Package: fastTopics 0.7-46

Peter Carbonetto

fastTopics: Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

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.

Authors:Peter Carbonetto [aut, cre], Kevin Luo [aut], Kushal Dey [aut], Joyce Hsiao [ctb], Abhishek Sarkar [ctb], Anthony Hung [ctb], Xihui Lin [ctb], Paul C. Boutros [ctb], Minzhe Wang [ctb], Tracy Ke [ctb], Eric Weine [ctb], Matthew Stephens [aut]

fastTopics_0.7-46.tar.gz
fastTopics_0.7-46.zip(r-4.7)fastTopics_0.7-46.zip(r-4.6)fastTopics_0.7-46.zip(r-4.5)
fastTopics_0.7-46.tgz(r-4.6-x86_64)fastTopics_0.7-46.tgz(r-4.6-arm64)fastTopics_0.7-46.tgz(r-4.5-x86_64)fastTopics_0.7-46.tgz(r-4.5-arm64)
fastTopics_0.7-46.tar.gz(r-4.7-arm64)fastTopics_0.7-46.tar.gz(r-4.7-x86_64)fastTopics_0.7-46.tar.gz(r-4.6-arm64)fastTopics_0.7-46.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
fastTopics/json (API)

# Install 'fastTopics' in R:
install.packages('fastTopics', repos = c('https://stephenslab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/stephenslab/fasttopics/issues

Pkgdown/docs site:https://stephenslab.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • newsgroups - Topic modeling results from the "20 Newsgroups" data set.
  • pbmc_facs - Mixture of 10 FACS-purified PBMC Single-Cell RNA-seq data

On CRAN:

Conda:

openblascpp

8.92 score 87 stars 2 packages 880 scripts 371 downloads 47 exports 97 dependencies

Last updated from:5cf9a448b1. Checks:8 ERROR, 4 WARNING, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR280
linux-devel-x86_64ERROR247
source / vignettesERROR305
linux-release-arm64ERROR291
linux-release-x86_64ERROR271
macos-release-arm64WARNING187
macos-release-x86_64WARNING395
macos-oldrel-arm64WARNING177
macos-oldrel-x86_64WARNING340
windows-develERROR303
windows-releaseERROR286
windows-oldrelERROR259
wasm-releaseFAIL150

Exports:annotation_heatmapcompare_fitscostde_analysisde_analysis_control_defaultdeviance_poisson_nmfembedding_plot_2dembedding_plot_2d_ggplot_callfit_multinom_modelfit_poisson_nmffit_poisson_nmf_control_defaultfit_topic_modelinit_poisson_nmfinit_poisson_nmf_from_clusteringloadings_plotloadings_plot_ggplot_callloglik_multinom_topic_modelloglik_poisson_nmfloglik_vs_rank_ggplot_callmerge_topicsmultinom2poissonpca_from_topicspca_hexbin_plotpca_hexbin_plot_ggplot_callpca_plotplot_loglik_vs_rankplot_progresspoisson2binompoisson2multinomrun_homerselect_loadingssimulate_count_datasimulate_multinom_gene_datasimulate_poisson_gene_datasimulate_toy_gene_datastructure_plotstructure_plot_default_embed_methodstructure_plot_ggplot_calltsne_from_topicstsne_plotumap_from_topicsumap_plotvolcano_plotvolcano_plot_do_label_defaultvolcano_plot_ggplot_callvolcano_plot_ly_callvolcano_plotly

Dependencies:ashraskpassbase64encBHbslibcachemclicowplotcpp11crayoncrosstalkcurldata.tabledigestdplyrdqrngetrunctevaluatefarverfastmapFNNfontawesomefsgenericsggplot2ggrepelgluegtablegtoolshighrhmshtmltoolshtmlwidgetshttrinvgammairlbaisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclemagrittrMatrixmemoisemimemixsqpopensslotelpbapplypillarpkgconfigplotlyplyrprettyunitsprogresspromisespurrrquadprogR6rappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppParallelRcppProgressreshape2RhpcBLASctlrlangrmarkdownRSpectraRtsneS7sassscalessitmoSQUAREMstringistringrsystibbletidyrtidyselecttinytextruncnormutf8uwotvctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Annotation Heatmapannotation_heatmap
Summarize and Compare Model Fitscompare_fits
Differential Expression Analysis using a Topic Modelde_analysis de_analysis_control_default
PCA, t-SNE and UMAP Plotsembedding_plot_2d embedding_plot_2d_ggplot_call pca_hexbin_plot pca_hexbin_plot_ggplot_call pca_plot tsne_plot umap_plot
Fit Simple Multinomial Modelfit_multinom_model
Fit Non-negative Matrix Factorization to Count Datafit_poisson_nmf fit_poisson_nmf_control_default init_poisson_nmf init_poisson_nmf_from_clustering
Simple Interface for Fitting a Multinomial Topic Modelfit_topic_model
Loadings Plotloadings_plot loadings_plot_ggplot_call
NMF and Topic Model Likelihoods and Deviancescost deviance_poisson_nmf loglik_multinom_topic_model loglik_poisson_nmf
Combine Topics in Multinomial Topic Modelmerge_topics
Recover Poisson NMF Fit from Multinomial Topic Model Fitmultinom2poisson
Topic modeling results from the "20 Newsgroups" data set.newsgroups
Mixture of 10 FACS-purified PBMC Single-Cell RNA-seq datapbmc_facs
Low-dimensional Embeddings from Poisson NMF or Multinomial Topic Modelpca_from_topics tsne_from_topics umap_from_topics
Plot Log-Likelihood Versus Rankloglik_vs_rank_ggplot_call plot_loglik_vs_rank
Plot Progress of Model Fitting Over Timeplot_progress
Recover Binomial Topic Model Fit from Poisson NMF fitpoisson2binom
Recover Multinomial Topic Model Fit from Poisson NMF fitpoisson2multinom
Predict Methods for Poisson NMF and Multinomial Topic Modelpredict.multinom_topic_model_fit predict.poisson_nmf_fit
Perform HOMER Motif Enrichment Analysis using DE Genomic Positionsrun_homer
Extract or Re-order Data Rows in Poisson NMF or Multinomial Topic Model Fitselect select.multinom_topic_model_fit select.poisson_nmf_fit select_loadings
Simulate Count Data from Poisson NMF Modelsimulate_count_data
Simulate Gene Expression Data from Poisson NMF or Multinomial Topic Modelsimulate_multinom_gene_data simulate_poisson_gene_data
Simulate Toy Gene Expression Datasimulate_toy_gene_data
Structure Plotplot.multinom_topic_model_fit plot.poisson_nmf_fit structure_plot structure_plot_default_embed_method structure_plot_ggplot_call
Summarize Poisson NMF or Multinomial Topic Model Fitprint.summary.multinom_topic_model_fit print.summary.poisson_nmf_fit summary.multinom_topic_model_fit summary.poisson_nmf_fit
Volcano Plots for Visualizing Results of Differential Expression Analysisplot.topic_model_de_analysis volcano_plot volcano_plotly volcano_plot_do_label_default volcano_plot_ggplot_call volcano_plot_ly_call