metabolomics_mouse_fecal
Hao He
2023-07-18
Last updated: 2023-07-18
Checks: 7 0
Knit directory: DO_Opioid/
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Unstaged changes:
Modified: .gitignore
Modified: _workflowr.yml
Modified: analysis/marker_violin.Rmd
Modified: output/CC_SARS_Chr16_QTL_interval.pdf
Modified: output/CC_SARS_Chr16_plotGeno.pdf
Modified: output/CC_SARS_Chr16_plotGeno.png
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/metabolomics_mouse_fecal.Rmd) and HTML (docs/metabolomics_mouse_fecal.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8566571 | xhyuo | 2023-07-18 | qtl snp/gene plot |
| html | ff42a20 | xhyuo | 2023-07-17 | Build site. |
| Rmd | 5ab28b1 | xhyuo | 2023-07-17 | qtl plot |
| html | 44a7ef8 | xhyuo | 2023-07-12 | Build site. |
| Rmd | 0fe14ed | xhyuo | 2023-07-12 | qtl |
| html | 7473070 | xhyuo | 2023-07-03 | Build site. |
| Rmd | e84fb30 | xhyuo | 2023-07-03 | h2_se |
| html | 75ea822 | xhyuo | 2023-06-25 | Build site. |
| Rmd | f2c07ec | xhyuo | 2023-06-25 | h2 |
Last update: 2023-07-18
Loading libraries
library(ggplot2)
library(gridExtra)
library(GGally)
library(parallel)
library(qtl)
library(parallel)
library(survival)
library(regress)
library(abind)
library(tidyverse)
library(broman)
library(qtl2)
library(qtlcharts)
library(DT)
library(biomaRt)
rz.transform <- function(y) {
rankY=rank(y, ties.method="average", na.last="keep")
rzT=qnorm(rankY/(length(na.exclude(rankY))+1))
return(rzT)
}
# estimated SE of estimated heritability
# Visscher & Goddard (2015), https://doi.org/10.1534/genetics.114.171017
#
# lambda_i = eigenvalues of kinship matrix
# a = sum[ (lam_i-1)^2 / (1 + h^2(lam_i - 1))^2 ]
# b = sum[ (lam_i - 1) / (1 + h^2(lam_i - 1)) ]
# N = sample size
#
# var(h_hat^2) = 2 / (a - b^2/N)
se_herit <-
function(pheno, kinship)
{
h2 <- as.numeric(est_herit(pheno, kinship))
d <- decomp_kinship(kinship)
lam <- d$values
a <- sapply(h2, function(hsq) sum( (lam-1)^2 / (1 + hsq*(lam-1))^2 ) )
b <- sapply(h2, function(hsq) sum( (lam-1) / (1 + hsq*(lam-1)) ) )
n <- length(lam)
setNames( sqrt(2 / (a - b^2/n)), colnames(pheno) )
}Heritability and QTL
# Read phenotype data -----------------------------------------------------
sampleinfo <- readxl::read_excel(path = "data/metabolomics_mouse_fecal/Untargeted metabolomics_74 Mouse Fecal_YanjiaoZhou_06122023.xlsx", sheet = 1,
range = "A5:C79") %>%
dplyr::rename(., lab_ID = 1) %>%
dplyr::filter(!(lab_ID %in% c(11, 67))) %>%
dplyr::mutate(lab_ID = as.character(lab_ID))
#first dataset
rawdata1 = readxl::read_excel(path = "data/metabolomics_mouse_fecal/Untargeted metabolomics_74 Mouse Fecal_YanjiaoZhou_06122023.xlsx", sheet = 2) %>%
dplyr::mutate(Index = paste0("Dat1_", as.character(1:nrow(.))), .before = 1) %>%
dplyr::select(1:3, Fecal_1:Fecal_74)
#second dataset
rawdata2 = readxl::read_excel(path = "data/metabolomics_mouse_fecal/Bile acids_74 Fecal_YanjiaoZhou_07072023.xlsx", sheet = 2) %>%
dplyr::rename(CV = 2, Name = Sample) %>%
dplyr::filter(!is.na(CV)) %>%
dplyr::mutate(Index = paste0("Dat2_", as.character(1:nrow(.))), .before = 1) %>%
dplyr::select(1:2, starts_with("Fecal_")) %>%
dplyr::mutate(Formula = "", .after = Name)
#rbind two dataset
all.equal(colnames(rawdata1), colnames(rawdata2))
# [1] TRUE
rawdata <- rbind(rawdata1, rawdata2)
#make it longer format by t()
rawdata_long <- rawdata %>%
dplyr::select(-1:-3) %>%
t() %>%
as.data.frame() %>%
