Last updated: 2022-04-15
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Knit directory: Serreze-T1D_Workflow/
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Unstaged changes:
Modified: analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_5.batches.Rmd
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load("data/gm_allqc_5.batches_mis.RData")
#gm_allqc
gm=gm_allqc
gm
Object 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 131356
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770
table(gm$covar$"diabetic status")
Both Projects Diabetes Proj ICI-No Diabetes Myocarditis Spont. T1D
1 3 164 2 34
T1D <17 T1D<17 T1D>17
36 51 17
table(gm$covar$group)
(A2 Parental) (DQ8 Parental) B6.g7 Parental EOI F1
2 2 1 164 1
ICI PBS
104 34
#ICI-Early vs (PBS-T1D + ICI > 17 weeks of age)
##extracting animals with ici-early and pbs group status
miceinfo <- gm$covar[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
table(miceinfo$group)
ICI PBS
104 34
mice.ids <- rownames(miceinfo)
gm <- gm[mice.ids]
gm
Object of class cross2 (crosstype "bc")
Total individuals 138
No. genotyped individuals 138
No. phenotyped individuals 138
No. with both geno & pheno 138
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131356
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770
table(gm$covar$"diabetic status")
Spont. T1D T1D <17 T1D<17 T1D>17
34 36 51 17
table(gm$covar$group)
ICI PBS
104 34
#Creating a new lable for pbs+ici late and ici-early
table(gm$covar$group)
ICI PBS
104 34
gm$covar$group.new <- ifelse(gm$covar$"diabetic status" == "T1D>17" | gm$covar$group == "PBS", "PBS_ICI.Late", "ICI.Early")
table(gm$covar$group.new)
ICI.Early PBS_ICI.Late
87 51
#binary Yes/No T1D
gm$covar$ICI.Early.vs.PBS_ICI.Late <- ifelse(gm$covar$group.new == "PBS_ICI.Late", 0, 1)
table(gm$covar$"diabetic status")
Spont. T1D T1D <17 T1D<17 T1D>17
34 36 51 17
table(gm$covar$group)
ICI PBS
104 34
table(gm$covar$group.new)
ICI.Early PBS_ICI.Late
87 51
table(gm$covar$ICI.Early.vs.PBS_ICI.Late)
0 1
51 87
gm.full <- gm
covars <- read_csv("data/covar_corrected.cleaned_ici-early.vs.pbs.ici-late_5.batches_mis.csv")
#removing any missing info
missing_ids <- covars[which(covars$"rz.age"==""),]$Mouse.ID
length(missing_ids)
[1] 0
missing_ids
character(0)
if(length(missing_ids) != 0){
gm <- gm[paste0("-",as.character(missing_ids)),]
gm
table(gm$covar$group)
table(gm$covar$group.new)
#keeping only mouseids in covars
covars <- covars[gm$covar$id,]
table(covars$group)
table(covars$group.new)
}
query_variants <- create_variant_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/cc_variants.sqlite")
query_genes <- create_gene_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/mouse_genes_mgi.sqlite")
##removing problmetic marker
gm <- drop_markers(gm, "UNCHS013106")
#pmap_interp = interp_map(map, gm$gmap, gm$pmap)
markers <- marker_names(gm)
gmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/genetic_map.csv")
pmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/physical_map.csv")
#mapdf <- merge(gmapdf,pmapdf, by=c("marker","chr"), all=T)
#rownames(mapdf) <- mapdf$marker
#mapdf <- mapdf[markers,]
#names(mapdf) <- c('marker','chr','gmapdf','pmapdf')
#mapdfnd <- mapdf[!duplicated(mapdf[c(2:3)]),]
pr.qc <- calc_genoprob(gm)
gm
Object of class cross2 (crosstype "bc")
Total individuals 138
No. genotyped individuals 138
No. phenotyped individuals 138
No. with both geno & pheno 138
No. phenotypes 1
No. covariates 8
No. phenotype covariates 0
No. chromosomes 20
Total markers 131355
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7585 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770
covars$rz.age <- as.numeric(covars$rz.age)
Xcovar <- get_x_covar(gm)
