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Copy path10. QTL Clustering.R
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10. QTL Clustering.R
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library(GenomicRanges)
library(ggplot2)
library(gplots)
library(RColorBrewer)
library(made4)
library(BSgenome.Mmusculus.UCSC.mm10)
library(DOQTL)
library(HZE)
library(amap)
library(sigclust)
setwd("~/Desktop/")
# Plot function (w/ binning to average markers and max LOD)
plot.hs.qtl = function(qtl, bin.width = 1000, ...) {
new.qtl = NULL
for(chr in 1:length(qtl)) {
print(chr)
# Create 100 SNP bins.
brks = cut(x = 1:length(qtl[[chr]]), breaks = length(qtl[[chr]]) / bin.width)
# Split up the SNP positions and get the mean.
pos = split(start(qtl[[chr]]), brks)
pos = sapply(pos, mean)
# Split up the p-values and get the max.
pv = split(mcols(qtl[[chr]])$p.value, brks)
pv = sapply(pv, min)
# Make a single new GRanges object to return.
gr = GRanges(seqnames = seqnames(qtl[[chr]])[1],
ranges = IRanges(start = pos, width = 1), p.value = pv)
if(chr == 1) {
new.qtl = gr
} else {
new.qtl = c(new.qtl, gr)
} # else
} # for(chr)
# Get the chromosome lengths.
chrlen = seqlengths(BSgenome.Mmusculus.UCSC.mm10)
names(chrlen) = sub("^chr", "", names(chrlen))
chrlen = chrlen[seqlevels(new.qtl)] * 1e-6
# Add the chr lengths to the chromosomes for plotting.
# Switch positions to genome Mb.
gmb = start(new.qtl) * 1e-6
for(chr in 2:length(chrlen)) {
wh = which(seqnames(new.qtl) == names(chrlen)[chr])
gmb[wh] = gmb[wh] + sum(chrlen[1:(chr - 1)])
} # for(chr)
# Get chromosome mid-points for plotting the Chr name.
chrmid = (chrlen / 2) + cumsum(c(1, chrlen[-length(chrlen)]))
# Make the plot.
col = rep(rgb(0,0,0), length(new.qtl))
even.chr = which(seqnames(new.qtl) %in% (1:10 * 2))
col[even.chr] = rgb(0.7,0.7,0.7)
plot(gmb, -log10(new.qtl$p.value), pch = 20, xaxt = "n",
col = col, las = 1, xlab = "", ylab = "-log10(p-value)", ...)
mtext(side = 1, line = 0.5, at = chrmid, text = names(chrlen), cex = 1.2)
return(new.qtl)
} # plot.hs.qtl
##NEURO#############################################
##NEURO#############################################
##NEURO#############################################
HZE.Neuro.Frz.Total <- plot.hs.qtl(QTL.HZE.frz.total)
rm(QTL.HZE.frz.total)
HZE.Neuro.AVGMO <- plot.hs.qtl(QTL.HZE.avgmo.total)
rm(QTL.HZE.avgmo.total)
HZE.Neuro.tone.frz <- plot.hs.qtl(QTL.HZE.tone.frz)
rm(QTL.HZE.tone.frz)
HZE.Neuro.train.frz <- plot.hs.qtl(QTL.HZE.train.frz)
rm(QTL.HZE.train.frz)
HZE.Neuro.train.shock <- plot.hs.qtl(QTL.HZE.train.shock)
rm(QTL.HZE.train.shock)
Gamma.Neuro.AVGMO.total <- plot.hs.qtl(QTL.gamma.avgmo.total)
rm(QTL.gamma.avgmo.total)
Gamma.Neuro.Frz.Total <- plot.hs.qtl(QTL.gamma.frz.total)
rm(QTL.gamma.frz.total)
Gamma.Neuro.tone.frz <- plot.hs.qtl(QTL.gamma.tone.frz)
rm(QTL.gamma.tone.frz)
Gamma.Neuro.train.frz <- plot.hs.qtl(QTL.gamma.train.frz)
rm(QTL.gamma.train.frz)
Gamma.Neuro.train.shock <- plot.hs.qtl(QTL.gamma.train.shock)
rm(QTL.gamma.train.shock)
UN.Neuro.AVGMO.total <- plot.hs.qtl(QTL.UN.avgmo.total)
rm(QTL.UN.avgmo.total)
UN.Neuro.Frz.Total <- plot.hs.qtl(QTL.UN.frz.total)
rm(QTL.UN.frz.total)
UN.Neuro.tone.frz <- plot.hs.qtl(QTL.UN.tone.frz)
rm(QTL.UN.tone.frz)
UN.Neuro.train.frz <- plot.hs.qtl(QTL.UN.train.frz)
rm(QTL.UN.train.frz)
UN.Neuro.train.shock <- plot.hs.qtl(QTL.UN.train.shock)
rm(QTL.UN.train.shock)
combined <- data.frame(#HZE.Neuro.Frz.Total = -log10(HZE.Neuro.Frz.Total$p.value),
HZE.Neuro.AVGMO = -log10(HZE.Neuro.AVGMO$p.value),
HZE.Neuro.tone.frz = -log10(HZE.Neuro.tone.frz$p.value),
HZE.Neuro.train.frz = -log10(HZE.Neuro.train.frz$p.value),
HZE.Neuro.train.shock = -log10(HZE.Neuro.train.shock$p.value),
Gamma.Neuro.AVGMO.total = -log10(Gamma.Neuro.AVGMO.total$p.value),
#Gamma.Neuro.Frz.Total = -log10(Gamma.Neuro.Frz.Total$p.value),
Gamma.Neuro.tone.frz = -log10(Gamma.Neuro.tone.frz$p.value),
Gamma.Neuro.train.frz = -log10(Gamma.Neuro.train.frz$p.value),
Gamma.Neuro.train.shock = -log10(Gamma.Neuro.train.shock$p.value),
UN.Neuro.AVGMO.total = -log10(UN.Neuro.AVGMO.total$p.value),
#UN.Neuro.Frz.Total = -log10(UN.Neuro.Frz.Total$p.value),
UN.Neuro.tone.frz = -log10(UN.Neuro.tone.frz$p.value),
UN.Neuro.train.frz = -log10(UN.Neuro.train.frz$p.value),
UN.Neuro.train.shock = -log10(UN.Neuro.train.shock$p.value))
LOD.fnx <- function(x){
if(x >= "5.5")
return(x)
if(x < "5.5")
return(0)
}
combined$HZE.Neuro.Frz.Total <- sapply(combined$HZE.Neuro.Frz.Total, LOD.fnx)
combined$HZE.Neuro.AVGMO <- sapply(combined$HZE.Neuro.AVGMO, LOD.fnx)
