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Commit 57d14cc7 authored by Seifert, Prof. Dr. Stephan's avatar Seifert, Prof. Dr. Stephan
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Projekt angelegt und Daten gespeichert

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data
subprojects/01_subproject/R_functions/html_functions.R 0 → 100644
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myTabl = function(data){
DT::datatable(data, rownames = FALSE,
class = 'cell-border stripe',
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'excel', 'pdf'),
autoWidth = TRUE))
}
myPlot = function(data){
}
\ No newline at end of file
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*.pdf
*.RData
*.tex
*.log
subprojects/01_subproject/scripts/.Rhistory 0 → 100644
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load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/ranger_janitza/different_seeds/seq_seed1.RData")
array.seed1=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/ranger_janitza/different_seeds/array_seed2.RData")
array.seed2=saveLoadReference
importance.array1=array.seed1$importance.array
importance.array2=array.seed2$importance.array
variables.array2=subset(importance.array2,importance.array2[,2]==0)
variables.array1=subset(importance.array1,importance.array1[,2]==0)
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/ranger_janitza/different_seeds/seq_seed1.RData")
array.seq1=saveLoadReference
files.dir="sftp://stephan@balin/home/stephan/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/ranger_janitza/different_seeds"
?loadObject
library(base)
?loadObject
require(R.utils)
?loadObject
seq.seed1=loadObject(file=paste(files.dir,"/seq_seed1.RData"))
files.dir="/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/ranger_janitza"
seq.seed1=loadObject(file=paste(files.dir,"/seq_seed1.RData"))
seq.seed1=loadObject(file=paste0(files.dir,"/seq_seed1.RData"))
files.dir="/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/ranger_janitza/different_seeds"
seq.seed1=loadObject(file=paste0(files.dir,"/seq_seed1.RData"))
files.dir="/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/ranger_janitza/different_seeds"
require(R.utils)
seq.seed1=loadObject(file=paste0(files.dir,"/seq_seed1.RData"))
seq.seed2=loadObject(file=paste0(files.dir,"/seq_seed2.RData"))
seq.array1=loadObject(file=paste0(files.dir,"/array_seed1.RData"))
seq.array2=loadObject(file=paste0(files.dir,"/array_seed2.RData"))
array.seed1=loadObject(file=paste0(files.dir,"/array_seed1.RData"))
array.seed2=loadObject(file=paste0(files.dir,"/array_seed2.RData"))
importance.array1=array.seed1$importance.array
importance.array2=array.seed2$importance.array
importance.seq2=seq.seed2$importance.array
importance.seq1=seq.seed1$importance.array
variables.array1=subset(importance.array1,importance.array1[,2]==0)
variables.array2=subset(importance.array2,importance.array2[,2]==0)
variables.seq1=subset(importance.seq1,importance.seq1[,2]==0)
variables.seq2=subset(importance.seq2,importance.seq2[,2]==0)
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/data_input/breastcancer.array.RData")
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/array/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_1.RData")
array1.boruta=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/array/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_2.RData")
array2.boruta=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/array/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_42.RData")
array.frauke.boruta=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/rna/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_1.RData")