addcovar = model.matrix(~ICI.Early.vs.PBS_ICI.Late, data = covars)[,-1]
#addcovar.i = model.matrix(~ICI.Early.vs.PBS_ICI.Late, data = covars)[,-1]
K <- calc_kinship(pr.qc, type = "loco")
#heatmap(K[[1]])
K.overall <- calc_kinship(pr.qc, type = "overall")
#heatmap(K.overall)
kinship <- calc_kinship(pr.qc)
heatmap(kinship)
operm <- scan1perm(pr.qc, covars["rz.age"], model="normal", n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap))
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
names(summary_table) <- c("autosomes","X")
summary_table$significance.level <- rownames(summary_table)
rownames(summary_table) <- NULL
summary_table[c(3,1:2)] %>%
kable(escape = F,align = c("ccc")) %>%
kable_styling("striped", full_width = T) %>%
column_spec(1, bold=TRUE)
significance.level | autosomes | X |
---|---|---|
0.01 | 3.703438 | 3.657877 |
0.05 | 3.578645 | 3.655085 |
0.1 | 3.422268 | 3.651433 |
plot_lod<-function(out,map){
for (i in 1:dim(out)[2]){
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - ICI-Early vs PBS & ICI-Late (no covariates) [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
}
}
print("with no kinship")
[1] “with no kinship”
outf <- scan1(pr.qc, covars["rz.age"], Xcovar=Xcovar, model="normal")
plot_lod(outf,gm$gmap)
print("with normal kinship")
[1] “with normal kinship”
outk <- scan1(pr.qc, covars["rz.age"], Xcovar=Xcovar, model="normal", kinship=kinship)
plot_lod(outk,gm$gmap)
print("with loco kinship")
[1] “with loco kinship”
out <- scan1(pr.qc, covars["rz.age"], Xcovar=Xcovar, model="normal", kinship=K)
plot_lod(out,gm$gmap)
print(paste0("number of samples in analysis = ", as.data.frame(attributes(out)$sample_size)[1,]))
[1] “number of samples in analysis = 138”
peaks<-find_peaks(out, gm$gmap, threshold=summary(operm,alpha=0.1)$A, thresholdX = summary(operm,alpha=0.1)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks$marker <- find_marker(gm$gmap, chr=peaks$chr,pos=peaks$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]
rownames(peaks) <- NULL
print(kable(peaks, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
#plot only peak chromosomes
plot_lod_chr<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " - ICI-Early vs PBS & ICI-Late (no covariates) [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
}
}
for(i in unique(peaks$chr)){
#for (i in 1:nrow(peaks)){
#plot_lod_chr(out,gm$gmap, peaks$chr[i])
plot_lod_chr(out,gm$gmap, i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.1) level of ",summary(operm,alpha=0.1)$A, " [autosomes]/",summary(operm,alpha=0.1)$X, " [x-chromosome]"))
}
[1] “There are no peaks that have a LOD that reaches suggestive (p<0.1) level of 3.42226766678485 [autosomes]/3.65143262837961 [x-chromosome]”
peaks_mba<-find_peaks(out, gm$pmap, threshold=summary(operm,alpha=0.1)$A, thresholdX = summary(operm,alpha=0.1)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks_mba$marker <- find_marker(gm$pmap, chr=peaks_mba$chr,pos=peaks_mba$pos)
names(peaks_mba)[2] <- c("phenotype")
peaks_mba <- peaks_mba[-1]
rownames(peaks_mba) <- NULL
print(kable(peaks_mba, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
plot_lod_chr_mb<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " - ICI-Early vs PBS & ICI-Late (no covariates) [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
}
}
for(i in unique(peaks_mba$chr)){
#for (i in 1:nrow(peaks_mba)){
#plot_lod_chr_mb(out,gm$pmap, peaks_mba$chr[i])
plot_lod_chr_mb(out,gm$pmap,i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.1) level of ",summary(operm,alpha=0.1)$A, " [autosomes]/",summary(operm,alpha=0.1)$X, " [x-chromosome]"))
}
[1] “There are no peaks that have a LOD that reaches suggestive (p<0.1) level of 3.42226766678485 [autosomes]/3.65143262837961 [x-chromosome]”
