.ind1 != Name.ind2 ~ FALSE
)) %>%
filter(prop_match >= 0.85 | same.sample == TRUE)
save(filtered.summary.cg,
file = "data/Jackson_Lab_Bubier_MURGIGV01/filtered.summary.cg.RData")
#display filtered.summary.cg
DT::datatable(filtered.summary.cg, filter = list(position = 'top', clear = FALSE),
options = list(pageLength = 40, scrollY = "300px", scrollX = "40px"))#plot prop matrix for same.sample = false and prop_match >= 0.85
filter.id <- data.frame(id = unique(c(filtered.summary.cg[filtered.summary.cg$same.sample == F,]$ind1,
filtered.summary.cg[filtered.summary.cg$same.sample == F,]$ind2)))
filter.id$name <- do.call(rbind.data.frame,
strsplit(filter.id$id, "V01_"))[,2]
filter.id <- filter.id[order(filter.id$name),]
gm_filter <- gm[filter.id$id,]
#replace id names
old_ids <- do.call(rbind.data.frame,
strsplit(ind_ids(gm_filter), "V01_"))[,2]
new_ids <- setNames(old_ids,
ind_ids(gm_filter))
gm_filter <- replace_ids(gm_filter, new_ids)
#save gm_filter for same.sample = false and prop_match >= 0.85
save(gm_filter, file = "data/Jackson_Lab_Bubier_MURGIGV01/gm_filterprop_match_0.85.RData")
#compare geno
filter.cg <- compare_geno(gm_filter, cores=10, proportion = TRUE)
filter.cg[lower.tri(filter.cg)] <- NA
filter.cg[filter.cg < 0.5] <- NA # for ggplot lowest value 0.5
diag(filter.cg) <- 0
# Melt the correlation matrix
melted_cormat <- melt(filter.cg, na.rm = TRUE)
# Heatmap
p <- ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "white", high = "red",
limit = c(0.5,1), space = "Lab",
name="Proportions of matching genotypes") +
scale_y_discrete(position = "right") +
xlab("") +
ylab("") +
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5,
size = 10, hjust = 1),
axis.text.y = element_text(size = 10)) +
coord_fixed()
p
| Version | Author | Date |
|---|---|---|
| 11cf315 | xhyuo | 2021-06-01 |
pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/Proportion_matching_genotypes_before_removal_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)])
pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/Proportion_matching_genotypes_after_removal_samples_percent_missing_5.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)])
#show samples with missing genotypes >5
miss_dat_5 <- miss_dat %>%
arrange(desc(Percent_missing_genotype_data)) %>%
filter(labels2 != "")
dim(miss_dat_5)[1] 9 6#display miss_dat
DT::datatable(miss_dat_5,filter = list(position = 'top', clear = FALSE),
options = list(pageLength = 40, scrollY = "300px", scrollX = "40px"))Array intensities and Genotype frequencies
#result object is Array intensities 286518*3284
result <- result[seq(1, nrow(result), by=2),-(1:2)] + result[-seq(1, nrow(result), by=2),-(1:2)]
result <- result[,intersect(ind_ids(gm), colnames(result))]
n <- names(sort(percent_missing[intersect(ind_ids(gm), colnames(result))], decreasing=TRUE))
iboxplot(log10(t(result[,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$founder_geno[1:19])
g <- g[,colSums(fg==0)==0]
fg <- fg[,colSums(fg==0)==0]
fgn <- colSums(fg==3)
gf_ind <- vector("list", 4)
for(i in 1:4) {
gf_ind[[i]] <- t(apply(g[,fgn==i], 1, function(a) table(factor(a, 1:3))/sum(a != 0)))
}
par(mfrow=c(4,1), mar=c(0.6, 0.6, 2.6, 0.6))
for(i in 1:4) {
triplot(c("AA", "AB", "BB"), main=paste0("MAF = ", i, "/8"))
tripoints(gf_ind[[i]], pch=21, bg="lightblue")
tripoints(c((1-i/8)^2, 2*i/8*(1-i/8), (i/8)^2), pch=21, bg="violetred")
if(i>=3) { # label mouse with lowest het
wh <- which(gf_ind[[i]][,2] == min(gf_ind[[i]][,2]))
tritext(gf_ind[[i]][wh,,drop=FALSE] + c(0.02, -0.02, 0),
names(wh), adj=c(0, 1))
}
# label other mice
if(i==1) {
lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.3]