combined$HZE.Neuro.tone.frz <- sapply(combined$HZE.Neuro.tone.frz, LOD.fnx)
combined$HZE.Neuro.train.frz <- sapply(combined$HZE.Neuro.train.frz, LOD.fnx)
combined$HZE.Neuro.train.shock <- sapply(combined$HZE.Neuro.train.shock, LOD.fnx)
combined$Gamma.Neuro.AVGMO.total <- sapply(combined$Gamma.Neuro.AVGMO.total, LOD.fnx)
combined$Gamma.Neuro.Frz.Total <- sapply(combined$Gamma.Neuro.Frz.Total, LOD.fnx)
combined$Gamma.Neuro.tone.frz <- sapply(combined$Gamma.Neuro.tone.frz, LOD.fnx)
combined$Gamma.Neuro.train.frz <- sapply(combined$Gamma.Neuro.train.frz, LOD.fnx)
combined$Gamma.Neuro.train.shock <- sapply(combined$Gamma.Neuro.train.shock, LOD.fnx)
combined$UN.Neuro.AVGMO.total <- sapply(combined$UN.Neuro.AVGMO.total, LOD.fnx)
combined$UN.Neuro.Frz.Total <- sapply(combined$UN.Neuro.Frz.Total, LOD.fnx)
combined$UN.Neuro.tone.frz <- sapply(combined$UN.Neuro.tone.frz, LOD.fnx)
combined$UN.Neuro.train.frz <- sapply(combined$UN.Neuro.train.frz, LOD.fnx)
combined$UN.Neuro.train.shock <- sapply(combined$UN.Neuro.train.shock, LOD.fnx)
combined.max.divide <- data.frame(HZE.AML = (combined$HZE.AML/max(combined$HZE.AML)),
HZE.cataract = (combined$HZE.cataract/max(combined$HZE.cataract)),
HZE.LSA.DLBCL = (combined$HZE.LSA.DLBCL)/max(combined$HZE.LSA.DLBCL),
HZE.LSA.FBL = (combined$HZE.LSA.FBL/max(combined$HZE.LSA.FBL)),
HZE.HardACA = (combined$HZE.HardACA/max(combined$HZE.HardACA)),
HZE.HardAD = (combined$HZE.HardAD/max(combined$HZE.HardAD)),
HZE.HCC = (combined$HZE.HCC/max(combined$HZE.HCC)))
require(made4)
combined = combined[, colSums(combined) > 0.5]
heatplot(t(combined), margins = c(5, 13), dend="row", method = "ave", main = "", key = F, labCol=NA)
##COAT#############################################
##COAT#############################################
##COAT#############################################
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Gamma_Albino_GR_QTL.Rdata")
Gamma.Albino = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Gamma_Black_GR_QTL.Rdata")
Gamma.Black = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Gamma_Brown_GR_QTL.Rdata")
Gamma.Brown = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Gamma_Grey_GR_QTL.Rdata")
Gamma.Grey = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Gamma_LB_GR_QTL.Rdata")
Gamma.LB = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Gamma_LG_GR_QTL.Rdata")
Gamma.LG = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/HZE_Albino_GR_QTL.Rdata")
HZE.Albino = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/HZE_Black_GR_QTL.Rdata")
HZE.Black = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/HZE_Brown_GR_QTL.Rdata")
HZE.Brown = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/HZE_Grey_GR_QTL.Rdata")
HZE.Grey = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/HZE_LB_GR_QTL.Rdata")
HZE.LB = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/HZE_LG_GR_QTL.Rdata")
HZE.LG = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Unirradiated_Albino_GR_QTL.Rdata")
Unirradiated.Albino = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Unirradiated_Black_GR_QTL.Rdata")
Unirradiated.Black = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Unirradiated_Brown_GR_QTL.Rdata")
Unirradiated.Brown = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Unirradiated_Grey_GR_QTL.Rdata")
Unirradiated.Grey = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Unirradiated_LB_GR_QTL.Rdata")
Unirradiated.LB = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "/Users/elijah/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Coat/Unirradiated_LG_QTL.Rdata")
Unirradiated.LG = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
combined <- data.frame(HZE.Albino = -log10(HZE.Albino$p.value),
HZE.Black = -log10(HZE.Black$p.value),
HZE.Brown = -log10(HZE.Brown$p.value),
HZE.Grey = -log10(HZE.Grey$p.value),
#HZE.LB = -log10(HZE.LB$p.value),
HZE.LG = -log10(HZE.LG$p.value),
Gamma.Albino = -log10(Gamma.Albino$p.value),
Gamma.Black = -log10(Gamma.Black$p.value),
Gamma.Brown = -log10(Gamma.Brown$p.value),
Gamma.Grey = -log10(Gamma.Grey$p.value),
#Gamma.LB = -log10(Gamma.LB$p.value),
Gamma.LG = -log10(Gamma.LG$p.value),
Unirradiated.Albino = -log10(Unirradiated.Albino$p.value),
Unirradiated.Black = -log10(Unirradiated.Black$p.value),
Unirradiated.Brown = -log10(Unirradiated.Brown$p.value),
Unirradiated.Grey = -log10(Unirradiated.Grey$p.value),
#Unirradiated.LB = -log10(Unirradiated.LB$p.value),
Unirradiated.LG = -log10(Unirradiated.LG$p.value))
combined = t(combined)
heatplot(combined, margins = c(5, 10), dend="row", method = "ave", main = "", key = F, labCol=NA)
