% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/runSNF.R
 \name{CiteFuse}
 \alias{CiteFuse}

 \title{CiteFuse}

 \usage{

 CiteFuse(
   sce,
   altExp_name = "ADT",
   W_list = NULL,
   gene_select = TRUE,
   dist_cal_RNA = "correlation",
   dist_cal_ADT = "propr",
   ADT_subset = NULL,
   K_knn = 20,

   K_knn_Aff = 30,
   sigma = 0.45,

   t = 10,

   metadata_names = NULL,

   verbose = TRUE,

   topN = 2000

 )

 }
 \arguments{

 \item{sce}{a SingleCellExperiment}

 
 \item{altExp_name}{expression name of ADT matrix}
 
 \item{W_list}{affinity list, if it is NULL, the function will calculate it.}
 

 \item{gene_select}{whether highly variable genes will be selected

 for RNA-seq to calcualte simlarity matrix using `scran` package}

 
 \item{dist_cal_RNA}{similarity metrics used for RNA matrix}
 
 \item{dist_cal_ADT}{similarity metrics used for ADT matrix}
 
 \item{ADT_subset}{A vector  indicates the subset that will be used.}
 
 \item{K_knn}{Number of nearest neighbours}
 

 \item{K_knn_Aff}{Number of nearest neighbors for computing affinity matrix}
 
 \item{sigma}{Variance for local model for computing affinity matrix}
 

 \item{t}{Number of iterations for the diffusion process.}
 
 \item{metadata_names}{A vector indicates the names of metadata returned}
 
 \item{verbose}{whether print out the process}

 \item{topN}{top highly variable genes are used 

 variable gene selection 
 (see `modelGeneVar` in `scran` package for more details)}

 }

 \value{
 A SingleCellExperiment object with fused matrix results stored
 }

 \description{
 A function to runSNF for CITE seq data
 }

 \examples{
 data("sce_ctcl_subset", package = "CiteFuse")
 sce_ctcl_subset <- CiteFuse(sce_ctcl_subset)
 
 }

 \references{
 B Wang, A Mezlini, F Demir, M Fiume, T Zu, M Brudno, B Haibe-Kains, 
 A Goldenberg (2014) Similarity Network Fusion: a fast and effective method 
 to aggregate multiple data types on a genome wide scale. 
 Nature Methods. Online. Jan 26, 2014
 }