This function provides a reference-based deconvolution by resembling all estimated cell-type proportions based on each reference set.
InteRD1(bulk.data,list_marker,cell_type_unique,comb_used,
lambda_option,tol=1e-06)The `ExpressionSet` object for a target bulk data.
A list of pre-specified marker genes corresponding to each cell type.
A list of cell types. It should match the order in list.marker.
A list of pre-estimated cell type proportions based on different references.
A sequence of values for the tuning parameter.
A tolerance value for convergence. The default is 1e-06
A list containing estimated cell type proportions corresponding to each tuning value, named `est`; and a sequence of goodness-of-fit values corresponding to each tuning value, named `metrics`. The smaller the better; and a list of weights corresponding to each tuning value, named `weights_list`.
##read data
library(InteRD)
readRDSFromWeb<-function(ref) {readRDS(gzcon(url(ref)))}
urlremote<-"https://github.com/chencxxy28/Data/raw/main/data_InteRD/"
pseudo.seger<-readRDSFromWeb(paste0(urlremote,"pseudo.seger.rds"))
comb<-readRDSFromWeb(paste0(urlremote,"comb_seger.rds"))
list_marker<-readRDSFromWeb(paste0(urlremote,"list_markerbaron20.rds"))
lambda_option<-0
cell_type_unique<-c("alpha","beta","delta","gamma")
InteRD1.output<-InteRD1(bulk.data =pseudo.seger,list_marker,
cell_type_unique,comb_used=comb,lambda_option,tol=1e-02)
#> calculate criteria
InteRD1<-InteRD.predict.prop(InteRD.output=InteRD1.output)