getMetaPrograms {GeneNMF}

Extract consensus gene programs (meta-programs)

Description

Run it over a list of NMF models obtained using multiNMF; it will determine gene programs that are consistently observed across samples and values of k.

Usage

getMetaPrograms(
  nmf.res,
  method = 0.5,
  max.genes = 200,
  hclust.method = "ward.D2",
  nprograms = 10,
  min.confidence = 0.2,
  remove.empty = TRUE
)

Arguments

nmf.res

A list of NMF models obtained from multiNMF

method

Parameter passed to extractFeatures to obtain top genes for each program

max.genes

Max number of genes for each programs

hclust.method

Method to build similarity tree between individual programs

nprograms

Total number of meta-programs

min.confidence

Percentage of programs in which a gene is seen (out of programs in the corresponding program tree branch/cluster), to be retained in the consensus metaprograms

remove.empty

Whether to remove meta-programs with no genes above confidence threshold

Value

Returns a list with i) 'metaprograms.genes' top genes for each meta-program; ii) 'metaprograms.metrics' dataframe with meta-programs statistics: a) freq. of samples where the MP is present, b) average silhouette width, c) mean Jaccard similarity, d) number of genes in MP, e) number of gene programs in MP; iii) 'programs.jaccard': matrix of Jaccard similarities between meta-programs; iv) 'programs.tree': hierarchical clustering of meta-programs (hclust tree); v) 'programs.clusters': meta-program identity for each program

Examples

library(Seurat)
data(sampleObj)
geneNMF_programs <- multiNMF(list(sampleObj), k=5)
geneNMF_metaprograms <- getMetaPrograms(geneNMF_programs, nprograms=3)