}
else if(i==2) {
lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.48]
}
else if(i==3) {
lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.51]
}
else if(i==4) {
lab <- rownames(gf_ind[[i]])[gf_ind[[i]][,2]>0.6]
}
for(ind in lab) {
if(grepl("^F", ind) && i != 3) {
tritext(gf_ind[[i]][ind,,drop=FALSE] + c(-0.01, 0, +0.01), ind, adj=c(1,0.5))
} else {
tritext(gf_ind[[i]][ind,,drop=FALSE] + c(0.01, 0, -0.01), ind, adj=c(0,0.5))
}
}
}
Crossover counts and Genotyping error LOD scores
#load pre-caluated results
load("data/Jackson_Lab_Bubier_MURGIGV01/pr.RData")
load("data/Jackson_Lab_Bubier_MURGIGV01/m.RData")
load("data/Jackson_Lab_Bubier_MURGIGV01/nxo.RData")
#crossover
setScreenSize(height = 300, width = 400)Set screen size to height=300 x width=400totxo <- rowSums(nxo)[ind_ids(gm)]
all.equal(ind_ids(gm), names(totxo))[1] TRUEiplot(seq_along(totxo),
totxo,
group=gm$covar$ngen,
chartOpts=list(xlab="Mouse", ylab="Number of crossovers",
margin=list(left=80,top=40,right=40,bottom=40,inner=5),
axispos=list(xtitle=25,ytitle=50,xlabel=5,ylabel=5)))#save crossover into pdf
pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/number_crossover.pdf")
cross_over <- data.frame(Mouse = seq_along(totxo), Number_crossovers = totxo, generation = gm$covar$ngen)
names(totxo) <- as.character(do.call(rbind.data.frame, strsplit(names(totxo), "V01_"))[,2])
names(totxo)[totxo <= 800 & totxo >= 400] = ""
# Change point shapes and colors
p <-ggplot(cross_over, aes(x=Mouse, y=Number_crossovers, fill = generation, color=generation)) +
geom_point() +
geom_text_repel(aes(label=names(totxo),hjust=0,vjust=0), show.legend = FALSE, max.overlaps = 15)
p
dev.off()png
2 p
#Here are the crossover counts for those mice with percent_missing >= 5:
tmp <- cbind(percent_missing=round(percent_missing,2), total_xo=totxo)[percent_missing >= 5,]
#display miss_dat
DT::datatable(tmp[order(tmp[,1]),], filter = list(position = 'top', clear = FALSE),
options = list(pageLength = 40, scrollY = "300px", scrollX = "40px"))# Genotyping error LOD scores
load("data/Jackson_Lab_Bubier_MURGIGV01/e.RData")
errors_ind <- rowSums(e>2)/n_typed(gm)*100
lab <- paste0(names(errors_ind), " (", myround(percent_missing,1), "%)")
setScreenSize(height = 300, width = 400)Set screen size to height=300 x width=400iplot(seq_along(errors_ind), errors_ind, indID=lab,
chartOpts=list(xlab="Mouse", ylab="Percent genotyping errors", ylim=c(0, 8),
axispos=list(xtitle=25, ytitle=50, xlabel=5, ylabel=5)))save(errors_ind, file = "data/Jackson_Lab_Bubier_MURGIGV01/errors_ind.RData")
# Apparent genotyping errors
load("data/Jackson_Lab_Bubier_MURGIGV01/snpg.RData")
gobs <- do.call("cbind", gm$geno)
gobs[gobs==0] <- NA
par(pty="s")
err_direct <- rowMeans(snpg != gobs, na.rm=TRUE)*100
errors_ind_0 <- rowSums(e > 0)/n_typed(gm)*100
par(mar=c(4.1,4.1,0.6, 0.6))
grayplot(errors_ind_0, err_direct,
xlab="Percent errors (error LOD > 0)",
ylab="Percent errors (obs vs predicted)",
xlim=c(0, 2), ylim=c(0, 2))
abline(0,1,lty=2, col="gray60")
pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/Percent_genotype_errors_obs_vs_predicted.pdf",width = 20, height = 20)
par(pty="s")
err_direct <- rowMeans(snpg != gobs, na.rm=TRUE)*100
errors_ind_0 <- rowSums(e > 0)/n_typed(gm)*100
par(mar=c(4.1,4.1,0.6, 0.6))
grayplot(errors_ind_0, err_direct,
xlab="Percent errors (error LOD > 0)",
ylab="Percent errors (obs vs predicted)",
xlim=c(0, 2), ylim=c(0, 2))
abline(0,1,lty=2, col="gray60")
dev.off()png
2 Missing data in Markers and Genotype frequencies Markers
#It can also be useful to look at the proportion of missing genotypes by marker.
