Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data
Summarize and Compare Model Fits
Differential Expression Analysis using a Topic Model
PCA, t-SNE and UMAP Plots
Low-dimensional Embeddings from Poisson NMF or Multinomial Topic Model
Recover Poisson NMF Fit from Multinomial Topic Model Fit
Fit Simple Multinomial Model
Fit Non-negative Matrix Factorization to Count Data
Simple Interface for Fitting a Multinomial Topic Model
NMF and Topic Model Likelihoods and Deviances
Loadings Plot
Combine Topics in Multinomial Topic Model
Plot Log-Likelihood Versus Rank
Plot Progress of Model Fitting Over Time
Recover Multinomial Topic Model Fit from Poisson NMF fit
Predict Methods for Poisson NMF and Multinomial Topic Model
Perform HOMER Motif Enrichment Analysis using DE Genomic Positions
Extract or Re-order Data Rows in Poisson NMF or Multinomial Topic Mode...
Simulate Count Data from Poisson NMF Model
Simulate Gene Expression Data from Poisson NMF or Multinomial Topic Mo...
Simulate Toy Gene Expression Data
Structure Plot
Summarize Poisson NMF or Multinomial Topic Model Fit
Volcano Plots for Visualizing Results of Differential Expression Analy...
Implements fast, scalable optimization algorithms for fitting topic models ("grade of membership" models) and non-negative matrix factorizations to count data. The methods exploit the special relationship between the multinomial topic model (also, "probabilistic latent semantic indexing") and Poisson non-negative matrix factorization. The package provides tools to compare, annotate and visualize model fits, including functions to efficiently create "structure plots" and identify key features in topics. The 'fastTopics' package is a successor to the 'CountClust' package. For more information, see <doi:10.48550/arXiv.2105.13440> and <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub repository for additional vignettes not included in the package on CRAN.
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