For each peak LOD location we give a list of gene
if(nrow(peaks) >0){
for (i in 1:nrow(peaks)){
#for (i in 1:1){
#Plot 1
g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
#par(mar=c(4.1, 4.1, 1.5, 0.6))
plot_pxg(g, covars[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
##dev.off()
chr = peaks$chr[i]
# Plot 2
pr_sub <- pull_genoprobint(pr.qc, gm$gmap, chr, c(peaks$ci_lo[i], peaks$ci_hi[i]))
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar)
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], Xcovar=Xcovar)
#coeff <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
#coeff_sub <- scan1coef(pr_sub[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
blup <- scan1blup(pr.qc[,chr], covars[peaks$phenotype[i]])
blup_sub <- scan1blup(pr_sub[,chr], covars[peaks$phenotype[i]])
write.csv(as.data.frame(blup_sub), paste0("data/ici-early.vs.pbs.ici-late_",peaks$phenotype[i],"-no.covariates_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_5.batches_mis.csv"), quote=F)
#plot_coef(coeff,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i]," [scan1coeff; positions in cM] )")
# )
#plot_coef(coeff_sub,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i],"; 1.5 LOD drop interval [scan1coeff; positions in cM] ) ")
# )
plot_coef(blup,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," [scan1blup; positions in cM] )")
)
plot_coef(blup_sub,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i],"; 1.5 LOD drop interval [scan1blup; positions in cM] )")
)
# Plot 3
#c2effB <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary", contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
#c2effBb <- scan1blup(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]],Xcovar=Xcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
#plot(c2effB, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
#plot(c2effBb, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
##last_coef <- unclass(c2effB)[nrow(c2effB),2:3] # last two coefficients
##for(t in seq(along=last_coef))
## axis(side=4, at=last_coef[t], names(last_coef)[t], tick=FALSE)
#Table 1
chr = peaks_mba$chr[i]
start=as.numeric(peaks_mba$ci_lo[i])
end=as.numeric(peaks_mba$ci_hi[i])
genesgss = query_genes(chr, start, end)
write.csv(genesgss, file=paste0("data/ici-early.vs.pbs.ici-late_",peaks$phenotype[i],"-no.covariates_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_5.batches_mis.csv"), quote=F)
rownames(genesgss) <- NULL
genesgss$strand_old = genesgss$strand
genesgss$strand[genesgss$strand=="+"] <- "positive"
genesgss$strand[genesgss$strand=="-"] <- "negative"
#genesgss <-
#table <-
#genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")] %>%
#kable(escape = F,align = c("ccccccccccc")) %>%
#kable_styling("striped", full_width = T) #%>%
#cat #%>%
#column_spec(1, bold=TRUE)
#
#print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], escape = F,align = c("ccccccccccc")))
print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))
#table
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.1) level of ",summary(operm,alpha=0.1)$A, " [autosomes]/",summary(operm,alpha=0.1)$X, " [x-chromosome]"))
}
[1] “There are no peaks that have a LOD that reaches suggestive (p<0.1) level of 3.42226766678485 [autosomes]/3.65143262837961 [x-chromosome]”
operm <- scan1perm(pr.qc, covars["rz.age"], model="normal", addcovar=addcovar, n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap))
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
names(summary_table) <- c("autosomes","X")
summary_table$significance.level <- rownames(summary_table)
rownames(summary_table) <- NULL
summary_table[c(3,1:2)] %>%
kable(escape = F,align = c("ccc")) %>%
