MultiRD.onegroup: MultiRD algorithm to deconvolute cell type proportions from a...
In chencxxy28/MultiRD: Multi-robust deconvolution algorithm

This is the main algorithm that MultiRD is relied on to implement deconvolution.

MultiRD.onegroup(
bulk.data,
list.marker,
celltype.unique,
subject.level.proportion,
population.level.proportion,
proportion.sd=1,
lambda.option=c(seq(from=0,to=0.075,length=15),10,50,100,500,1000),
tol=0.001,
iter.num=1000
)

`bulk.data`	ExpressionSet object for a target bulk data
`list.marker`	A list of pre-specified marker genes corresponding to each cell type
`celltype.unique`	A list of cell types. It should match the order in list.marker
`subject.level.proportion`	A pre-specified cell type proportions for the target bulk data, which could be obtained from reference-based deconvolution approach.
`population.level.proportion`	A pre-specified population-level cell type proportions, which could be obtained from single-cell RNA-seq and external expression data from different studies, species, or data types
`proportion.sd`	an optional adjustment based on pre-specified standard deviation of cell-type proportion estimation. The default is 1 for each cell type.
`lambda.option`	a sequence of values for the tunning parameter
`tol`	a small constant used for convergence criteria. The default is 0.001
`iter.num`	The maximum number of iteration. The default is 1000.

`est.prop`	a list containing estimated cell type proportions corresponding to each tuning value.
`metrics`	a sequence of goodness-of-fit values corresponding to each tuning value. The smaller the better.