/
recla-analysis.R
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/
recla-analysis.R
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## ----setup2-------------------------------------------------------------------
is_inst <- function(pkg) {
nzchar(system.file(package = pkg))
}
qtl2_indic <- is_inst("qtl2")
knitr::opts_chunk$set(eval = qtl2_indic)
## ----pkgs---------------------------------------------------------------------
library(dplyr)
library(ggplot2)
library(qtl2pleio)
library(qtl2)
## ----load-recla---------------------------------------------------------------
file <- paste0("https://raw.githubusercontent.com/rqtl/",
"qtl2data/master/DO_Recla/recla.zip")
recla <- read_cross2(file)
# make sex a covariate for use in qtl2pleio::scan_pvl
recla[[6]][ , 1, drop = FALSE] -> sex
# insert pseudomarkers
insert_pseudomarkers(recla, step = 0.10) -> pseudomap
gm <- pseudomap$`8`
## ----calc-genoprobs-code------------------------------------------------------
probs <- calc_genoprob(recla, map = pseudomap, cores = 1)
## ----calc-aprobs--------------------------------------------------------------
aprobs <- genoprob_to_alleleprob(probs)
## ----calc-kinship-------------------------------------------------------------
kinship <- calc_kinship(aprobs, "loco")
## ----log-phenos---------------------------------------------------------------
recla$pheno -> ph
log(ph) -> lph
apply(FUN = broman::winsorize, X = lph, MARGIN = 2) -> wlph
as_tibble(wlph) -> wlph_tib
## ----scan1--------------------------------------------------------------------
sex2 <- matrix(as.numeric(sex == "female"), ncol = 1)
colnames(sex2) <- "female"
rownames(sex2) <- rownames(aprobs[[1]])
out <- scan1(genoprobs = aprobs,
pheno = wlph,
kinship = kinship,
addcovar = sex2,
reml = TRUE
)
## ----get-peaks----------------------------------------------------------------
(peaks <- find_peaks(out, pseudomap, threshold = 5) %>%
dplyr::arrange(chr, pos) %>%
dplyr::select(- lodindex))
peaks8 <- peaks %>%
dplyr::filter(chr == 8, pos > 50, pos < 60)
pos_LD_light_pct <- peaks8 %>%
dplyr::filter(lodcolumn == "LD_light_pct") %>%
dplyr::select(pos)
pos_HP_latency <- peaks8 %>%
dplyr::filter(lodcolumn == "HP_latency") %>%
dplyr::select(pos)
## ----cors---------------------------------------------------------------------
cor(wlph[ , 7], wlph[ , 10], use = "complete.obs")
cor(wlph[ , 22], wlph[ , 10], use = "complete.obs")
cor(wlph[ , 7], wlph[ , 22], use = "complete.obs")
## ----scatter------------------------------------------------------------------
ggplot() +
geom_point(data = wlph_tib, aes(y = HP_latency, x = LD_light_pct)) +
labs(x = "Percent time in light", y = "Hot plate latency")
## ----lod10-plot---------------------------------------------------------------
plot(out, map = pseudomap,
lodcolumn = 10,
main = "percent time in light"
)
## ----lod22-plot---------------------------------------------------------------
plot(out, map = pseudomap,
lodcolumn = 22,
main = "hot plate latency"
)
## ----coefs-calc---------------------------------------------------------------
scan1coef(aprobs[ , 8], pheno = wlph[ , 10], kinship = kinship$`8`,
reml = TRUE,
addcovar = sex2) -> s1c_10
scan1coef(aprobs[ , 8], pheno = wlph[ , 22], kinship = kinship$`8`,
reml = TRUE,
addcovar = sex2) -> s1c_22
## ----coefs-subset-------------------------------------------------------------
# subset scan1output objects
s1c_10s <- s1c_10[650:999, ]
# 650:999 is the same as the interval for the two-dimensional scan.
s1c_22s <- s1c_22[650:999, ]
## ----plot-coefs---------------------------------------------------------------
plot_coefCC(s1c_10s, scan1_output = out[ , 10, drop = FALSE], map = pseudomap, main = "percent time in light")
plot_coefCC(s1c_22s, scan1_output = out[ , 22, drop = FALSE], map = pseudomap, main = "hot plate latency")
## ----2d-scan, eval = FALSE----------------------------------------------------
# scan_pvl(probs = aprobs$`8`,
# pheno = wlph[, c(10, 22)],
# addcovar = sex2,
# kinship = kinship$`8`,
# start_snp = 650,
# n_snp = 350) -> pvl1022
## ----2d-scan-results, echo = TRUE---------------------------------------------
as_tibble(read.table("https://zenodo.org/record/3210710/files/recla-10-22.txt?download=1", stringsAsFactors = FALSE)) -> pvl1022
## ----lrt-calc-----------------------------------------------------------------
(mylrt <- calc_lrt_tib(pvl1022))
## ----profile-plot-------------------------------------------------------------
colnames(recla$pheno)[c(10, 22)] <- c("Percent time in light", "Hot plate latency")
pvl1022 %>%
mutate(log10lik = loglik / log(10)) %>%
dplyr::select(- loglik) %>%
calc_profile_lods() %>%
add_pmap(pmap = recla$pmap$`8`) %>%
ggplot() + geom_line(aes(x = marker_position, y = profile_lod, colour = trait))
## ----get-pleio-peak-----------------------------------------------------------
find_pleio_peak_tib(pvl1022, start_snp = 650)
## ----read-boot----------------------------------------------------------------
## read boot lrt files
boot_lrt <- list()
for (i in 1:1000){
n <- i - 1
fn <- paste0("https://raw.githubusercontent.com/fboehm/qtl2pleio-manuscript-chtc/master/Recla-bootstrap/results/recla-boot-run561_", n, ".txt")
boot_lrt[i] <- read.table(fn)
}
# convert list to numeric vector
boot_lrt <- unlist(boot_lrt)
## ----pval---------------------------------------------------------------------
sum(boot_lrt >= mylrt) / length(boot_lrt)
## ----session-info, eval = TRUE------------------------------------------------
devtools::session_info()