kable_styling("striped", full_width = T) %>%
column_spec(1, bold=TRUE)
significance.level | autosomes | X |
---|---|---|
0.01 | 4.081691 | 5.959101 |
0.05 | 3.991521 | 5.500427 |
0.1 | 3.878528 | 4.900624 |
plot_lod<-function(out,map){
for (i in 1:dim(out)[2]){
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - ICI-Early vs PBS & ICI-Late (with additive covariates) [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
}
}
print("with no kinship")
[1] “with no kinship”
outfa <- scan1(pr.qc, covars["rz.age"], Xcovar=Xcovar, model="normal", addcovar=addcovar)
plot_lod(outfa,gm$gmap)
print("with normal kinship")
[1] “with normal kinship”
outka <- scan1(pr.qc, covars["rz.age"], Xcovar=Xcovar, model="normal", addcovar=addcovar, kinship=kinship)
plot_lod(outka,gm$gmap)
print("with loco kinship")
[1] “with loco kinship”
outa <- scan1(pr.qc, covars["rz.age"], Xcovar=Xcovar, model="normal", addcovar=addcovar, kinship=K)
plot_lod(outa,gm$gmap)
out = outa
print(paste0("number of samples in analysis = ", as.data.frame(attributes(out)$sample_size)[1,]))
[1] “number of samples in analysis = 138”
peaks<-find_peaks(out, gm$gmap, threshold=summary(operm,alpha=0.1)$A, thresholdX = summary(operm,alpha=0.1)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks$marker <- find_marker(gm$gmap, chr=peaks$chr,pos=peaks$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]
rownames(peaks) <- NULL
print(kable(peaks, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
#plot only peak chromosomes
plot_lod_chr<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " - ICI-Early vs PBS & ICI-Late (with additive covariates) [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
}
}
for(i in unique(peaks$chr)){
#for (i in 1:nrow(peaks)){
#plot_lod_chr(out,gm$gmap, peaks$chr[i])
plot_lod_chr(out,gm$gmap, i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.1) level of ",summary(operm,alpha=0.1)$A, " [autosomes]/",summary(operm,alpha=0.1)$X, " [x-chromosome]"))
}
[1] “There are no peaks that have a LOD that reaches suggestive (p<0.1) level of 3.87852828222947 [autosomes]/4.9006239459512 [x-chromosome]”
peaks_mba<-find_peaks(out, gm$pmap, threshold=summary(operm,alpha=0.1)$A, thresholdX = summary(operm,alpha=0.1)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks_mba$marker <- find_marker(gm$pmap, chr=peaks_mba$chr,pos=peaks_mba$pos)
names(peaks_mba)[2] <- c("phenotype")
peaks_mba <- peaks_mba[-1]
rownames(peaks_mba) <- NULL
print(kable(peaks_mba, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
plot_lod_chr_mb<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " - ICI-Early vs PBS & ICI-Late (with additive covariates) [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
}
}
for(i in unique(peaks_mba$chr)){
#for (i in 1:nrow(peaks_mba)){
#plot_lod_chr_mb(out,gm$pmap, peaks_mba$chr[i])
plot_lod_chr_mb(out,gm$pmap,i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.1) level of ",summary(operm,alpha=0.1)$A, " [autosomes]/",summary(operm,alpha=0.1)$X, " [x-chromosome]"))
}
[1] “There are no peaks that have a LOD that reaches suggestive (p<0.1) level of 3.87852828222947 [autosomes]/4.9006239459512 [x-chromosome]”
For each peak LOD location we give a list of gene
if(nrow(peaks) >0){
for (i in 1:nrow(peaks)){
#for (i in 1:1){
#Plot 1
g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
#par(mar=c(4.1, 4.1, 1.5, 0.6))
plot_pxg(g, covars[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
##dev.off()
chr = peaks$chr[i]
# Plot 2
pr_sub <- pull_genoprobint(pr.qc, gm$gmap, chr, c(peaks$ci_lo[i], peaks$ci_hi[i]))
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar)
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], Xcovar=Xcovar)
