CiteFuse

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