#Markers with a lot of missing data were likely difficult to call, and so the genotypes that were called may contain a lot of errors.
pmis_mar <- n_missing(gm, "marker", "proportion")*100
par(mar=c(5.1,0.6,0.6, 0.6))
hist(pmis_mar, breaks=seq(0, 100, length=201),
main="", yaxt="n", ylab="", xlab="Percent missing genotypes")
rug(pmis_mar)
pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/Percent_missing_genotype_data_per_marker.pdf")
par(mar=c(5.1,0.6,0.6, 0.6))
hist(pmis_mar, breaks=seq(0, 100, length=201),
main="", yaxt="n", ylab="", xlab="Percent missing genotypes")
rug(pmis_mar)
dev.off()png
2 # Genotype frequencies Markers
gf_mar <- t(apply(g, 2, function(a) table(factor(a, 1:3))/sum(a != 0)))
gn_mar <- t(apply(g, 2, function(a) table(factor(a, 1:3))))
pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/genotype_frequency_marker.pdf")
par(mfrow=c(2,2), 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"))
z <- gf_mar[fgn==i,]
z <- z[rowSums(is.na(z)) < 3,]
tripoints(z, pch=21, bg="gray80", cex=0.6)
tripoints(c((1-i/8)^2, 2*i/8*(1-i/8), (i/8)^2), pch=21, bg="violetred")
}
dev.off()png
2 par(mfrow=c(2,2), 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"))
z <- gf_mar[fgn==i,]
z <- z[rowSums(is.na(z)) < 3,]
tripoints(z, pch=21, bg="gray80", cex=0.6)
tripoints(c((1-i/8)^2, 2*i/8*(1-i/8), (i/8)^2), pch=21, bg="violetred")
}
dev.off()null device
1 # Genotype errors Markers
errors_mar <- colSums(e>2)/n_typed(gm, "marker")*100
grayplot(pmis_mar, errors_mar,
xlab="Proportion missing", ylab="Proportion genotyping errors")
pdf(file = "data/Jackson_Lab_Bubier_MURGIGV01/genotype_error_marker.pdf")
grayplot(pmis_mar, errors_mar,
xlab="Proportion missing", ylab="Proportion genotyping errors")
dev.off()pdf
2 Remove bad samples
#qc_infor
#percent missing
qc_info <- left_join(gm$covar, miss_dat)Joining, by = "id"#add cross_over
cross_over$id <- rownames(cross_over)
qc_info <- qc_info %>% left_join(cross_over[,-1])Joining, by = "id"#mismatch sex
qc_info <- qc_info %>%
mutate(sex.match = case_when(
predict.sex == sex ~ TRUE,
predict.sex != sex ~ FALSE
))
#genotype errors
qc_info <- qc_info %>%
left_join(
data.frame(id = names(errors_ind),
genotype_erros = errors_ind,stringsAsFactors = F)
)Joining, by = "id"#add duplicated id to be remove
qc_info <- qc_info %>%
mutate(remove.id.duplicated = case_when(
id %in% unique(c(filtered.summary.cg$remove.id)) ~ TRUE,
!(id %in% unique(c(filtered.summary.cg$remove.id))) ~ FALSE
))
#bad sample label
qc_info <- qc_info %>%
mutate(bad.sample = case_when(
(ngen ==1 | Number_crossovers <= 200 | Number_crossovers >=1000 | percent_missing >= 10 | genotype_erros >= 1 | remove.id.duplicated == TRUE) ~ TRUE,
TRUE ~ FALSE
))
#Note for two sample