#coeff <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
#coeff_sub <- scan1coef(pr_sub[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
blup <- scan1blup(pr.qc[,chr], covars[peaks$phenotype[i]])
blup_sub <- scan1blup(pr_sub[,chr], covars[peaks$phenotype[i]])
write.csv(as.data.frame(blup_sub), paste0("data/ici-early.vs.pbs.ici-late_",peaks$phenotype[i],"-additive.covariates_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_5.batches_mis.csv"), quote=F)
#plot_coef(coeff,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i]," [scan1coeff; positions in cM] )")
# )
#plot_coef(coeff_sub,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i],"; 1.5 LOD drop interval [scan1coeff; positions in cM] ) ")
# )
plot_coef(blup,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," [scan1blup; positions in cM] )")
)
plot_coef(blup_sub,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i],"; 1.5 LOD drop interval [scan1blup; positions in cM] )")
)
# Plot 3
#c2effB <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary", contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
#c2effBb <- scan1blup(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]],Xcovar=Xcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
#plot(c2effB, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
#plot(c2effBb, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
##last_coef <- unclass(c2effB)[nrow(c2effB),2:3] # last two coefficients
##for(t in seq(along=last_coef))
## axis(side=4, at=last_coef[t], names(last_coef)[t], tick=FALSE)
#Table 1
chr = peaks_mba$chr[i]
start=as.numeric(peaks_mba$ci_lo[i])
end=as.numeric(peaks_mba$ci_hi[i])
genesgss = query_genes(chr, start, end)
write.csv(genesgss, file=paste0("data/ici-early.vs.pbs.ici-late_",peaks$phenotype[i],"-additive.covariates_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_5.batches_mis.csv"), quote=F)
rownames(genesgss) <- NULL
genesgss$strand_old = genesgss$strand
genesgss$strand[genesgss$strand=="+"] <- "positive"
genesgss$strand[genesgss$strand=="-"] <- "negative"
#genesgss <-
#table <-
#genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")] %>%
#kable(escape = F,align = c("ccccccccccc")) %>%
#kable_styling("striped", full_width = T) #%>%
#cat #%>%
#column_spec(1, bold=TRUE)
#
#print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], escape = F,align = c("ccccccccccc")))
print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))
#table
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.1) level of ",summary(operm,alpha=0.1)$A, " [autosomes]/",summary(operm,alpha=0.1)$X, " [x-chromosome]"))
}
[1] “There are no peaks that have a LOD that reaches suggestive (p<0.1) level of 3.87852828222947 [autosomes]/4.9006239459512 [x-chromosome]”
operm <- scan1perm(pr.qc, covars["rz.age"], model="normal", addcovar=addcovar, intcovar=addcovar, n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap))
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
names(summary_table) <- c("autosomes","X")
summary_table$significance.level <- rownames(summary_table)
rownames(summary_table) <- NULL
summary_table[c(3,1:2)] %>%
kable(escape = F,align = c("ccc")) %>%
kable_styling("striped", full_width = T) %>%
column_spec(1, bold=TRUE)
significance.level | autosomes | X |
---|---|---|
0.01 | 4.348406 | 4.290610 |
0.05 | 4.184643 | 4.147244 |
0.1 | 3.979431 | 3.959766 |
plot_lod<-function(out,map){
for (i in 1:dim(out)[2]){
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - ICI-Early vs PBS & ICI-Late (with interactive covariate) [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
}
}
print("with no kinship")
[1] “with no kinship”
outfi <- scan1(pr.qc, covars["rz.age"], Xcovar=Xcovar, model="normal", addcovar=addcovar, intcovar=addcovar)