hc <- hcluster(combined, method = "pearson", diag = FALSE, upper = FALSE,
link = "ave", members = NULL, nbproc = 2, doubleprecision = TRUE)
plot(hc)
pvalue = sigclust(combined, nsim = 1000, icovest = 3)
plot(pvalue)
perms = 10000
cluster.height = matrix(1, nrow = perms, ncol = 2, dimnames = list(1:perms, c("min", "average")))
for(p in 1:perms) {
time = Sys.time()
print(p)
combined.perm <- data.frame(HZE.Albino = (sample(combined$HZE.Albino)),
HZE.Black = (sample(combined$HZE.Black)),
HZE.Brown = (sample(combined$HZE.Brown)),
HZE.Grey = (sample(combined$HZE.Grey)),
HZE.LB = (sample(combined$HZE.LB)),
#HZE.LG = (sample(combined$HZE.LG)),
Gamma.Albino = (sample(combined$Gamma.Albino)),
Gamma.Black = (sample(combined$Gamma.Black)),
Gamma.Brown = (sample(combined$Gamma.Brown)),
Gamma.Grey = (sample(combined$Gamma.Grey)),
Gamma.LB = (sample(combined$Gamma.LB)),
#Gamma.LG = (sample(combined$Gamma.LG)),
Unirradiated.Albino = (sample(combined$Unirradiated.Albino)),
Unirradiated.Black = (sample(combined$Unirradiated.Black)),
Unirradiated.Brown = (sample(combined$Unirradiated.Brown)),
Unirradiated.Grey = (sample(combined$Unirradiated.Grey)),
Unirradiated.LB = (sample(combined$Unirradiated.LB)))
hc <- hcluster(t(combined.perm), method = "pearson", diag = F, upper = FALSE,
link = "complete", members = NULL, nbproc = 2, doubleprecision = T)
cluster.height[p,] = c(min(hc$height), ((min(hc$height) + max(hc$height))/2))
print(paste(round(difftime(Sys.time(), time, units = 'mins'), digits = 2),
"minutes..."))
}
hist(cluster.height[,1], breaks = 100, main = "Distribution of Minimum Heights for Null Distribution")
quantile(cluster.height[,1],c(0.025,0.975))
quantile(cluster.height[,1],c(0.01,0.99))
##HZE#############################################
##HZE#############################################
##HZE#############################################
load(file = "~/Desktop/R/QTL/WD/3.\ CoxPH\ Mapping/Rdata/HZE_cat2_QTL.Rdata")
HZE.cataract = plot.hs.qtl(qtl)
rm(qtl)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_AML_GR_QTL.Rdata")
HZE.AML = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_HardACA_GR_QTL.Rdata")
HZE.HardACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_HardAD_GR_QTL.Rdata")
HZE.HardAD = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_HCC_GR_QTL.Rdata")
HZE.HCC = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_HS_GR_QTL.Rdata")
HZE.HS = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_HSA_GR_QTL.Rdata")
HZE.HSA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_LSA.Bmerge_GR_QTL.Rdata")
HZE.LSA.B = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_LSA.BLL_GR_QTL.Rdata")
HZE.LSA.BLL = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_LSA.DLBCL_GR_QTL.Rdata")
HZE.DLBCL = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_LSA.FBL_GR_QTL.Rdata")
HZE.FBL = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_LSA.PreT_GR_QTL.Rdata")
HZE.LSA.PreT = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_MammACA_GR_QTL.Rdata")
HZE.MammACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_OSA_GR_QTL.Rdata")
HZE.OSA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_Pit_GR_QTL.Rdata")
HZE.pit = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_PulACA_GR_QTL.Rdata")
HZE.PulACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_STS_GR_QTL.Rdata")
HZE.STS = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_Thyroid_GR_QTL.Rdata")
HZE.Thyroid = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/HZE_NN_QTL.Rdata")
HZE.NonNeoplastic = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
##Gamma#############################################
##Gamma#############################################
##Gamma#############################################
load(file = "~/Desktop/R/QTL/WD/3.\ CoxPH\ Mapping/Rdata/Gamma_cat2_QTL.Rdata")
Gamma.cataract = plot.hs.qtl(qtl)
rm(qtl)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_AML_GR_QTL.Rdata")
Gamma.AML = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_GCT_GR_QTL.Rdata")
Gamma.GCT = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_HardACA_GR_QTL.Rdata")
Gamma.HardACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_HardAD_GR_QTL.Rdata")
Gamma.HardAD = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_HCC_GR_QTL.Rdata")
Gamma.HCC = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_HS_GR_QTL.Rdata")
Gamma.HS = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_HSA_GR_QTL.Rdata")
Gamma.HSA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_LSA.Bmerge_GR_QTL.Rdata")
