Description Usage Arguments Details Value Author(s) Examples
generate S for ISKmeans
1 2 3 4 |
seed |
random seed |
S |
number of studies |
k |
number of clusters |
meanSamplesPerK |
mean samples per cluster |
nModule |
number of modules. A module is a group of genes. |
meanGenesPerModule |
number of genes per module |
Gmean |
gene expression template follows N(Gmean,Gsd^2) |
Gsd |
gene expression template follows N(Gmean,Gsd^2) |
sigma1 |
noise 1 |
sigma2 |
noise 2 |
sigma3 |
noise 3 |
G0 |
number of noise genes |
nconfounder |
number of confounders |
nrModule |
number of modules for confounding variables |
rMeanSubtypes |
number of subtypes defined by confounding variables |
diffmu |
effect size difference for subtype predictive genes |
fold |
how to vary subtype predictive gene signal. 1: original. 0: no signal. |
rho |
para for inverse Wishart distribution. |
df.prior |
para for inverse Wishart distribution. |
groupProb |
subtype predictive genes have prior group information. By prob 1-groupProb, the information will be altered. |
generate S for ISKmeans
alist
Caleb
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | Sdata =generateS(seed=15213,S=2,k=3,meanSamplesPerK=c(40,40,30),nModule=30,meanGenesPerModule=30,
sigma1=1,sigma2=1,sigma3=1,G0=5000,
nconfounder=4,nrModule=20,rMeanSubtypes=3,diffmu=1,fold=c(1,1),
rho = 0.5,df.prior = 100,groupProb=1)
#
gapStat = gapStatistics(Sdata$d, K = 3,alpha = 0.5, group = Sdata$groupUnion)
iRes <- ISKmeans(Sdata$d,K=3,group=Sdata$groupUnion,gamma=0.15,alpha=0.05)
iRes$Cs
iRes$ws
Sdata$subPredictGeneUnion
table(Sdata$subPredictGeneUnion, iRes$ws!=0)
sum(Sdata$subPredictGeneUnion)
sum(iRes$ws!=0)
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