qc_info[qc_info$id == "Jackson_Lab_Bubier_MURGIGV01_20210701_30895_G1", "bad.sample"] <- FALSE
qc_info[qc_info$id == "Jackson_Lab_Bubier_MURGIGV01_20210809_31047_H10", "bad.sample"] <- FALSE
save(qc_info, file = "data/Jackson_Lab_Bubier_MURGIGV01/qc_info.RData")
write.csv(qc_info, file = "data/Jackson_Lab_Bubier_MURGIGV01/qc_info.csv", quote = FALSE)
#display qc_info
DT::datatable(qc_info, filter = list(position = 'top', clear = FALSE),
options = list(pageLength = 40, scrollY = "300px", scrollX = "40px"))#remove bad samples
gm.no.bad <- gm[paste0("-",as.character(qc_info[qc_info$bad.sample == TRUE, "id"])),]
gm.no.badObject of class cross2 (crosstype "do")
Total individuals 395
No. genotyped individuals 395
No. phenotyped individuals 395
No. with both geno & pheno 395
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 112728
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
8555 8666 6420 6615 6571 6443 6294 5677 5870 5447 6352 5167 5274 5039 4555 4369
17 18 19 X
4330 4002 3108 3974 # subjects
# update other stuff
e <- e[ind_ids(gm.no.bad),]
g <- g[ind_ids(gm.no.bad),]
snpg <- snpg[ind_ids(gm.no.bad),]
length(errors_mar[errors_mar > 5])[1] 262# omit the markers with error rates >5%.
bad_markers <- find_markerpos(gm.no.bad, names(errors_mar[errors_mar > 5]))
save(bad_markers, file = "data/Jackson_Lab_Bubier_MURGIGV01/bad_markers.RData")
#drop bad markers
gm_after_qc <- drop_markers(gm.no.bad, names(errors_mar)[errors_mar > 5])
gm_after_qcObject of class cross2 (crosstype "do")
Total individuals 395
No. genotyped individuals 395
No. phenotyped individuals 395
No. with both geno & pheno 395
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 112466
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
8528 8648 6402 6605 6556 6423 6281 5664 5857 5436 6341 5153 5263 5025 4543 4359
17 18 19 X
4320 3985 3104 3973 save(gm_after_qc, file = paste0("data/Jackson_Lab_Bubier_MURGIGV01/gm_DO", length(ind_ids(gm_after_qc)) ,"_qc.RData"))
save(e,g,snpg, file = "data/Jackson_Lab_Bubier_MURGIGV01/e_g_snpg_qc.RData")
#replace id
new.id <- str_split_fixed(ind_ids(gm_after_qc), "_",7)[,6]
names(new.id) <- ind_ids(gm_after_qc)
gm_after_qc <- replace_ids(gm_after_qc, new.id)
gm_after_qcObject of class cross2 (crosstype "do")
Total individuals 395
No. genotyped individuals 395
No. phenotyped individuals 395
No. with both geno & pheno 395
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 112466
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
8528 8648 6402 6605 6556 6423 6281 5664 5857 5436 6341 5153 5263 5025 4543 4359
17 18 19 X
4320 3985 3104 3973 save(gm_after_qc, file = paste0("data/Jackson_Lab_Bubier_MURGIGV01/gm_DO", length(ind_ids(gm_after_qc)) ,"_qc_newid.RData"))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.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] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] DT_0.17 reshape2_1.4.4