seq1.boruta=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/rna/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_2.RData")
seq2.boruta=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/rna/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_42.RData")
seq.frauke.boruta=saveLoadReference
intersect(array.frauke.boruta$var, seq.frauke.boruta$var))
intersect(array.frauke.boruta$var, seq.frauke.boruta$var))
intersect(array.frauke.boruta$var, seq.frauke.boruta$var)
iarray.frauke.boruta$var
array.frauke.boruta$var
intersect(array.frauke.boruta$info$var, seq.frauke.boruta$info$var)
array.frauke.boruta$info$var
array.frauke.boruta
array.frauke.boruta$info
array.frauke.boruta[[1]]$info$var
array.frauke.boruta[[1]]$info
array.frauke.boruta[[1]]$var
intersect(array.frauke.boruta[[1]]$var, seq.frauke.boruta[[1]]$var)
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/data_input/breastcancer.array.RData")
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/data_input/breastcancer.array.RData")
saveLoadReference
View(saveLoadReference)
sim.file = file.path("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data_input/sum_exp_rna_array_common_genes_standardized.RData")
sim.data = loadObject(file = sim.file)
library(relVarId)
sim.data = loadObject(file = sim.file)
library(R.utils)
sim.data = loadObject(file = sim.file)
sim.file = file.path("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data_input/sum_exp_rna_array_common_genes_standardized.RData")
sim.data = loadObject(file = sim.file)
data.array=assays(sim.data)[[1]]
View(data.array)
y.array=colData(sim.data)$estrogen
data.array.use= data.array[,-which(is.na(y.array))]
y.array.use=y.array[-which(is.na(y.array))]
y.array
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/array/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_1.RData")
boruta.array.1
boruta.array.1=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/array/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_1.RData")
boruta.array.1=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/array/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_2.RData")
boruta.array.1=saveLoadReference
boruta.array.2=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/array/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_1.RData")
boruta.array.1=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/array/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_42.RData")
boruta.array.f=saveLoadReference
var.boruta.array.1[[1]]$var
var.boruta.array.1=boruta-array.1[[1]]$var
var.boruta.array.1=boruta.array.1[[1]]$var
var.boruta.array.2=boruta.array.2[[1]]$var
var.boruta.array.f=boruta.array.f[[1]]$var
length(intersect(var.boruta.array.1,var.boruta.array.2)
length(intersect(var.boruta.array.1,var.boruta.array.2))
length(intersect(var.boruta.array.3,var.boruta.array.2))
length(intersect(var.boruta.array.f,var.boruta.array.2))
length(intersect(var.boruta.array.f,var.boruta.array.1))
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/rna/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_1.RData")
boruta.seq.1=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/rna/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_2.RData")
boruta.seq.2=saveLoadReference
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/VarSelMinDep/subprojects/13_frauke/output/classification/rna/ntree_10000_mtry_prop_0.33/boruta/par_standard/results_rep_42.RData")
boruta.seq.f=saveLoadReference
var.boruta.seq.f=boruta.seq.f[[1]]$var
var.boruta.seq.1=boruta.seq.1[[1]]$var
var.boruta.seq.2=boruta.seq.2[[1]]$var
length(intersect(var.boruta.seq.f,var.boruta.seq.1))
length(intersect(var.boruta.seq.2,var.boruta.seq.1))