Gamma.LSA.B = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_LSA.BLL_GR_QTL.Rdata")
Gamma.LSA.BLL = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_LSA.DLBCL_GR_QTL.Rdata")
Gamma.LSA.DLBCL = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_LSA.FBL_GR_QTL.Rdata")
Gamma.LSA.FBL = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_LSA.PreT_GR_QTL.Rdata")
Gamma.LSA.PreT = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_MammACA_GR_QTL.Rdata")
Gamma.MammACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_OSA_GR_QTL.Rdata")
Gamma.OSA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_Pit_GR_QTL.Rdata")
Gamma.Pit = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_PulACA_GR_QTL.Rdata")
Gamma.PulACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_STS_GR_QTL.Rdata")
Gamma.STS = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_Thyroid_GR_QTL.Rdata")
Gamma.Thyroid = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Gamma_NN_QTL.Rdata")
Gamma.NonNeoplastic = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
##Unirradiated#############################################
##Unirradiated#############################################
##Unirradiated#############################################
load(file = "~/Desktop/R/QTL/WD/3.\ CoxPH\ Mapping/Rdata/Unirradiated_cat2_QTL.Rdata")
Unirradiated.cataract = plot.hs.qtl(qtl)
rm(qtl)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_AML_GR_QTL.Rdata")
Unirradiated.AML = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_HardACA_GR_QTL.Rdata")
Unirradiated.HardACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_HardAD_GR_QTL.Rdata")
Unirradiated.HardAD = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_HCC_GR_QTL.Rdata")
Unirradiated.HCC = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_HS_GR_QTL.Rdata")
Unirradiated.HS = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_HSA_GR_QTL.Rdata")
Unirradiated.HSA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_LSA.Bmerge_GR_QTL.Rdata")
Unirradiated.LSA.B = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_LSA.BLL_GR_QTL.Rdata")
Unirradiated.LSA.BLL = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_LSA.DLBCL_GR_QTL.Rdata")
Unirradiated.LSA.DLBCL = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_LSA.FBL_GR_QTL.Rdata")
Unirradiated.LSA.FBL = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_LSA.PreT_GR_QTL.Rdata")
Unirradiated.LSA.PreT = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_MammACA_GR_QTL.Rdata")
Unirradiated.MammACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_OSA_GR_QTL.Rdata")
Unirradiated.OSA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_PulACA_GR_QTL.Rdata")
Unirradiated.PulACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_STS_GR_QTL.Rdata")
Unirradiated.STS = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_Thyroid_GR_QTL.Rdata")
Unirradiated.Thyroid = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file ="~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Unirradiated_NN_QTL.Rdata")
Unirradiated.NonNeoplastic = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
##Total#############################################
##Total#############################################
##Total#############################################
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_AML_QTL.Rdata")
Total.AML = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_HardACA_QTL.Rdata")
Total.HardACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_HCC_QTL.Rdata")
Total.HCC = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_HSA_QTL.Rdata")
Total.HSA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_LSA_QTL.Rdata")
Total.LSA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_MammACA_QTL.Rdata")
Total.MammACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_OSA_QTL.Rdata")
Total.OSA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_Pit_QTL.Rdata")
Total.Pit = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_PulACA_QTL.Rdata")
Total.PulACA = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_STS_QTL.Rdata")
Total.STS = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
load(file = "~/Desktop/R/QTL/WD/2.\ Binomial\ Mapping/Rdata/Total_Thyroid_QTL.Rdata")
Total.Thyroid = plot.hs.qtl(qtl)
rm(qtl, file.prefix)
#Combining the columns#############################################
#Combining the columns#############################################
#Combining the columns#############################################
#Combining the columns#############################################
