getMetaPrograms: 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
)

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) 'metaprogram.composition' dataframe containing the number of individual for each sample that contributed to the consensus MPs; iv) 'programs.similarity': matrix of similarities (Jaccard or cosine) between meta-programs; v) 'programs.tree': hierarchical clustering of meta-programs (hclust tree); vi) 'programs.clusters': meta-program identity for each program

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

Examples

Run this code

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

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