length(intersect(var.boruta.seq.2,var.boruta.seq.f))
length(intersect(var.boruta.array.f,var.boruta.seq.f))
20/104
?wrapper.rf
library(Boruta)
?Boruta
Boruta
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/results_silke.RData")
results.silke=saveLoadReference
info.res.silke=results.silke$info.var.sel
View(info.res.silke)
var.silke.array=colnames(subset(info.res.silke,info.res.silke$var.sel.boruta.array==1))
var.silke.array=rownames(subset(info.res.silke,info.res.silke$var.sel.boruta.array==1))
var.silke.seq=rownames(subset(info.res.silke,info.res.silke$var.sel.boruta.rna==1))
intersect(var.boruta.array.f,var.silke.array)
intersect(var.boruta.seq.f,var.silke.seq)
array.txt=read.table("/home/stephan/Dokumente/VarSelMinDep/subprojects/13_frauke/data_input/sum_exp_rna_array_common_genes_standardized_assay.txt")
View(array.txt)
array.col=read.table("/home/stephan/Dokumente/VarSelMinDep/subprojects/13_frauke/data_input/sum_exp_rna_array_common_genes_standardized_coldata.txt")
View(array.txt)
sim.file = file.path("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data_input/sum_exp_rna_array_common_genes_standardized.RData")
sim.data = loadObject(file = sim.file)
data.array=assays(sim.data)[[1]]
array.txt.matrix=as.matrix(array.txt)
View(array.txt.matrix)
View(data.array)
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/allmethods/Boruta_seedFrauke_relVarID0.41_VitaSMD_from_50100200/array_train/SMD100.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/allmethods/Boruta_seedFrauke_relVarID0.41_VitaSMD_from_50100200/array_train/Janitza.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/allmethods/Boruta_seedFrauke_relVarID0.41_VitaSMD_from_50100200/array_train/SMD100.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/allmethods/Boruta_seedFrauke_relVarID0.41_VitaSMD_from_50100200/array_train/Janitza.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/allmethods/Boruta_seedFrauke_relVarID0.41_VitaSMD_from_50100200/array_train/Boruta.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/allmethods/50100200/array_train/SMD50.RData")
var.array=saveLoadReference$var
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/12_experimental/data/gene_expression_array/experimental/classification/results/ntree_10000_mtry_prop_0.33/allmethods/50100200/seq_train/SMD50.RData")
var.seq=saveLoadReference$var
intersect(var.array,var.seq)
length(intersect(var.array,var.seq))/length(union(var.array,var.seq))
length(intersect(var.array,var.seq))/min(var.array,var.seq)
length(intersect(var.array,var.seq))/141
library(ranger)
?ranger
35*72
50*72
require(MASS)
# library(geoR)
# geoR has a _fast_ function for inverse chisquare draws
# geoR is not currently available on the compute servers
if(!require(geoR)){
rinvchisq <- function(n, df) {1/rchisq(n, df)}
}
############################################################################
data.gen.DJsim <- function(n.samp, n.case, n.path, n.assoc.path,
n.gene.path, n.metab.path, d1, d0, dd1, c1, c0, cc1,
corr.gg, corr.mm, corr.mg, gamma.range, eta.range) {
##########################################
### DATA PARAMETERS ###
# n.samp, number of samples
# n.case, number of cases
# n.path, number of pathways
# n.assoc.path, number of pathways associated with case status
# n.gene.path, number of genes per pathway
# n.metab.path, number of metabolties per pathway
# d1, probability of differential gene expression within associated pathway
# d0, probability of differential gene expression otherwise
# dd1, probability of up regulation
# c1, probability of differential metabolite intensity
# within associated pathway
# c0, probability of differential metabolite intensity otherwise
# cc1, probabiliy of up reguation
# gamma.range (a,b), draw gamma from uniform U(a,b), a>=0, b>0
# eta.range (c,d), draw gamma from uniform U(c,d), c>=0, d>0
# corr.gg, correlation between two genes
# corr.mm, correlation between two metabolites
# corr.mg, correlation between gene and metabolite
############################################
