A function to runSNF for CITE seq data

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 = 20,
  metadata_names = NULL,
  verbose = TRUE,
  FDR = 0.05,
  bio = 0.01
)

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

FDR

false discovery rate threshold for highly variable gene selection

bio

biological component of the variance threshold for highly 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)
#> Calculating affinity matrix #> Performing SNF