rownames_to_column(var = "lab_ID") %>%
dplyr::mutate(lab_ID = str_remove_all(lab_ID, "Fecal_")) %>%
dplyr::mutate(across(V1:last_col(), rz.transform))
colnames(rawdata_long)[-1] = rawdata$Index
#left_join
do.pheno <- left_join(sampleinfo[, 1:2], rawdata_long) %>%
as.data.frame()
# Read genotype data -----------------------------------------------------------
load("/projects/csna/csna_workflow/data/Jackson_Lab_12_batches/gm_DO3173_qc_newid.RData")#gm_after_qc
overlap.id = intersect(ind_ids(gm_after_qc), as.character(do.pheno$ID))
#subset
gm = gm_after_qc[overlap.id, ]
do.pheno = do.pheno[do.pheno$ID %in% overlap.id, ]
do.pheno = do.pheno[match(ind_ids(gm), do.pheno$ID) , ]
#add sex into do.pheno
load("/projects/csna/csna_workflow/data/Jackson_Lab_12_batches/qc_info.RData")
do.pheno <- left_join(do.pheno, qc_info[, c(1,3)], by = c("ID" = "name")) %>%
dplyr::relocate(sex, .after = "ID")
rownames(do.pheno) = do.pheno$ID
all.equal(ind_ids(gm), do.pheno$ID)
# [1] TRUE
if(FALSE){
#genotype probability for 68 subjects
pr = calc_genoprob(gm, cores = 20, quiet = FALSE)
saveRDS(pr, file = "data/metabolomics_mouse_fecal/pr.rds")
apr <- genoprob_to_alleleprob(pr, cores = 20)
saveRDS(apr, file = "data/metabolomics_mouse_fecal/apr.rds")
#Calculating a kinship matrix
k <- calc_kinship(pr, "overall", use_allele_probs = FALSE, quiet = TRUE, cores = 20)
saveRDS(k, file = "data/metabolomics_mouse_fecal/k.rds")
k_loco <- calc_kinship(apr, "loco", cores = 20)
saveRDS(k_loco, file = "data/metabolomics_mouse_fecal/k_loco.rds")
#addcovar
options(na.action='na.pass')
addcovar = model.matrix(~sex, data = do.pheno[, c("sex"), drop=F])[,-1, drop=F]
colnames(addcovar) <- c("sex")
rownames(addcovar) <- rownames(do.pheno)
#herit
hsq_se <- map_dfr(colnames(do.pheno)[-1:-3], function(x){
phe = do.pheno[,x]
names(phe) <- do.pheno$ID
h2 = est_herit(pheno = phe,
kinship = k,
addcovar = addcovar,
cores = 20)
d <- decomp_kinship(k)
lam <- d$values
a <- sapply(h2, function(hsq) sum( (lam-1)^2 / (1 + hsq*(lam-1))^2 ) )
b <- sapply(h2, function(hsq) sum( (lam-1) / (1 + hsq*(lam-1)) ) )
n <- length(lam)
se <- sqrt(2 / (a - b^2/n))
data.frame(h2 = h2, se = se)
})
hsq.df <- data.frame(Index = colnames(do.pheno)[-1:-3],
hsq_se) %>%
dplyr::left_join(rawdata[, 1:3])
save(hsq.df, file = "data/metabolomics_mouse_fecal/hsq.out")
#qtl
out <- scan1(genoprobs = apr,
pheno = do.pheno[, -1:-3],
addcovar = addcovar,
kinship = k_loco,
cores = 20)
save(out, file = "data/metabolomics_mouse_fecal/qtl.out")
#permutation
operm <- scan1perm(genoprobs = apr,
pheno = do.pheno[, 4, drop=F],
addcovar = addcovar,
kinship = k_loco,
cores = 20,
n_perm = 1000)
save(operm, file = "data/metabolomics_mouse_fecal/operm.out")
#5% significance thresholds
cutoff = summary(operm, alpha=c(0.05))
cutoff
# LOD thresholds (1000 permutations)
# Dat1_1
# 0.05 8.25
#peaks
peaks <- find_peaks(out, gm$gmap, threshold = cutoff, drop = 1.5)
save(peaks, file = "data/metabolomics_mouse_fecal/peaks.out")
}
load("data/metabolomics_mouse_fecal/hsq.out")
load("data/metabolomics_mouse_fecal/peaks.out")
#histogram raw data
for(i in c(233, 460, 3408, 710, 3806, 3805)){
#histogram
dat = data.frame(pheno = t(rawdata[i, 4:77]))
p <- ggplot(data=dat, aes(dat$pheno)) +
geom_histogram() +
ylab("Number of DO mice") + xlab(paste0("raw ", rawdata[rawdata$Index ==paste0("Dat1_", i), "Name"]," ",
rawdata[rawdata$Index ==paste0("Dat1_", i), "Formula"]))
print(p)
