Bootstrap the topic decomposition to find each topic's robust top words

Refits the NMF topic model on many bootstrap resamples of the papers and reports, for every topic, how reliably each word stays among the topic's top terms. This separates words that genuinely characterise a topic from words that only surface in a single lucky fit, and lets you see how the top-word lists change with and without the PPI view (run once per include_ppi).

Usage

bootstrapTopicModels(
  subnetwork,
  n_boot = 50,
  n_topics = 5,
  include_ppi = TRUE,
  n_top_terms = 10,
  min_term_count = 2,
  max_iter = 200,
  tol = 1e-04,
  seed = 1
)

Arguments

subnetwork: list with nodes and edges data.frames, e.g. the output of getSubnetworkFromIndra.
n_boot: number of bootstrap resamples. Default 50.
n_topics: number of topics (rank of the factorization). Default 5.
include_ppi: logical; factorize the PPI/edge view jointly with the text (TRUE, default) or use paper words only (FALSE). See decomposeSubnetworkByTopic.
n_top_terms: number of top words that define a topic's "top list" in each resample (the cutoff for the selection-frequency tally). Default 10.
min_term_count: minimum corpus frequency for a word to be kept when building the text matrix. Default 2.
max_iter: maximum number of NMF multiplicative-update iterations. Default 200.
tol: relative-change tolerance for NMF early stopping. Default 1e-4.
seed: random seed for the reference fit, the resampling, and each bootstrap NMF. Default 1.

Value

A list with

include_ppi, n_boot, n_topics: the settings used.
topTerms: named list topic_1 ... topic_k. Each is a data.frame sorted by selection_freq, with columns term, selection_freq (fraction of resamples the word was in this topic's top n_top_terms), and mean_weight (mean within-topic word weight across resamples). A word with selection_freq near 1 is a stable signature of the topic.
reference: named list of the top n_top_terms words per topic from the single full-data fit, for comparison.

Details

Because topic indices are arbitrary across fits (label switching), each resample's topics are first aligned to a reference fit on the full data by cosine similarity of their topic-word vectors. The papers are resampled with replacement; the word vocabulary is held fixed (from the full data) so topics remain comparable across resamples, and the NMF seed is held fixed so the variability reported reflects data resampling rather than random initialization.

Note

Beta feature: This function is experimental and the API may change without notice in future versions.

Examples

if (FALSE) { # \dontrun{
input <- data.table::fread(system.file(
    "extdata/groupComparisonModel.csv",
    package = "MSstatsBioNet"
))
subnetwork <- getSubnetworkFromIndra(input)

# Top words with PPIs included vs. words only:
boot_ppi  <- bootstrapTopicModels(subnetwork, include_ppi = TRUE)
boot_text <- bootstrapTopicModels(subnetwork, include_ppi = FALSE)

head(boot_ppi$topTerms$topic_1)     # robust signature words for topic 1
boot_text$topTerms$topic_1
} # }