n.control <- n.samp - n.case
W.i <- c(rep(1, n.case), rep(0, n.control))
## GENE EXPRESSION ##
n.gene <- n.path * n.gene.path
#alpha = 0
#beta.j
mean.gene.var <- rinvchisq(n=n.gene, df=4)
beta.j <- rnorm(n=n.gene, mean=0, sd=2*sqrt(mean.gene.var))
#omega.j
abs.omega.j <- runif(n=n.gene, min=gamma.range[1], max=gamma.range[2])
bin.omega.j <- rbinom(n=n.gene, size=1, prob=dd1)
omega.j <- abs.omega.j * ifelse(bin.omega.j==1,1,-1)
#Dj
n.assoc.gene <- n.assoc.path*n.gene.path
dd <- c(rep(d1, n.assoc.gene), rep(d0, n.gene-n.assoc.gene))
Dj <- rbinom(n.gene, 1, dd)
## METABOLITE INTENSITY ##
n.metab <- n.path * n.metab.path
#theta = 0
#phi.k
mean.metab.var <- rinvchisq(n=n.metab, df=4)
phi.k <- rnorm(n=n.metab, mean=0, sd=2*sqrt(mean.metab.var))
#eta.k
abs.eta.k <- runif(n=n.metab, min=eta.range[1], max=eta.range[2])
bin.eta.k <- rbinom(n=n.metab, size=1, prob=cc1)
eta.k <- abs.eta.k * ifelse(bin.eta.k==1,1,-1)
#Ck
n.assoc.metab <- n.assoc.path*n.metab.path
cc <- c(rep(c1, n.assoc.metab), rep(c0, n.metab-n.assoc.metab))
Ck <- rbinom(n.metab, 1, cc)
## Draw multivariate normal vectors per pathway ##
y.ij.base <- z.ik.base <- vector()
for(l in 1:n.path){
start.g <- (l-1)*n.gene.path + 1
stop.g <- l*n.gene.path
start.m <- (l-1)*n.metab.path + 1
stop.m <- l*n.metab.path
# mean vector #
mean.vector <- c(beta.j[start.g:stop.g],phi.k[start.m:stop.m])
# variance-covariance matrix #
var.g <- mean.gene.var[start.g:stop.g]
var.m <- mean.metab.var[start.m:stop.m]
cov.gg <- corr.gg*(sqrt(var.g) %*% t(sqrt(var.g))) #Ng x Ng
cov.mm <- corr.mm*(sqrt(var.m) %*% t(sqrt(var.m))) #Nm x Nm
cov.mg <- corr.mg*(sqrt(var.m) %*% t(sqrt(var.g))) #Nm x Ng
cov.mat <- rbind(cbind(cov.gg, t(cov.mg)), cbind(cov.mg, cov.mm))
diag(cov.mat) <- c(var.g, var.m)
#
y.z.base <- mvrnorm(n=n.samp, mu=mean.vector, Sigma=cov.mat)
y.ij.base <- rbind(y.ij.base, t(y.z.base)[1:n.gene.path,])
z.ik.base <- rbind(z.ik.base,
t(y.z.base)[(n.gene.path+1):(n.gene.path+n.metab.path),])
}
y.ij <- y.ij.base + (omega.j*Dj) %*% t(W.i)
z.ik <- z.ik.base + (eta.k*Ck) %*% t(W.i)
dat1 <- list(y.ij = y.ij, z.ik=z.ik)
return(dat1)
}
set.seed(7492391)
for(cycle in 1:2){
print(cycle);print(Sys.time())
sim <- data.gen.DJsim(n.samp=30, n.case=15, n.path=50, n.assoc.path=10,
n.gene.path=20, n.metab.path=4, d1=0.5, d0=0, dd1=0.5, c1=0.5,
c0=0, cc1=0.5, corr.gg=0.2, corr.mm=0.2, corr.mg=0.1,
gamma.range=c(0.5,2.5), eta.range=c(0.5,1.5))
}
View(data.gen.DJsim)
sim
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/Confounding_Celltypes_RF/02_smd_confounding/data/results/confounding_same_independent/ntree_10000_mtry_prop_0.33/smd/par_3000/results_rep_1_data_set_1.RData")
data=saveLoadReference
data$var
length(data$var)
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/Confounding_Celltypes_RF/02_smd_confounding/data/results/confounding_same_independent/ntree_10000_mtry_prop_0.33/smd/par_3000/results_rep_4_data_set_1.RData")
data2=saveLoadReference
data2$trees[[1]]
load("/run/user/1000/gvfs/sftp:host=balin,user=stephan/home/stephan/Confounding_Celltypes_RF/02_smd_confounding/data/results/confounding_same_independent/ntree_10000_mtry_prop_0.33/smd/par_3000/results_rep_7_data_set_1.RData")
1200*12
14400*15
load("/home/stephan/Dokumente/Confounding_Celltypes_RF/01_simulation_confounding/data/confounding_only/input/sum_exp_object_rep_1_test.RData")