}
# Warning: Use of `dat$pheno` is discouraged. Use `pheno` instead.
| Version | Author | Date |
|---|---|---|
| 44a7ef8 | xhyuo | 2023-07-12 |
# Warning: Use of `dat$pheno` is discouraged. Use `pheno` instead.
| Version | Author | Date |
|---|---|---|
| 44a7ef8 | xhyuo | 2023-07-12 |
# Warning: Use of `dat$pheno` is discouraged. Use `pheno` instead.
| Version | Author | Date |
|---|---|---|
| 44a7ef8 | xhyuo | 2023-07-12 |
# Warning: Use of `dat$pheno` is discouraged. Use `pheno` instead.
| Version | Author | Date |
|---|---|---|
| 44a7ef8 | xhyuo | 2023-07-12 |
# Warning: Use of `dat$pheno` is discouraged. Use `pheno` instead.
| Version | Author | Date |
|---|---|---|
| 44a7ef8 | xhyuo | 2023-07-12 |
# Warning: Use of `dat$pheno` is discouraged. Use `pheno` instead.
| Version | Author | Date |
|---|---|---|
| 44a7ef8 | xhyuo | 2023-07-12 |
#histogram rankZ data
for(i in c(233, 460, 3408, 710, 3806, 3805)){
p <- ggplot(data=do.pheno, aes(do.pheno[, paste0("Dat1_", i)])) +
geom_histogram() +
ylab("Number of DO mice") + xlab(paste0("rankZ ", hsq.df[hsq.df$Index ==paste0("Dat1_", i), "Name"]," ",
hsq.df[hsq.df$Index ==paste0("Dat1_", i), "Formula"]))
print(p)
}
| Version | Author | Date |
|---|---|---|
| 44a7ef8 | xhyuo | 2023-07-12 |
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| 44a7ef8 | xhyuo | 2023-07-12 |
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| 44a7ef8 | xhyuo | 2023-07-12 |
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| 44a7ef8 | xhyuo | 2023-07-12 |
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| 44a7ef8 | xhyuo | 2023-07-12 |
| Version | Author | Date |
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| 44a7ef8 | xhyuo | 2023-07-12 |
Heritability Results
#display hsq.df
DT::datatable(hsq.df[, c(1,4,5,2,3)],
filter = list(position = 'top', clear = FALSE),
extensions = 'Buttons',
options = list(dom = 'Blfrtip',
buttons = c('csv', 'excel'),
lengthMenu = list(c(10,25,50,-1),
c(10,25,50,"All")),
pageLength = 40,
scrollY = "300px",
scrollX = "40px"),
caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left; color:black; font-size:200% ;','Heritability'))QTL Results
load("data/metabolomics_mouse_fecal/qtl.out")
load("data/metabolomics_mouse_fecal/operm.out")
# overall maximum
max.dt <- data.frame(lodcolumn = colnames(out)) %>%
dplyr::mutate(max.lod = map_dbl(lodcolumn, function(x){
maxlod(out[, x])
})) %>%
dplyr::mutate(pvalue = map_dbl(max.lod, function(x){
sum(operm[,1] > x)/1000
})) %>%
dplyr::mutate(fdr = p.adjust(pvalue, method = "fdr"))
#display QTL peaks
peaks_dat <- left_join(peaks, hsq.df[, c(1,4,5,2,3)], by = c("lodcolumn" = "Index")) %>%
inner_join(max.dt, c("lodcolumn" = "lodcolumn", "lod" = "max.lod")) %>%
dplyr::relocate(pvalue, .after = lod) %>%
dplyr::relocate(fdr, .after = pvalue)
index = peaks_dat[peaks_dat$fdr <= 0.05, "lodcolumn"]
query_variants <- create_variant_query_func("data/cc_variants.sqlite")
query_genes <- create_gene_query_func("data/mouse_genes_mgi.sqlite")
apr <- readRDS("data/metabolomics_mouse_fecal/apr.rds")
k_loco <- readRDS("data/metabolomics_mouse_fecal/k_loco.rds")
#addcovar
options(na.action='na.pass')
addcovar = model.matrix(~sex, data = do.pheno[, c("sex"), drop=F])[,-1, drop=F]
colnames(addcovar) <- c("sex")
rownames(addcovar) <- rownames(do.pheno)
