doGaussianMixtureModel: Do Gaussian mixture modelling for isoform expression...

In nghiavtr/ISOP: Isoform-Level Expression Patterns in Single-cell RNA-sequencing Data

Do Gaussian mixture modelling for isoform expression differences

Description

Do Gaussian mixture modelling for isoform expression differences

Usage

doGaussianMixtureModel(dstat, s = NULL, delta, p = NULL, tbreak = NULL,
  useExt = FALSE, delta.thres = 0.75)

Arguments

dstat	A vector of isoform difference
s	A vector of initital values of standard deviation of components
delta	A vector of initial values of mean of components
p	A vector of initial values of proportion of components
tbreak	The number of breaks to create bins
useExt	Option of binning tails or not
delta.thres	Used for estimated p0

Value

A list of results and parameters of the mixture model

Examples

data(isoformDataSample)
#preprocessing
isoformDataSample=ifelse(isoformDataSample <= 3,0,isoformDataSample)
isoformDataSample=isoformDataSample[which(rowSums(isoformDataSample)>0),]
#tranform read count dataset to log scale
isoformDataSample=ifelse(isoformDataSample==0,0,log2(isoformDataSample))
#now data is ready
tbreak=round(sqrt(ncol(isoformDataSample)))
x0=isoformDataSample[1,]
x1=isoformDataSample[2,]
deltaVal=x0-x1
s=c(1,1,1);
delta=c(-5,0,5) 
res=doGaussianMixtureModel(deltaVal,s=s,delta=delta,tbreak=tbreak)

nghiavtr/ISOP documentation built on April 21, 2023, 3:57 p.m.