data=saveLoadReference
colData=colData(data)
library(SummarizedExperiment)
colData=colData(data)
View(colData)
View(rowRanges())
View(rowRanges
View(rowRanges)
rowRanges=rowRanges(data)
View(colData)
info = as.data.frame(mcols(se.sim))
info = as.data.frame(mcols(data))
View(info)
pheno = as.data.frame(colData(data))
View(pheno)
meth = assays(sim.se)$meth
meth = assays(data)$meth
meth = assays(data)
View(info)
colnames(meth)
names=rownames(meth)
meth = assays(data)$meth
colnames(meth)
View(meth)
data.gen.DJsim <- function(n.samp, n.case, n.path, n.assoc.path,
n.gene.path, n.metab.path, d1, d0, dd1, c1, c0, cc1,
corr.gg, corr.mm, corr.mg, gamma.range, eta.range) {
##########################################
### DATA PARAMETERS ###
# n.samp, number of samples
# n.case, number of cases
# n.path, number of pathways
# n.assoc.path, number of pathways associated with case status
# n.gene.path, number of genes per pathway
# n.metab.path, number of metabolties per pathway
# d1, probability of differential gene expression within associated pathway
# d0, probability of differential gene expression otherwise
# dd1, probability of up regulation
# c1, probability of differential metabolite intensity
# within associated pathway
# c0, probability of differential metabolite intensity otherwise
# cc1, probabiliy of up reguation
# gamma.range (a,b), draw gamma from uniform U(a,b), a>=0, b>0
# eta.range (c,d), draw gamma from uniform U(c,d), c>=0, d>0
# corr.gg, correlation between two genes
# corr.mm, correlation between two metabolites
# corr.mg, correlation between gene and metabolite
############################################
n.control <- n.samp - n.case
W.i <- c(rep(1, n.case), rep(0, n.control))
## GENE EXPRESSION ##
n.gene <- n.path * n.gene.path
#alpha = 0
#beta.j
mean.gene.var <- rinvchisq(n=n.gene, df=4)
beta.j <- rnorm(n=n.gene, mean=0, sd=2*sqrt(mean.gene.var))
#omega.j
abs.omega.j <- runif(n=n.gene, min=gamma.range[1], max=gamma.range[2])
bin.omega.j <- rbinom(n=n.gene, size=1, prob=dd1)
omega.j <- abs.omega.j * ifelse(bin.omega.j==1,1,-1)
#Dj
n.assoc.gene <- n.assoc.path*n.gene.path
dd <- c(rep(d1, n.assoc.gene), rep(d0, n.gene-n.assoc.gene))
Dj <- rbinom(n.gene, 1, dd)
## METABOLITE INTENSITY ##
n.metab <- n.path * n.metab.path
#theta = 0
#phi.k
mean.metab.var <- rinvchisq(n=n.metab, df=4)
phi.k <- rnorm(n=n.metab, mean=0, sd=2*sqrt(mean.metab.var))
#eta.k
abs.eta.k <- runif(n=n.metab, min=eta.range[1], max=eta.range[2])
bin.eta.k <- rbinom(n=n.metab, size=1, prob=cc1)
eta.k <- abs.eta.k * ifelse(bin.eta.k==1,1,-1)
#Ck
n.assoc.metab <- n.assoc.path*n.metab.path
cc <- c(rep(c1, n.assoc.metab), rep(c0, n.metab-n.assoc.metab))
Ck <- rbinom(n.metab, 1, cc)
## Draw multivariate normal vectors per pathway ##
y.ij.base <- z.ik.base <- vector()
for(l in 1:n.path){
start.g <- (l-1)*n.gene.path + 1
stop.g <- l*n.gene.path
start.m <- (l-1)*n.metab.path + 1
stop.m <- l*n.metab.path
# mean vector #
mean.vector <- c(beta.j[start.g:stop.g],phi.k[start.m:stop.m])
# variance-covariance matrix #
var.g <- mean.gene.var[start.g:stop.g]
var.m <- mean.metab.var[start.m:stop.m]
cov.gg <- corr.gg*(sqrt(var.g) %*% t(sqrt(var.g))) #Ng x Ng
cov.mm <- corr.mm*(sqrt(var.m) %*% t(sqrt(var.m))) #Nm x Nm
cov.mg <- corr.mg*(sqrt(var.m) %*% t(sqrt(var.g))) #Nm x Ng
cov.mat <- rbind(cbind(cov.gg, t(cov.mg)), cbind(cov.mg, cov.mm))
diag(cov.mat) <- c(var.g, var.m)
#
y.z.base <- mvrnorm(n=n.samp, mu=mean.vector, Sigma=cov.mat)
y.ij.base <- rbind(y.ij.base, t(y.z.base)[1:n.gene.path,])
z.ik.base <- rbind(z.ik.base,
t(y.z.base)[(n.gene.path+1):(n.gene.path+n.metab.path),])
}
y.ij <- y.ij.base + (omega.j*Dj) %*% t(W.i)
z.ik <- z.ik.base + (eta.k*Ck) %*% t(W.i)