cutoff = 8.25
if(FALSE){
#peaks
#peak coeff and SNP
coef_c1 <- list()
coef_c2 <- list()
out_snps <- list()
out_genes <- list()
for(j in index) {
chr <- find_markerpos(gm, names(which.max(out[,j])))$chr
phe <- do.pheno[, j]
names(phe) <- rownames(do.pheno)
#peak coeff
coef_c1[[j]] <- scan1coef(genoprobs=apr[,chr],
pheno=phe,
kinship=k_loco[[chr]],
addcovar=addcovar)
#peak blup
coef_c2[[j]] <- scan1blup(genoprobs=apr[,chr],
pheno=phe,
kinship=k_loco[[chr]],
addcovar=addcovar,
cores = 20)
#peak SNPs
peak_Mbp <- find_markerpos(gm, names(which.max(out[,j])))$pmap
variants <- query_variants(chr, peak_Mbp - 1, peak_Mbp + 1)
out_snps[[j]] <- scan1snps(genoprobs = apr[,chr],
map = gm$pmap,
pheno = phe,
kinship = k_loco[[chr]],
addcovar = addcovar,
query_func=query_variants,
chr=chr,
start=peak_Mbp - 1,
end=peak_Mbp + 1,
keep_all_snps=TRUE)
out_genes[[j]] <- query_genes(chr, peak_Mbp - 1, peak_Mbp + 1)
}
save(coef_c1, coef_c2,
out_snps, out_genes, file = "data/metabolomics_mouse_fecal/index_out_coef_snp_gene.RData")
}
load("data/metabolomics_mouse_fecal/index_out_coef_snp_gene.RData")
#genome-wide plot
for(j in index){
par(mar=c(5.1, 4.1, 1.1, 1.1))
ymx <- maxlod(out[, j]) # overall maximum LOD score
plot(out, map=gm$pmap, lodcolumn=j, col="slateblue", ylim=c(0, ymx+5))
abline(h=8.25, col="red")
title(main = paste0(peaks_dat[peaks_dat$lodcolumn == j, "Name"],
peaks_dat[peaks_dat$lodcolumn == j, "Formula"]))
print(j)
chr <- find_markerpos(gm, names(which.max(out[,j])))$chr
print(peaks_dat[peaks_dat$lodcolumn == j, ])
#coeff plot
par(mar=c(4.1, 4.1, 0.6, 0.6))
#plot_coefCC(coef_c1[[j]], gm$pmap[chr], scan1_output=subset(out, gm$pmap, lodcolumn=j), bgcolor="gray95", legend=NULL)
plot_coefCC(coef_c2[[j]], gm$pmap[chr], scan1_output=subset(out, gm$pmap, lodcolumn=j), bgcolor="gray95", legend=NULL)
plot(out_snps[[j]]$lod, out_snps[[j]]$snpinfo, drop_hilit=1.5, genes=out_genes[[j]])
}
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
# [1] "Dat1_159"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 13 159 Dat1_159 7 41.146 10.56996 0 0 40.713 41.214
# Name Formula h2 se
# 13 Methylmalonic acid C4 H6 O4 0.07132245 0.478351
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
# [1] "Dat1_358"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi Name
# 29 358 Dat1_358 3 11.064 10.7755 0 0 11.059 11.173 EPK
# Formula h2 se
# 29 C16 H28 N4 O6 0.8829885 0.6020464
| Version | Author | Date |
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| ff42a20 | xhyuo | 2023-07-17 |
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| ff42a20 | xhyuo | 2023-07-17 |
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
# [1] "Dat1_377"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi Name
# 33 377 Dat1_377 9 71.322 18.09949 0 0 71.32 71.403 MFCD00153866
# Formula h2 se
# 33 C6 H9 N O4 1 0.5731433
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
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| ff42a20 | xhyuo | 2023-07-17 |
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
# [1] "Dat1_378"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 34 378 Dat1_378 9 71.322 17.18862 0 0 71.321 71.402
# Name Formula h2 se
# 34 (2S,4S)-4-Amino-2-hydroxy-2-methylpentanedioic acid C6 H11 N O5 1 0.5731433
| Version | Author | Date |
