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,
  nMP = 10,
  specificity.weight = 5,
  weight.explained = 0.5,
  max.genes = 200,
  metric = c("cosine", "jaccard"),
  hclust.method = "ward.D2",
  min.confidence = 0.5,
  remove.empty = TRUE
)

Arguments

nmf.res

A list of NMF models obtained from multiNMF

nMP

Total number of meta-programs

specificity.weight

A parameter controlling how specific gene should be for each program. 'specificity.weight=0' no constraint on specificity, and positive values impose increasing specificity.

weight.explained

Fraction of NMF weights explained by selected genes. For example if weight.explained=0.5, all genes that together account for 50% of NMF weights are used to return program signatures.

max.genes

Max number of genes for each programs

metric

Metric to calculate pairwise similarity between programs

hclust.method

Method to build similarity tree between individual 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 similarity (cosine or Jaccard), d) number of genes in MP, e) number of gene programs in MP; iii) 'programs.similarity': matrix of similarities (Jaccard or cosine) 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, nMP=3)