dat1 <- list(y.ij = y.ij, z.ik=z.ik)
return(dat1)
}
?rinvchisq
require(MASS)
# library(geoR)
# geoR has a _fast_ function for inverse chisquare draws
# geoR is not currently available on the compute servers
if(!require(geoR)){
rinvchisq <- function(n, df) {1/rchisq(n, df)}
}
View(rinvchisq)
?rchisq
set.seed(7492391)
for(cycle in 1:2){
print(cycle);print(Sys.time())
sim <- data.gen.DJsim(n.samp=30, n.case=15, n.path=50, n.assoc.path=10,
n.gene.path=20, n.metab.path=4, d1=0.5, d0=0, dd1=0.5, c1=0.5,
c0=0, cc1=0.5, corr.gg=0.2, corr.mm=0.2, corr.mg=0.1,
gamma.range=c(0.5,2.5), eta.range=c(0.5,1.5))
}
sim
library(relVarId)
?is.na
32
is.na(as.numeric(31))
is.na(as.numeric("test"))
round("test")
test = "test"
if (is.na(test) {
mtry.prop = as.numeric(test)
} else {
mtry.prop = test
}
if (is.na(test) {
mtry.prop = as.numeric(test)
} else {
mtry.prop = test
}
test=42
if (is.na(test) {
mtry.prop = as.numeric(test)
} else {
mtry.prop = test
}
load("/run/user/1000/gvfs/sftp:host=balin/home/stephan/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_sqrt_nodesize_prop_7.94155e-05/boruta/par_0.01/results_rep_1_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_1_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_1_data_set_2.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_2_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_2_data_set_2.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_3_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_0.33/janitza/par_0/results_rep_1_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_0.33/janitza/par_0/results_rep_1_data_set_2.RData")
load("/run/user/1000/gvfs/sftp:host=balin/home/stephan/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_sqrt_nodesize_prop_7.94155e-05/boruta/par_0.01/results_rep_1_data_set_2.RData")
load("/run/user/1000/gvfs/sftp:host=balin/home/stephan/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_sqrt_nodesize_prop_7.94155e-05/boruta/par_0.01/results_rep_1_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_5_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_5_data_set_2.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_4_data_set_2.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_4_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_3_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_3_data_set_2.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_2_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_2_data_set_2.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_1_data_set_1.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_1_data_set_2.RData")
load("/home/stephan/Dokumente/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_^(3/4)_nodesize_prop_7.94155e-05/janitza/par_0/results_rep_7_data_set_1.RData")
load("/run/user/1000/gvfs/sftp:host=balin/home/stephan/VarSelMinDep/subprojects/04_Comparison/data/gene_expression_array/simulation/classification/effects_-2_-1_-0.5_0.5_1_2/samples_200/causal.var_150/results/ntree_10000_mtry_prop_sqrt_nodesize_prop_7.94155e-05/boruta/par_0.02/results_rep_1_data_set_1.RData")
8350*12
7650*12
91800*0.8
myTabl()
? myTabl()
install.packages("webshot")
install.packages("htmlwidgets","PhantomJS")
setwd("~/Dokumente/Projektstruktur_markdown/project_template/subprojects/01_subproject/scripts")
library(SurrogateMinimalDepth)
data("SMD_example_data")
data = data("SMD_example_data")
data
dim(SMD_example_data)
library(DT)
install.packages("DT")
?library
?require
?datatable
View(SMD_example_data)
source("init.R")
## functions
files = dir(functions.dir, pattern = ".R*", full.name = TRUE)
if (length(files) > 0) {
print(paste("loading", length(files), "file(s) ..."))
