Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE, fig.width = 4, fig.height = 4,
comment = "#>"
)
## ----input--------------------------------------------------------------------
colkas <- qtl::read.cross(format="csvs",dir="../inst",
genfile="ColKas_geno.csv",
phefile = "ColKas_pheno.csv",
na.strings = c("_"), estimate.map=TRUE, crosstype = "riself"
)
colkas_genoprob <- qtl::calc.genoprob(colkas, step=2)
## ----pve----------------------------------------------------------------------
library(rNeighborQTL)
x <- colkas$pheno[,2]
y <- colkas$pheno[,3]
smap_colkas <- data.frame(x,y)
s_seq <- quantile(dist(smap_colkas),c(0.1*(1:10)))
colkas_pve <- calc_pve(genoprobs=colkas_genoprob,
pheno=log(colkas$pheno[,5]+1),
smap=smap_colkas, s_seq=s_seq,
addcovar=as.matrix(colkas$pheno[,7:9])
)
## ----eff, fig.width=4, fig.height=8-------------------------------------------
colkas_eff <- eff_neighbor(genoprobs=colkas_genoprob,
pheno=log(colkas$pheno[,5]+1),
smap=smap_colkas, scale=7,
addcovar=as.matrix(colkas$pheno[,7:9])
)
## ----LOD----------------------------------------------------------------------
colkas_scan <- scan_neighbor(genoprobs=colkas_genoprob,
pheno=log(colkas$pheno[,5]+1),
smap=smap_colkas, scale=7,
addcovar=as.matrix(colkas$pheno[,7:9])
)
plot_nei(colkas_scan)
## ----perm, eval=FALSE---------------------------------------------------------
# colkas_perm <- perm_neighbor(genoprobs=colkas_genoprob,
# pheno=log(colkas$pheno[,5]+1),
# smap=smap_colkas, scale=7,
# addcovar=as.matrix(colkas$pheno[,6:8]),
# times=3, p_val=c(0.5,0.1)
# )
## ----self---------------------------------------------------------------------
plot_nei(colkas_scan, type="self")
colkas_scanone <- qtl::scanone(colkas_genoprob,
pheno.col=log(colkas$pheno$holes+1),
addcovar=as.matrix(colkas$pheno[,7:9]),
method="hk")
plot(colkas_scanone)
## ----CIM, eval=FALSE----------------------------------------------------------
# colkas_cim <- scan_neighbor(genoprobs=colkas_genoprob,
# pheno=log(colkas$pheno[,5]+1),
# smap=smap_colkas, scale=7,
# addcovar=as.matrix(colkas$pheno[,7:9]),
# addQTL="c4_nga8"
# )
# plot_nei(colkas_cim)
## ----int, eval=FALSE----------------------------------------------------------
# colkas_int <- int_neighbor(genoprobs=colkas_genoprob,
# pheno=log(colkas$pheno[,5]+1),
# smap=smap_colkas, scale=7,
# addcovar=as.matrix(colkas$pheno[,7:9]),
# addQTL="c4_nga8", intQTL="c4_nga8"
# )
#
# plot_nei(colkas_int, type="int")
## ----bin----------------------------------------------------------------------
s_seq <- quantile(dist(smap_colkas),c(0.1*(1:10)))
colkas_pveBin <- calc_pve(genoprobs=colkas_genoprob,
pheno=colkas$pheno[,7],
smap=smap_colkas, s_seq=s_seq,
response="binary", addcovar=as.matrix(colkas$pheno[,8:9]),
fig=TRUE)
colkas_scanBin <- scan_neighbor(genoprobs=colkas_genoprob,
pheno=colkas$pheno[,7],
smap=smap_colkas, scale=2.24,
addcovar=as.matrix(colkas$pheno[,8:9]),
response="binary")
plot_nei(colkas_scanBin)
## ----fake---------------------------------------------------------------------
#F2 lines
set.seed(1234)
data("fake.f2",package="qtl")
fake_f2 <- subset(fake.f2, chr=1:19)
smap_f2 <- cbind(runif(qtl::nind(fake_f2),1,100),runif(qtl::nind(fake_f2),1,100))
genoprobs_f2 <- qtl::calc.genoprob(fake_f2,step=2)
s_seq <- quantile(dist(smap_f2),c(0.1*(1:10)))
nei_eff <- sim_nei_qtl(genoprobs_f2, a2=0.5, d2=0.5,
smap=smap_f2,
scale=s_seq[1], n_QTL=1
)
pve_f2 <- calc_pve(genoprobs=genoprobs_f2,
pheno=nei_eff$nei_y,
smap=smap_f2, s_seq=s_seq[1:5],
addcovar=as.matrix(cbind(fake_f2$pheno$sex,fake_f2$pheno$pgm)),
fig=FALSE)
deltaPVE <- pve_f2[-1,3] - c(0,pve_f2[1:4,3])
argmax_s <- s_seq[1:5][deltaPVE==max(deltaPVE)]
scan_f2 <- scan_neighbor(genoprobs=genoprobs_f2,
pheno=nei_eff$nei_y,
smap=smap_f2, scale=argmax_s,
addcovar=as.matrix(cbind(fake_f2$pheno$sex,fake_f2$pheno$pgm))
)
plot_nei(scan_f2)
## ----bc-----------------------------------------------------------------------
#backcross lines
set.seed(1234)
data("fake.bc",package="qtl")
fake_bc <- subset(fake.bc, chr=1:19)
smap_bc <- cbind(runif(qtl::nind(fake_bc),1,100),runif(qtl::nind(fake_bc),1,100))
genoprobs_bc <- qtl::calc.genoprob(fake_bc,step=2)
s_seq <- quantile(dist(smap_bc),c(0.1*(1:10)))
nei_eff <- sim_nei_qtl(genoprobs_bc, a2=0.3, d2=-0.3,
smap=smap_bc,
scale=s_seq[1], n_QTL=1)
pve_bc <- calc_pve(genoprobs=genoprobs_bc,
pheno=nei_eff$nei_y,
smap=smap_bc, s_seq=s_seq[1:5],
addcovar=as.matrix(cbind(fake_bc$pheno$sex,fake_bc$pheno$age)),
fig=FALSE)
deltaPVE <- pve_bc[-1,3] - c(0,pve_bc[1:4,3])
argmax_s <- s_seq[1:5][deltaPVE==max(deltaPVE)]
scan_bc <- scan_neighbor(genoprobs=genoprobs_bc,
pheno=nei_eff$nei_y,
smap=smap_bc, scale=argmax_s,
addcovar=as.matrix(cbind(fake_bc$pheno$sex,fake_bc$pheno$age))
)
plot_nei(scan_bc)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.