#Combining the columns#############################################
#Combining the columns#############################################
combined <- data.frame(
#HZE.AML = -log10(HZE.AML$p.value),
HZE.cataract = -log10(HZE.cataract$p.value),
#HZE.LSA.B = -log10(HZE.LSA.B$p.value),
HZE.LSA.DLBCL = -log10(HZE.DLBCL$p.value),
HZE.LSA.FBL = -log10(HZE.FBL$p.value),
HZE.LSA.BLL= -log10(HZE.LSA.BLL$p.value),
HZE.HardACA = -log10(HZE.HardACA$p.value),
HZE.HardAD = -log10(HZE.HardAD$p.value),
HZE.HCC = -log10(HZE.HCC$p.value),
#HZE.HS = -log10(HZE.HS$p.value),
#HZE.HSA = -log10(HZE.HSA$p.value),
HZE.LSA.PreT= -log10(HZE.LSA.PreT$p.value),
HZE.MammACA= -log10(HZE.MammACA$p.value),
#HZE.OSA= -log10(HZE.OSA$p.value),
HZE.Pit= -log10(HZE.pit$p.value),
HZE.PulACA= -log10(HZE.PulACA$p.value),
#HZE.STS= -log10(HZE.STS$p.value),
HZE.Thyroid= -log10(HZE.Thyroid$p.value),
#Gamma.AML= -log10(Gamma.AML$p.value),
Gamma.cataract= -log10(Gamma.cataract$p.value),
Gamma.GCT= -log10(Gamma.GCT$p.value),
Gamma.HardACA= -log10(Gamma.HardACA$p.value),
Gamma.HardAD= -log10(Gamma.HardAD$p.value),
Gamma.HCC= -log10(Gamma.HCC$p.value),
#Gamma.HS= -log10(Gamma.HS$p.value),
#Gamma.HSA= -log10(Gamma.HSA$p.value),
Gamma.LSA.BLL= -log10(Gamma.LSA.BLL$p.value),
#Gamma.LSA.B= -log10(Gamma.LSA.B$p.value),
Gamma.LSA.DLBCL= -log10(Gamma.LSA.DLBCL$p.value),
Gamma.LSA.FBL= -log10(Gamma.LSA.FBL$p.value),
Gamma.LSA.PreT= -log10(Gamma.LSA.PreT$p.value),
Gamma.MammACA= -log10(Gamma.MammACA$p.value),
#Gamma.OSA= -log10(Gamma.OSA$p.value),
Gamma.Pit= -log10(Gamma.Pit$p.value),
Gamma.PulACA = -log10(Gamma.PulACA$p.value),
#Gamma.STS= -log10(Gamma.STS$p.value),
Gamma.Thyroid= -log10(Gamma.Thyroid$p.value),
#Unirradiated.AML= -log10(Unirradiated.AML$p.value),
Unirradiated.cataract= -log10(Unirradiated.cataract$p.value),
#Unirradiated.HardACA= -log10(Unirradiated.HardACA$p.value),
Unirradiated.HardAD= -log10(Unirradiated.HardAD$p.value),
Unirradiated.HCC= -log10(Unirradiated.HCC$p.value),
#Unirradiated.HS= -log10(Unirradiated.HS$p.value),
#Unirradiated.HSA= -log10(Unirradiated.HSA$p.value),
Unirradiated.LSA.BLL= -log10(Unirradiated.LSA.BLL$p.value),
#Unirradiated.LSA.B= -log10(Unirradiated.LSA.B$p.value),
Unirradiated.LSA.DLBCL= -log10(Unirradiated.LSA.DLBCL$p.value),
Unirradiated.LSA.FBL= -log10(Unirradiated.LSA.FBL$p.value),
Unirradiated.LSA.PreT= -log10(Unirradiated.LSA.PreT$p.value),
#Unirradiated.MammACA= -log10(Unirradiated.MammACA$p.value),
#Unirradiated.OSA= -log10(Unirradiated.OSA$p.value),
Unirradiated.PulACA= -log10(Unirradiated.PulACA$p.value))
combined = t(combined)
hc <- hcluster(combined, method = "pearson", diag = F, upper = FALSE,
link = "complete", members = NULL, nbproc = 2, doubleprecision = T)
plot(hc)
(combined$HZE.cataract/max(combined$HZE.cataract))
##### The below transformation does not make a
##### difference at all for clustering results
##### The below transformation does not make a
##### difference at all for clustering results
combined.perm <- data.frame(
HZE.AML = (sample(combined$HZE.AML)),
HZE.cataract = (sample(combined$HZE.cataract)),
HZE.LSA.B = (sample(combined$HZE.LSA.B)),
HZE.LSA.DLBCL = (sample(combined$HZE.DLBCL)),
HZE.LSA.FBL = (sample(combined$HZE.FBL)),
HZE.LSA.BLL= (sample(combined$HZE.LSA.BLL)),
HZE.HardACA = (sample(combined$HZE.HardACA)),
HZE.HardAD = (sample(combined$HZE.HardAD)),
HZE.HCC = (sample(combined$HZE.HCC)),
HZE.HS = (sample(combined$HZE.HS)),
HZE.HSA = (sample(combined$HZE.HSA)),
HZE.LSA.PreT= (sample(combined$HZE.LSA.PreT)),
HZE.MammACA= (sample(combined$HZE.MammACA)),
HZE.OSA= (sample(combined$HZE.OSA)),
HZE.Pit= (sample(combined$HZE.Pit)),
HZE.PulACA= (sample(combined$HZE.PulACA)),
HZE.STS= (sample(combined$HZE.STS)),
HZE.Thyroid= (sample(combined$HZE.Thyroid)),
Gamma.AML= (sample(combined$Gamma.AML)),
Gamma.cataract= (sample(combined$Gamma.cataract)),
Gamma.GCT= (sample(combined$Gamma.GCT)),
Gamma.HardACA= (sample(combined$Gamma.HardACA)),
Gamma.HardAD= (sample(combined$Gamma.HardAD)),
Gamma.HCC= (sample(combined$Gamma.HCC)),
Gamma.HS= (sample(combined$Gamma.HS)),
Gamma.HSA= (sample(combined$Gamma.HSA)),
Gamma.LSA.BLL= (sample(combined$Gamma.LSA.BLL)),
Gamma.LSA.B= (sample(combined$Gamma.LSA.B)),
Gamma.LSA.DLBCL= (sample(combined$Gamma.LSA.DLBCL)),
Gamma.LSA.FBL= (sample(combined$Gamma.LSA.FBL)),
Gamma.LSA.PreT= (sample(combined$Gamma.LSA.PreT)),
Gamma.MammACA= (sample(combined$Gamma.MammACA)),
Gamma.OSA= (sample(combined$Gamma.OSA)),
Gamma.Pit= (sample(combined$Gamma.Pit)),
Gamma.PulACA = (sample(combined$Gamma.PulACA)),
Gamma.STS= (sample(combined$Gamma.STS)),