plot_lod(outfi,gm$gmap)
outf.new <- outfi-outfa
plot_lod(outf.new,gm$gmap)
print("with normal kinship")
[1] “with normal kinship”
outki <- scan1(pr.qc, covars["rz.age"], Xcovar=Xcovar, model="normal", addcovar=addcovar, intcovar=addcovar, kinship=kinship)
plot_lod(outki,gm$gmap)
outk.new <- outki-outka
plot_lod(outk.new,gm$gmap)
print("with loco kinship")
[1] “with loco kinship”
outi <- scan1(pr.qc, covars["rz.age"], Xcovar=Xcovar, model="normal", addcovar=addcovar, intcovar=addcovar, kinship=K)
plot_lod(outi,gm$gmap)
outi.new <- outi-outa
plot_lod(outi.new,gm$gmap)
out = outi.new
print(paste0("number of samples in analysis = ", as.data.frame(attributes(out)$sample_size)[1,]))
[1] “number of samples in analysis = 138”
peaks<-find_peaks(out, gm$gmap, threshold=summary(operm,alpha=0.1)$A, thresholdX = summary(operm,alpha=0.1)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks$marker <- find_marker(gm$gmap, chr=peaks$chr,pos=peaks$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]
rownames(peaks) <- NULL
print(kable(peaks, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
#plot only peak chromosomes
plot_lod_chr<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " - ICI-Early vs PBS & ICI-Late (with interactive covariate) [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
}
}
for(i in unique(peaks$chr)){
#for (i in 1:nrow(peaks)){
#plot_lod_chr(out,gm$gmap, peaks$chr[i])
plot_lod_chr(out,gm$gmap, i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.1) level of ",summary(operm,alpha=0.1)$A, " [autosomes]/",summary(operm,alpha=0.1)$X, " [x-chromosome]"))
}
[1] “There are no peaks that have a LOD that reaches suggestive (p<0.1) level of 3.97943128824132 [autosomes]/3.95976624526595 [x-chromosome]”
peaks_mba<-find_peaks(out, gm$pmap, threshold=summary(operm,alpha=0.1)$A, thresholdX = summary(operm,alpha=0.1)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks_mba$marker <- find_marker(gm$pmap, chr=peaks_mba$chr,pos=peaks_mba$pos)
names(peaks_mba)[2] <- c("phenotype")
peaks_mba <- peaks_mba[-1]
rownames(peaks_mba) <- NULL
print(kable(peaks_mba, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
plot_lod_chr_mb<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " - ICI-Early vs PBS & ICI-Late (with interactive covariate) [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
}
}
for(i in unique(peaks_mba$chr)){
#for (i in 1:nrow(peaks_mba)){
#plot_lod_chr_mb(out,gm$pmap, peaks_mba$chr[i])
plot_lod_chr_mb(out,gm$pmap,i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.1) level of ",summary(operm,alpha=0.1)$A, " [autosomes]/",summary(operm,alpha=0.1)$X, " [x-chromosome]"))
}
[1] “There are no peaks that have a LOD that reaches suggestive (p<0.1) level of 3.97943128824132 [autosomes]/3.95976624526595 [x-chromosome]”
For each peak LOD location we give a list of gene
if(nrow(peaks) >0){
for (i in 1:nrow(peaks)){
#for (i in 1:1){
#Plot 1
g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
#par(mar=c(4.1, 4.1, 1.5, 0.6))
plot_pxg(g, covars[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
##dev.off()
chr = peaks$chr[i]
# Plot 2
pr_sub <- pull_genoprobint(pr.qc, gm$gmap, chr, c(peaks$ci_lo[i], peaks$ci_hi[i]))
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar)
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], Xcovar=Xcovar)
#coeff <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
#coeff_sub <- scan1coef(pr_sub[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
blup <- scan1blup(pr.qc[,chr], covars[peaks$phenotype[i]])
blup_sub <- scan1blup(pr_sub[,chr], covars[peaks$phenotype[i]])