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| ff42a20 | xhyuo | 2023-07-17 |
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| ff42a20 | xhyuo | 2023-07-17 |
| Version | Author | Date |
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| ff42a20 | xhyuo | 2023-07-17 |
# [1] "Dat1_536"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 47 536 Dat1_536 5 17.535 10.53676 0 0 17.497 17.652
# Name Formula h2 se
# 47 Stearoylethanolamide C20 H41 N O2 0 0.4230553
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| ff42a20 | xhyuo | 2023-07-17 |
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| ff42a20 | xhyuo | 2023-07-17 |
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
# [1] "Dat1_673"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 59 673 Dat1_673 7 41.153 10.89921 0 0 40.713 41.214
# Name Formula h2 se
# 59 Propionic acid C3 H6 O2 0 0.4230553
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
# [1] "Dat1_769"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi Name
# 62 769 Dat1_769 9 71.322 19.76486 0 0 71.32 71.403 MFCD00153866
# Formula h2 se
# 62 C6 H9 N O4 1 0.5731433
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
| Version | Author | Date |
|---|---|---|
| ff42a20 | xhyuo | 2023-07-17 |
# [1] "Dat1_770"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 63 770 Dat1_770 9 71.322 19.27253 0 0 71.32 71.329
# Name Formula h2 se
# 63 (2S,4S)-4-Amino-2-hydroxy-2-methylpentanedioic acid C6 H11 N O5 1 0.5731433
# [1] "Dat1_776"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi Name
# 65 776 Dat1_776 9 71.322 19.4123 0 0 71.32 71.403 6-APA
# Formula h2 se
# 65 C8 H12 N2 O3 S 1 0.5731433
# [1] "Dat1_1099"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi Name
# 94 1099 Dat1_1099 9 71.324 13.0579 0 0 71.32 71.403 ml354
# Formula h2 se
# 94 C16 H14 N2 O3 1 0.5731433
# [1] "Dat1_1993"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 154 1993 Dat1_1993 9 71.322 12.91449 0 0 71.32 71.327
# Name Formula h2 se
# 154 Cystathionine ketimine C7 H9 N O4 S 1 0.5731433
# [1] "Dat1_2005"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 156 2005 Dat1_2005 7 41.113 11.23992 0 0 40.7 41.214
# Name Formula h2 se
# 156 Succinic anhydride C4 H4 O3 0.129272 0.5160077
# [1] "Dat1_2487"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi Name
# 196 2487 Dat1_2487 1 41.479 11.74653 0 0 41.451 41.494 omaciclovir
# Formula h2 se
# 196 C10 H15 N5 O3 1 0.5731433
# [1] "Dat1_3046"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi Name
# 227 3046 Dat1_3046 9 71.322 12.77517 0 0 71.32 71.403 Alanylclavam
# Formula h2 se
# 227 C8 H12 N2 O4 1 0.5731433
# [1] "Dat1_3099"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 229 3099 Dat1_3099 1 81.375 10.43377 0 0 80.938 81.407
# Name
# 229 2-[(2Z,4E,6E,8E,10E,12E)-14-Hydroxy-6,11-dimethyl-2,4,6,8,10,12-tetradecahexaen-2-yl]-4,4,7a-trimethyl-2,4,5,6,7,7a-hexahydro-1-benzofuran-6-ol
# Formula h2 se
# 229 C27 H38 O3 1 0.5731433
# [1] "Dat1_3271"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi Name
# 238 3271 Dat1_3271 9 71.342 11.14863 0 0 71.32 71.403 Bisnorbiotin
# Formula h2 se
# 238 C8 H12 N2 O3 S 1 0.5731433
# [1] "Dat1_3325"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 240 3325 Dat1_3325 1 41.479 