Gamma.Thyroid= (sample(combined$Gamma.Thyroid)),
Unirradiated.AML= (sample(combined$Unirradiated.AML)),
Unirradiated.cataract= (sample(combined$Unirradiated.cataract)),
Unirradiated.HardACA= (sample(combined$Unirradiated.HardACA)),
Unirradiated.HardAD= (sample(combined$Unirradiated.HardAD)),
Unirradiated.HCC= (sample(combined$Unirradiated.HCC)),
Unirradiated.HS= (sample(combined$Unirradiated.HS)),
Unirradiated.HSA= (sample(combined$Unirradiated.HSA)),
Unirradiated.LSA.BLL= (sample(combined$Unirradiated.LSA.BLL)),
Unirradiated.LSA.B= (sample(combined$Unirradiated.LSA.B)),
Unirradiated.LSA.DLBCL= (sample(combined$Unirradiated.LSA.DLBCL)),
Unirradiated.LSA.FBL= (sample(combined$Unirradiated.LSA.FBL)),
Unirradiated.LSA.PreT= (sample(combined$Unirradiated.LSA.PreT)),
Unirradiated.MammACA= (sample(combined$Unirradiated.MammACA)),
Unirradiated.OSA= (sample(combined$Unirradiated.OSA)),
Unirradiated.PulACA= (sample(combined$Unirradiated.PulACA)))
## Method: "euclidean", "maximum", "manhattan",
## "canberra", "binary", "pearson", "abspearson",
## "correlation", "abscorrelation", "spearman", "kendall"
## Link: "ward", "single", "complete", "average",
## "mcquitty", "median", "centroid","centroid2"
hc <- hcluster(t(combined13), method = "pearson", diag = F, upper = FALSE,
link = "complete", members = NULL, nbproc = 2, doubleprecision = T)
plot(hc)
abline(h=0.2440728, col = "green")
pvalue = sigclust(combined, nsim = 1000, icovest = 3)
plot(pvalue)
##### Permutation for hcluster ######=
perms = 10000
time = Sys.time()
cluster.height = matrix(1, nrow = perms, ncol = 2, dimnames = list(1:perms, c("min", "average")))
for(p in 1:perms) {
print(p)
combined.perm <- data.frame(
HZE.AML = (sample(combined$HZE.AML)),
HZE.cataract = (sample(combined$HZE.cataract)),
#HZE.LSA.B = (sample(combined$HZE.LSA.B)),
HZE.LSA.DLBCL = (sample(combined$HZE.LSA.DLBCL)),
HZE.LSA.FBL = (sample(combined$HZE.LSA.FBL)),
HZE.LSA.BLL= (sample(combined$HZE.LSA.BLL)),
HZE.HardACA = (sample(combined$HZE.HardACA)),
HZE.HardAD = (sample(combined$HZE.HardAD)),
HZE.HCC = (sample(combined$HZE.HCC)),
#HZE.HS = (sample(combined$HZE.HS)),
#HZE.HSA = (sample(combined$HZE.HSA)),
HZE.LSA.PreT= (sample(combined$HZE.LSA.PreT)),
HZE.MammACA= (sample(combined$HZE.MammACA)),
#HZE.OSA= (sample(combined$HZE.OSA)),
HZE.Pit= (sample(combined$HZE.Pit)),
HZE.PulACA= (sample(combined$HZE.PulACA)),
#HZE.STS= (sample(combined$HZE.STS)),
HZE.Thyroid= (sample(combined$HZE.Thyroid)),
Gamma.AML= (sample(combined$Gamma.AML)),
Gamma.cataract= (sample(combined$Gamma.cataract)),
Gamma.GCT= (sample(combined$Gamma.GCT)),
Gamma.HardACA= (sample(combined$Gamma.HardACA)),
Gamma.HardAD= (sample(combined$Gamma.HardAD)),
Gamma.HCC= (sample(combined$Gamma.HCC)),
#Gamma.HS= (sample(combined$Gamma.HS)),
#Gamma.HSA= (sample(combined$Gamma.HSA)),
Gamma.LSA.BLL= (sample(combined$Gamma.LSA.BLL)),
#Gamma.LSA.B= (sample(combined$Gamma.LSA.B)),
Gamma.LSA.DLBCL= (sample(combined$Gamma.LSA.DLBCL)),
Gamma.LSA.FBL= (sample(combined$Gamma.LSA.FBL)),
Gamma.LSA.PreT= (sample(combined$Gamma.LSA.PreT)),
Gamma.MammACA= (sample(combined$Gamma.MammACA)),
#Gamma.OSA= (sample(combined$Gamma.OSA)),
Gamma.Pit= (sample(combined$Gamma.Pit)),
Gamma.PulACA = (sample(combined$Gamma.PulACA)),
#Gamma.STS= (sample(combined$Gamma.STS)),
Gamma.Thyroid= (sample(combined$Gamma.Thyroid)),
Unirradiated.AML= (sample(combined$Unirradiated.AML)),
Unirradiated.cataract= (sample(combined$Unirradiated.cataract)),
#Unirradiated.HardACA= (sample(combined$Unirradiated.HardACA)),
Unirradiated.HardAD= (sample(combined$Unirradiated.HardAD)),
Unirradiated.HCC= (sample(combined$Unirradiated.HCC)),
#Unirradiated.HS= (sample(combined$Unirradiated.HS)),
#Unirradiated.HSA= (sample(combined$Unirradiated.HSA)),
Unirradiated.LSA.BLL= (sample(combined$Unirradiated.LSA.BLL)),
#Unirradiated.LSA.B= (sample(combined$Unirradiated.LSA.B)),
Unirradiated.LSA.DLBCL= (sample(combined$Unirradiated.LSA.DLBCL)),
Unirradiated.LSA.FBL= (sample(combined$Unirradiated.LSA.FBL)),
Unirradiated.LSA.PreT= (sample(combined$Unirradiated.LSA.PreT)),
#Unirradiated.MammACA= (sample(combined$Unirradiated.MammACA)),
#Unirradiated.OSA= (sample(combined$Unirradiated.OSA)),
Unirradiated.PulACA= (sample(combined$Unirradiated.PulACA)))
hc <- hcluster(t(combined.perm), method = "pearson", diag = F, upper = FALSE,
link = "complete", members = NULL, nbproc = 2, doubleprecision = T)
cluster.height[p,] = c(min(hc$height), ((min(hc$height) + max(hc$height))/2))
print(paste(round(difftime(Sys.time(), time, units = 'mins'), digits = 2),
"minutes..."))