for (f in files) {
source(f)
}
}
data("SMD_example_data") %>% myTabl()
class(SMD_example_data)
%>% myTabl()
data("SMD_example_data")
%>% myTabl()+
data("SMD_example_data")
%>% myTabl()
myTabl(data)
data
View(SMD_example_data)
str(SMD_example_data)
source("init.R")
source("init.R")
View(SMD_example_data)
?apply
data.plot = apply(data,1,floor)
dim(data.plot = apply(SMD_example_data,1,floor))
data.plot = apply(SMD_example_data,1,floor)
View(data.plot)
?floor
data.plot = apply(SMD_example_data,1,floor(digits = 3))
data.plot = apply(SMD_example_data,1,round(digits = 3))
data.plot = round(SMD_example_data, digits = 3)
View(data.plot)
?datatable
install.packages("metricsgraphic")
devtools::install_github("hrbrmstr/metricsgraphics")
View(data.plot)
?mjs_plot
require(metricsgraphic)
require(metricsgraphics)
?mjs_plot
?mjs_point
source("init.R")
?mjs_plot
?mjs_points
?mjs_point
devtools::install_github("homerhanumat/bpexploder")
library(bpexploder)
View(data.plot)
data("iris")
iris
View(iris)
data = data.plot[,1]
1/9000000000
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cache
simulation_binary.Rnw
simulation_quantitative.Rnw
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− 0
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## information about directories
subproject.dir = dirname(getwd())
project.dir = dirname(dirname(subproject.dir))
## data
input.dir = file.path(project.dir, "data_input")
data.dir = file.path(subproject.dir, "data")
## analysis
plots.dir = file.path(subproject.dir, "img")
## functions
functions.dir = file.path(subproject.dir, "R_functions")
## load libraries
require(DT) ## interactive tables
require(R.utils) ## loadObject(), saveObject()
require(metricsgraphics) ## interactive graphs
require(summarytools)
require(SummarizedExperiment)
## replace = with <-; set code/output width to be 68
#options(replace.assign=TRUE)
## functions
files = dir(functions.dir, pattern = ".R*", full.name = TRUE)
if (length(files) > 0) {
print(paste("loading", length(files), "file(s) ..."))
for (f in files) {
source(f)
}
}
set.dir = function(dir) {
if (!file.exists(dir)) {
dir.create(dir, recursive = TRUE)
}
return(dir)
}
get.no.lines = function(f) {
temp = system(paste("wc -l", f), intern = TRUE)
return(as.numeric(unlist(strsplit(temp, " "))[1]))
}
subprojects/01_subproject/scripts/main.Rmd 0 → 100644
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---
title: "Example of title"
author: "Dr. Stephan Seifert"
date: '`r Sys.Date()`'
output:
html_document:
code_folding: hide
highlight: textmate
toc: yes
toc_float: yes
pdf_document:
toc: yes
---
\clearpage
```{r echo = FALSE, results = "hide", message = FALSE}
source("init.R")
```
\clearpage
```{r child = "subscript.Rmd"}
```
\clearpage
# Session information
```{r}
sessionInfo()
```
subprojects/01_subproject/scripts/main.html 0 → 100644
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− 0
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subprojects/01_subproject/scripts/subscript.Rmd 0 → 100644
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```{r, echo=F, message=F, warning=F}
library(DT, quietly = T)
```
# Tables
This is an example how you can create interactive Tables with datatable function, Kable and Xtable can be used for "normal" tables.
```{r}
data("iris")
datatable(iris, rownames = FALSE,
caption = "Table 1: This is a Table",
class = 'cell-border stripe',
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'excel', 'pdf'),
autoWidth = TRUE))
```
# Graphs
Here you can see how interactive graphs can be created based on mjs_plot function
```{r}
mjs_plot(iris[,1:2], x = colnames(iris)[1], y = colnames(iris)[2]) %>%
mjs_point() %>%
mjs_labs(x = colnames(iris)[1], y = colnames(iris)[2])
```
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