[3] forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.4 purrr_0.3.4
[7] readr_1.4.0 tidyr_1.1.2
[9] tibble_3.0.6 tidyverse_1.3.0
[11] mclust_5.4.7 DOQTL_1.19.0
[13] VariantAnnotation_1.36.0 Rsamtools_2.6.0
[15] SummarizedExperiment_1.20.0 Biobase_2.50.0
[17] MatrixGenerics_1.2.1 matrixStats_0.58.0
[19] BSgenome.Mmusculus.UCSC.mm10_1.4.0 BSgenome_1.58.0
[21] rtracklayer_1.50.0 Biostrings_2.58.0
[23] XVector_0.30.0 GenomicRanges_1.42.0
[25] GenomeInfoDb_1.26.7 IRanges_2.24.1
[27] S4Vectors_0.28.1 BiocGenerics_0.36.1
[29] ggrepel_0.9.1 ggplot2_3.3.3
[31] qtlcharts_0.12-10 qtl2_0.24
[33] broman_0.72-4 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 BiocFileCache_1.14.0
[4] plyr_1.8.6 crosstalk_1.1.1 BiocParallel_1.24.1
[7] digest_0.6.27 foreach_1.5.1 htmltools_0.5.1.1
[10] regress_1.3-21 gdata_2.18.0 magrittr_2.0.1
[13] memoise_2.0.0 cluster_2.1.1 doParallel_1.0.16
[16] QTLRel_1.6 annotate_1.68.0 modelr_0.1.8
[19] askpass_1.1 prettyunits_1.1.1 colorspace_2.0-0
[22] rvest_0.3.6 blob_1.2.1 rappdirs_0.3.3
[25] haven_2.3.1 xfun_0.21 crayon_1.4.1
[28] RCurl_1.98-1.2 jsonlite_1.7.2 qtl_1.47-9
[31] iterators_1.0.13 glue_1.4.2 gtable_0.3.0
[34] zlibbioc_1.36.0 DelayedArray_0.16.3 kernlab_0.9-29
[37] Rhdf5lib_1.12.1 prabclus_2.3-2 DEoptimR_1.0-8
[40] scales_1.1.1 DBI_1.1.1 Rcpp_1.0.6
[43] xtable_1.8-4 progress_1.2.2 bit_4.0.4
[46] htmlwidgets_1.5.3 httr_1.4.2 fpc_2.2-9
[49] modeltools_0.2-23 ellipsis_0.3.1 farver_2.0.3
[52] pkgconfig_2.0.3 XML_3.99-0.5 flexmix_2.3-17
[55] nnet_7.3-15 dbplyr_2.1.0 labeling_0.4.2
[58] tidyselect_1.1.0 rlang_0.4.10 later_1.1.0.1
[61] AnnotationDbi_1.52.0 munsell_0.5.0 cellranger_1.1.0
[64] tools_4.0.3 cachem_1.0.4 cli_2.3.0
[67] generics_0.1.0 RSQLite_2.2.3 broom_0.7.4
[70] evaluate_0.14 fastmap_1.1.0 yaml_2.2.1
[73] knitr_1.31 bit64_4.0.5 fs_1.5.0
[76] robustbase_0.93-7 whisker_0.4 xml2_1.3.2
[79] biomaRt_2.46.3 rstudioapi_0.13 compiler_4.0.3
[82] curl_4.3 reprex_1.0.0 stringi_1.5.3
[85] highr_0.8 annotationTools_1.64.0 GenomicFeatures_1.42.3
[88] lattice_0.20-41 Matrix_1.2-18 vctrs_0.3.6
[91] pillar_1.4.7 lifecycle_1.0.0 rhdf5filters_1.2.1
[94] RUnit_0.4.32 data.table_1.13.6 bitops_1.0-6
[97] corpcor_1.6.9 httpuv_1.5.5 R6_2.5.0
[100] hwriter_1.3.2 promises_1.2.0.1 codetools_0.2-18
[103] MASS_7.3-53 gtools_3.8.2 assertthat_0.2.1
[106] rhdf5_2.34.0 openssl_1.4.3 rprojroot_2.0.2
[109] withr_2.4.1 GenomicAlignments_1.26.0 GenomeInfoDbData_1.2.4
[112] diptest_0.75-7 hms_1.0.0 grid_4.0.3
[115] class_7.3-18 rmarkdown_2.6 git2r_0.28.0
[118] lubridate_1.7.9.2 This R Markdown site was created with workflowr