}
hist(cluster.height[,1], breaks = 75, main = "Distribution of Minimum Heights for Null Distribution")
quantile(cluster.height[,1],c(0.025,0.975))
quantile(cluster.height[,1],c(0.01,0.99))
abline(v=0.2505452, col = "green")
SFfigure(data = combined, locusname = 3)
LOD.fnx <- function(x){
if(x >= "2")
return(x)
if(x < "2")
return(0)
}
combined$HZE.AML <- sapply(combined$HZE.AML, LOD.fnx)
combined$HZE.cataract <- sapply(combined$HZE.cataract, LOD.fnx)
combined$HZE.LSA.DLBCL <- sapply(combined$HZE.LSA.DLBCL, LOD.fnx)
combined$HZE.LSA.FBL <- sapply(combined$HZE.LSA.FBL, LOD.fnx)
combined$HZE.HardACA <- sapply(combined$HZE.HardACA, LOD.fnx)
combined$HZE.HardAD <- sapply(combined$HZE.HardAD, LOD.fnx)
combined$HZE.HCC <- sapply(combined$HZE.HCC, LOD.fnx)
combined$HZE.HS <- sapply(combined$HZE.HS, LOD.fnx)
combined$HZE.LSA.BLL <- sapply(combined$HZE.LSA.BLL, LOD.fnx)
combined$HZE.LSA.PreT <- sapply(combined$HZE.LSA.PreT, LOD.fnx)
combined$HZE.MammACA <- sapply(combined$HZE.MammACA, LOD.fnx)
combined$HZE.OSA <- sapply(combined$HZE.OSA, LOD.fnx)
combined$HZE.Pit <- sapply(combined$HZE.Pit, LOD.fnx)
combined$HZE.PulACA <- sapply(combined$HZE.PulACA, LOD.fnx)
combined$HZE.STS <- sapply(combined$HZE.STS, LOD.fnx)
combined$HZE.Thyroid <- sapply(combined$HZE.Thyroid, LOD.fnx)
combined$HZE.NN <- sapply(combined$HZE.NN, LOD.fnx)
combined$Gamma.AML <- sapply(combined$Gamma.AML, LOD.fnx)
combined$Gamma.cataract <- sapply(combined$Gamma.cataract, LOD.fnx)
combined$Gamma.GCT <- sapply(combined$Gamma.GCT, LOD.fnx)
combined$Gamma.HardACA <- sapply(combined$Gamma.HardACA, LOD.fnx)
combined$Gamma.HardAD <- sapply(combined$Gamma.HardAD, LOD.fnx)
combined$Gamma.HCC <- sapply(combined$Gamma.HCC, LOD.fnx)
combined$Gamma.HS <- sapply(combined$Gamma.HS, LOD.fnx)
combined$Gamma.LSA.BLL <- sapply(combined$Gamma.LSA.BLL, LOD.fnx)
combined$Gamma.LSA.DLBCL <- sapply(combined$Gamma.LSA.DLBCL, LOD.fnx)
combined$Gamma.LSA.FBL <- sapply(combined$Gamma.LSA.FBL, LOD.fnx)
combined$Gamma.LSA.PreT <- sapply(combined$Gamma.LSA.PreT, LOD.fnx)
combined$Gamma.MammACA <- sapply(combined$Gamma.MammACA, LOD.fnx)
combined$Gamma.OSA <- sapply(combined$Gamma.OSA, LOD.fnx)
combined$Gamma.Pit <- sapply(combined$Gamma.Pit, LOD.fnx)
combined$Gamma.PulACA <- sapply(combined$Gamma.PulACA, LOD.fnx)
combined$Gamma.STS <- sapply(combined$Gamma.STS, LOD.fnx)
combined$Gamma.Thyroid <- sapply(combined$Gamma.Thyroid, LOD.fnx)
combined$Gamma.NN <- sapply(combined$Gamma.NN, LOD.fnx)
combined$Unirradiated.AML <- sapply(combined$Unirradiated.AML, LOD.fnx)
combined$Unirradiated.cataract <- sapply(combined$Unirradiated.cataract, LOD.fnx)
combined$Unirradiated.HardACA <- sapply(combined$Unirradiated.HardACA, LOD.fnx)
combined$Unirradiated.HardAD <- sapply(combined$Unirradiated.HardAD, LOD.fnx)
combined$Unirradiated.HCC <- sapply(combined$Unirradiated.HCC, LOD.fnx)
combined$Unirradiated.HS <- sapply(combined$Unirradiated.HS, LOD.fnx)
combined$Unirradiated.HSA <- sapply(combined$Unirradiated.HSA, LOD.fnx)
combined$Unirradiated.LSA.BLL <- sapply(combined$Unirradiated.LSA.BLL, LOD.fnx)
combined$Unirradiated.LSA.DLBCL <- sapply(combined$Unirradiated.LSA.DLBCL, LOD.fnx)
combined$Unirradiated.LSA.FBL <- sapply(combined$Unirradiated.LSA.FBL, LOD.fnx)
combined$Unirradiated.LSA.PreT <- sapply(combined$Unirradiated.LSA.PreT, LOD.fnx)
combined$Unirradiated.MammACA <- sapply(combined$Unirradiated.MammACA, LOD.fnx)
combined$Unirradiated.OSA <- sapply(combined$Unirradiated.OSA, LOD.fnx)
combined$Unirradiated.PulACA <- sapply(combined$Unirradiated.PulACA, LOD.fnx)
combined$Unirradiated.STS <- sapply(combined$Unirradiated.STS, LOD.fnx)
combined$Unirradiated.Thyroid <- sapply(combined$Unirradiated.Thyroid, LOD.fnx)
combined$Unirradiated.NN <- sapply(combined$Unirradiated.NN, LOD.fnx)
combined.max.divide <- data.frame(HZE.AML = (combined$HZE.AML/max(combined$HZE.AML)),
HZE.cataract = (combined$HZE.cataract/max(combined$HZE.cataract)),
HZE.LSA.DLBCL = (combined$HZE.LSA.DLBCL)/max(combined$HZE.LSA.DLBCL),
HZE.LSA.FBL = (combined$HZE.LSA.FBL/max(combined$HZE.LSA.FBL)),
HZE.HardACA = (combined$HZE.HardACA/max(combined$HZE.HardACA)),
HZE.HardAD = (combined$HZE.HardAD/max(combined$HZE.HardAD)),
HZE.HCC = (combined$HZE.HCC/max(combined$HZE.HCC)),
HZE.HS = (combined$HZE.HS/max(combined$HZE.HS)),
HZE.HSA = (combined$HZE.HSA/max(combined$HZE.HSA)),
HZE.LSA.BLL= (combined$HZE.LSA.BLL/max(combined$HZE.LSA.BLL)),
HZE.LSA.PreT= (combined$HZE.LSA.PreT/max(combined$HZE.LSA.PreT)),
HZE.MammACA= (combined$HZE.MammACA/max(combined$HZE.MammACA)),
HZE.OSA= (combined$HZE.OSA/max(combined$HZE.OSA)),
