CiteFuse {CiteFuse} | R Documentation

## CiteFuse

### Description

A function to runSNF for CITE seq data

### 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

`sce` | a SingleCellExperiment
`altExp_name` | expression name of ADT matrix
`W_list` | affinity list, if it is NULL, the function will calculate it.
`gene_select` | whether highly variable genes will be selected for RNA-seq to calcualte simlarity matrix using 'scran' package
`dist_cal_RNA` | similarity metrics used for RNA matrix
`dist_cal_ADT` | similarity metrics used for ADT matrix
`ADT_subset` | A vector indicates the subset that will be used.
`K_knn` | Number of nearest neighbours
`K_knn_Aff` | Number of nearest neighbors for computing affinity matrix
`sigma` | Variance for local model for computing affinity matrix
`t` | Number of iterations for the diffusion process.
`metadata_names` | A vector indicates the names of metadata returned
`verbose` | whether print out the process
`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

### 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

### Examples

    data("sce_ctcl_subset", package = "CiteFuse")
    sce_ctcl_subset <- CiteFuse(sce_ctcl_subset)

[Package CiteFuse version 1.2.1 Index]