write.csv(as.data.frame(blup_sub), paste0("data/ici-early.vs.pbs.ici-late_",peaks$phenotype[i],"-interactive.covariate_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_5.batches_mis.csv"), quote=F)
#plot_coef(coeff,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i]," [scan1coeff; positions in cM] )")
# )
#plot_coef(coeff_sub,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i],"; 1.5 LOD drop interval [scan1coeff; positions in cM] ) ")
# )
plot_coef(blup,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," [scan1blup; positions in cM] )")
)
plot_coef(blup_sub,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i],"; 1.5 LOD drop interval [scan1blup; positions in cM] )")
)
# Plot 3
#c2effB <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary", contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
#c2effBb <- scan1blup(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]],Xcovar=Xcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
#plot(c2effB, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
#plot(c2effBb, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
##last_coef <- unclass(c2effB)[nrow(c2effB),2:3] # last two coefficients
##for(t in seq(along=last_coef))
## axis(side=4, at=last_coef[t], names(last_coef)[t], tick=FALSE)
#Table 1
chr = peaks_mba$chr[i]
start=as.numeric(peaks_mba$ci_lo[i])
end=as.numeric(peaks_mba$ci_hi[i])
genesgss = query_genes(chr, start, end)
write.csv(genesgss, file=paste0("data/ici-early.vs.pbs.ici-late_",peaks$phenotype[i],"-interactive.covariate_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_5.batches_mis.csv"), quote=F)
rownames(genesgss) <- NULL
genesgss$strand_old = genesgss$strand
genesgss$strand[genesgss$strand=="+"] <- "positive"
genesgss$strand[genesgss$strand=="-"] <- "negative"
#genesgss <-
#table <-
#genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")] %>%
#kable(escape = F,align = c("ccccccccccc")) %>%
#kable_styling("striped", full_width = T) #%>%
#cat #%>%
#column_spec(1, bold=TRUE)
#
#print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], escape = F,align = c("ccccccccccc")))
print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))
#table
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.1) level of ",summary(operm,alpha=0.1)$A, " [autosomes]/",summary(operm,alpha=0.1)$X, " [x-chromosome]"))
}
[1] “There are no peaks that have a LOD that reaches suggestive (p<0.1) level of 3.97943128824132 [autosomes]/3.95976624526595 [x-chromosome]”
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] abind_1.4-5 qtl2_0.22 reshape2_1.4.4 ggplot2_3.3.5
[5] tibble_3.1.2 psych_2.0.7 readxl_1.3.1 cluster_2.1.0
[9] dplyr_1.0.8 optparse_1.6.6 rhdf5_2.28.1 mclust_5.4.6
[13] tidyr_1.0.2 data.table_1.14.0 knitr_1.33 kableExtra_1.1.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.1 bit64_4.0.5 viridisLite_0.4.0 assertthat_0.2.1
[5] highr_0.9 blob_1.2.1 cellranger_1.1.0 yaml_2.2.1
[9] pillar_1.6.1 RSQLite_2.2.7 backports_1.2.1 lattice_0.20-38
[13] glue_1.4.2 digest_0.6.27 promises_1.1.0 rvest_0.3.5
[17] colorspace_2.0-2 htmltools_0.5.1.1 httpuv_1.5.2 plyr_1.8.6
[21] pkgconfig_2.0.3 purrr_0.3.4 scales_1.1.1 webshot_0.5.2
[25] getopt_1.20.3 later_1.0.0 git2r_0.26.1 generics_0.0.2
[29] ellipsis_0.3.2 cachem_1.0.5 withr_2.4.2 cli_3.0.0
[33] mnormt_1.5-7 magrittr_2.0.1 crayon_1.4.1 memoise_2.0.0
[37] evaluate_0.14 fs_1.4.1 fansi_0.5.0 nlme_3.1-142
[41] xml2_1.3.1 tools_3.6.2 hms_0.5.3 lifecycle_1.0.1
[45] stringr_1.4.0 Rhdf5lib_1.6.3 munsell_0.5.0 compiler_3.6.2
[49] rlang_1.0.2 grid_3.6.2 rstudioapi_0.13 rmarkdown_2.1
[53] gtable_0.3.0 DBI_1.1.1 R6_2.5.0 fastmap_1.1.0
[57] bit_4.0.4 utf8_1.2.1 rprojroot_1.3-2 readr_1.3.1
[61] stringi_1.7.2 parallel_3.6.2 Rcpp_1.0.7 vctrs_0.3.8
[65] tidyselect_1.1.2 xfun_0.24