11.03427 0 0 41.451 41.494
# Name Formula h2 se
# 240 Fructose 1-phosphate C6 H13 O9 P 1 0.5731433
# [1] "Dat1_3439"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 248 3439 Dat1_3439 9 71.322 10.73324 0 0 71.32 71.327
# Name Formula h2 se
# 248 tyramine sulfate C8 H11 N O4 S 1 0.5731433
# [1] "Dat1_4031"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 290 4031 Dat1_4031 17 51.763 10.50568 0 0 51.458 52.617
# Name Formula h2 se
# 290 Threonylglutamine C9 H17 N3 O5 1 0.5731433
# [1] "Dat1_4112"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi Name
# 295 4112 Dat1_4112 9 71.322 10.61034 0 0 71.32 71.403 CPCCOEt
# Formula h2 se
# 295 C13 H13 N O4 0.2104277 0.558635
# [1] "Dat2_30"
# lodindex lodcolumn chr pos lod pvalue fdr ci_lo ci_hi
# 341 4744 Dat2_30 1 1.893 10.37835 0 0 1.879 4.439
# Name Formula h2 se
# 341 Hyocholic Acid (HCA) 0.1034566 0.5000092
#QTL peaks
DT::datatable(peaks_dat,
filter = list(position = 'top', clear = FALSE),
extensions = 'Buttons',
options = list(dom = 'Blfrtip',
buttons = c('csv', 'excel'),
lengthMenu = list(c(10,25,50,-1),
c(10,25,50,"All")),
pageLength = 40,
scrollY = "300px",
scrollX = "40px"),
caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left; color:black; font-size:200% ;','QTL Peaks'))
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] parallel stats graphics grDevices utils datasets methods
# [8] base
#
# other attached packages:
# [1] biomaRt_2.46.3 DT_0.17 qtlcharts_0.12-10 qtl2_0.24
# [5] broman_0.72-4 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.4
# [9] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.6
# [13] tidyverse_1.3.0 abind_1.4-5 regress_1.3-21 survival_3.2-7
# [17] qtl_1.47-9 GGally_2.1.0 gridExtra_2.3 ggplot2_3.3.3
# [21] workflowr_1.6.2
#
# loaded via a namespace (and not attached):
# [1] fs_1.5.0 lubridate_1.7.9.2 bit64_4.0.5
# [4] progress_1.2.2 RColorBrewer_1.1-2 httr_1.4.2
# [7] rprojroot_2.0.2 tools_4.0.3 backports_1.2.1
# [10] R6_2.5.0 BiocGenerics_0.36.1 DBI_1.1.1
# [13] colorspace_2.0-0 withr_2.4.1 prettyunits_1.1.1
# [16] tidyselect_1.1.0 rematch_1.0.1 curl_4.3
# [19] bit_4.0.4 compiler_4.0.3 git2r_0.28.0
# [22] Biobase_2.50.0 cli_2.3.0 rvest_0.3.6
# [25] xml2_1.3.2 labeling_0.4.2 scales_1.1.1
# [28] askpass_1.1 rappdirs_0.3.3 digest_0.6.27
# [31] rmarkdown_2.6 pkgconfig_2.0.3 htmltools_0.5.1.1
# [34] highr_0.8 dbplyr_2.1.0 fastmap_1.1.0
# [37] htmlwidgets_1.5.3 rlang_1.0.2 readxl_1.3.1
# [40] rstudioapi_0.13 RSQLite_2.2.3 farver_2.0.3
# [43] generics_0.1.0 jsonlite_1.7.2 crosstalk_1.1.1
# [46] magrittr_2.0.1 Matrix_1.3-2 S4Vectors_0.28.1
# [49] Rcpp_1.0.6 munsell_0.5.0 lifecycle_1.0.0
# [52] stringi_1.5.3 whisker_0.4 yaml_2.2.1
# [55] BiocFileCache_1.14.0 plyr_1.8.6 grid_4.0.3
# [58] blob_1.2.1 promises_1.2.0.1 crayon_1.4.1
# [61] lattice_0.20-41 haven_2.3.1 splines_4.0.3
# [64] hms_1.0.0 knitr_1.31 pillar_1.4.7
# [67] stats4_4.0.3 reprex_1.0.0 XML_3.99-0.5
# [70] glue_1.4.2 evaluate_0.14 data.table_1.13.6
# [73] modelr_0.1.8 vctrs_0.3.6 httpuv_1.5.5
# [76] cellranger_1.1.0 openssl_1.4.3 gtable_0.3.0
# [79] reshape_0.8.8 assertthat_0.2.1 cachem_1.0.4
# [82] xfun_0.21 broom_0.7.4 later_1.1.0.1
# [85] IRanges_2.24.1 AnnotationDbi_1.52.0 memoise_2.0.0
# [88] ellipsis_0.3.1This R Markdown site was created with workflowr