HZE.Pit= (combined$HZE.Pit/max(combined$HZE.Pit)),
HZE.PulACA= (combined$HZE.PulACA/max(combined$HZE.PulACA)),
HZE.STS= (combined$HZE.STS/max(combined$HZE.STS)),
HZE.Thyroid= (combined$HZE.Thyroid/max(combined$HZE.Thyroid)),
Gamma.AML= (combined$Gamma.AML/max(combined$Gamma.AML)),
Gamma.cataract= (combined$Gamma.cataract/max(combined$Gamma.cataract)),
Gamma.GCT= (combined$Gamma.GCT/max(combined$Gamma.GCT)),
Gamma.HardACA= (combined$Gamma.HardACA/max(combined$Gamma.HardACA)),
Gamma.HardAD= (combined$Gamma.HardAD/max(combined$Gamma.HardAD)),
Gamma.HCC= (combined$Gamma.HCC/max(combined$Gamma.HCC)),
Gamma.HS= (combined$Gamma.HS/max(combined$Gamma.HS)),
Gamma.HSA= (combined$Gamma.HSA/max(combined$Gamma.HSA)),
Gamma.LSA.BLL= (combined$Gamma.LSA.BLL/max(combined$Gamma.LSA.BLL)),
Gamma.LSA.DLBCL= (combined$Gamma.LSA.DLBCL/max(combined$Gamma.LSA.DLBCL)),
Gamma.LSA.FBL= (combined$Gamma.LSA.FBL/max(combined$Gamma.LSA.FBL)),
Gamma.LSA.PreT= (combined$Gamma.LSA.PreT/max(combined$Gamma.LSA.PreT)),
Gamma.MammACA= (combined$Gamma.MammACA/max(combined$Gamma.MammACA)),
Gamma.OSA= (combined$Gamma.OSA/max(combined$Gamma.OSA)),
Gamma.Pit= (combined$Gamma.Pit/max(combined$Gamma.Pit)),
Gamma.PulACA= (combined$Gamma.PulACA/max(combined$Gamma.PulACA)),
Gamma.STS= (combined$Gamma.STS/max(combined$Gamma.STS)),
Gamma.Thyroid= (combined$Gamma.Thyroid/max(combined$Gamma.Thyroid)),
Unirradiated.AML= (combined$Unirradiated.AML/max(combined$Unirradiated.AML)),
Unirradiated.cataract= (combined$Unirradiated.cataract/max(combined$Unirradiated.cataract)),
Unirradiated.HardACA= (combined$Unirradiated.HardACA/max(combined$Unirradiated.HardACA)),
Unirradiated.HardAD= (combined$Unirradiated.HardAD/max(combined$Unirradiated.HardAD)),
Unirradiated.HCC= (combined$Unirradiated.HCC/max(combined$Unirradiated.HCC)),
Unirradiated.HS= (combined$Unirradiated.HS/max(combined$Unirradiated.HS)),
Unirradiated.HSA= (combined$Unirradiated.HSA/max(combined$Unirradiated.HSA)),
Unirradiated.LSA.BLL= (combined$Unirradiated.LSA.BLL/max(combined$Unirradiated.LSA.BLL)),
Unirradiated.LSA.DLBCL= (combined$Unirradiated.LSA.DLBCL/max(combined$Unirradiated.LSA.DLBCL)),
Unirradiated.LSA.FBL= (combined$Unirradiated.LSA.FBL/max(combined$Unirradiated.LSA.FBL)),
Unirradiated.LSA.PreT= (combined$Unirradiated.LSA.PreT/max(combined$Unirradiated.LSA.PreT)),
Unirradiated.MammACA= (combined$Unirradiated.MammACA/max(combined$Unirradiated.MammACA)),
Unirradiated.OSA= (combined$Unirradiated.OSA/max(combined$Unirradiated.OSA)),
Unirradiated.PulACA= (combined$Unirradiated.PulACA/max(combined$Unirradiated.PulACA)),
Unirradiated.STS= (combined$Unirradiated.STS/max(combined$Unirradiated.STS)),
Unirradiated.Thyroid = (combined$Unirradiated.Thyroid/max(combined$Unirradiated.Thyroid)),
HZE.NN = (combined$HZE.NN/max(combined$HZE.NN)),
Gamma.NN = (combined$Gamma.NN/max(combined$Gamma.NN)),
Unirradiated.NN = (combined$Unirradiated.NN/max(combined$Unirradiated.NN)))
colSums(combined.max.divide)
##################################### made4 #####################################
##################################### made4 #####################################
##################################### made4 #####################################
##################################### made4 #####################################
require(made4)
combined1 = combined[1:933,]
combined2 = combined[934:1616,]
combined3 = combined[1617:2273,]
combined4 = combined[2274:2923,]
combined5 = combined[2924:3428,]
combined6 = combined[3429:4023,]
combined7 = combined[4024:4681,]
combined8 = combined[4682:5250,]
combined9 = combined[5251:5846,]
combined10 = combined[5847:6189,]
combined11 = combined[6190:6756,]
combined12 = combined[6757:7207,]
combined13 = combined[7208:7766,]
combined14 = combined[,]
combined15 = combined[,]
combined16 = combined[,]
combined17 = combined[,]
combined18 = combined[,]
combined19 = combined[,]
Gamma.AML@seqnames
combined = combined[, colSums(combined) > 0.5]
combined1 = combined1[, colSums(combined1) > 0.5]
combined2 = combined2[, colSums(combined2) > 0.5]
combined3 = combined3[, colSums(combined3) > 0.5]
combined4 = combined4[, colSums(combined4) > 0.5]
combined5 = combined5[, colSums(combined5) > 0.5]
combined1cat = cbind(HZE = combined1$HZE.cataract,
Gamma = combined1$Gamma.cataract,
Unirradiated = combined1$Unirradiated.cataract)
combined2thy = cbind(HZE = combined2$HZE.Thyroid,
Gamma = combined2$Gamma.Thyroid,
Unirradiated = combined2$Unirradiated.Thyroid)
heatplot(t(combined), margins = c(10, 13), dend="row", method = "ward", main = "", key = F, labCol=NA)