Package: fastTopics 0.6-186
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>.
Authors:
fastTopics_0.6-186.tar.gz
fastTopics_0.6-186.tar.gz(r-4.5-noble)fastTopics_0.6-186.tar.gz(r-4.4-noble)
fastTopics.pdf |fastTopics.html✨
fastTopics/json (API)
| # InstallfastTopics in R: |
| install.packages('fastTopics',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/stephenslab/fasttopics/issues
- pbmc_facs - Mixture of 10 FACS-purified PBMC Single-Cell RNA-seq data
Last updated 6 days agofrom:593edca80e
Exports:compare_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_progresspoisson2multinomrun_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:ashraskpassbase64encBHbslibcachemclicolorspacecowplotcpp11crayoncrosstalkcurldata.tabledigestdplyrdqrngetrunctevaluatefansifarverfastmapFNNfontawesomefsgenericsggplot2ggrepelgluegtablegtoolshighrhmshtmltoolshtmlwidgetshttrinvgammairlbaisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimemixsqpmunsellnlmeopensslpbapplypillarpkgconfigplotlyprettyunitsprogresspromisespurrrquadprogR6rappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppParallelRcppProgressRhpcBLASctlrlangrmarkdownRSpectraRtsnesassscalessitmoSQUAREMstringistringrsystibbletidyrtidyselecttinytextruncnormutf8uwotvctrsviridisLitewithrxfunyaml
Analysis of single-cell RNA-seq data using a topic model, Part 1: basic concepts
Rendered fromsingle_cell_rnaseq_basic.Rmdusingknitr::rmarkdownon Jun 30 2024.Last update: 2024-06-30
Started: 2024-06-30
Exploring the close relationship between topic modeling and non-negative matrix factorization for count data
Rendered fromrelationship.Rmdusingknitr::rmarkdownon Jun 30 2024.Last update: 2022-05-27
Started: 2022-05-27
Topic modeling vs. clustering of gene expression data: an illustration
Rendered fromtopics_vs_clusters.Rmdusingknitr::rmarkdownon Jun 30 2024.Last update: 2022-05-27
Started: 2022-05-27
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Summarize and Compare Model Fits | compare_fits |
| Differential Expression Analysis using a Topic Model | de_analysis de_analysis_control_default |
| PCA, t-SNE and UMAP Plots | embedding_plot_2d embedding_plot_2d_ggplot_call pca_hexbin_plot pca_hexbin_plot_ggplot_call pca_plot tsne_plot umap_plot |
| Fit Simple Multinomial Model | fit_multinom_model |
| Fit Non-negative Matrix Factorization to Count Data | fit_poisson_nmf fit_poisson_nmf_control_default init_poisson_nmf init_poisson_nmf_from_clustering |
| Simple Interface for Fitting a Multinomial Topic Model | fit_topic_model |
| Loadings Plot | loadings_plot loadings_plot_ggplot_call |
| NMF and Topic Model Likelihoods and Deviances | cost deviance_poisson_nmf loglik_multinom_topic_model loglik_poisson_nmf |
| Combine Topics in Multinomial Topic Model | merge_topics |
| Recover Poisson NMF Fit from Multinomial Topic Model Fit | multinom2poisson |
| Mixture of 10 FACS-purified PBMC Single-Cell RNA-seq data | pbmc_facs |
| Low-dimensional Embeddings from Poisson NMF or Multinomial Topic Model | pca_from_topics tsne_from_topics umap_from_topics |
| Plot Log-Likelihood Versus Rank | loglik_vs_rank_ggplot_call plot_loglik_vs_rank |
| Plot Progress of Model Fitting Over Time | plot_progress |
| Recover Multinomial Topic Model Fit from Poisson NMF fit | poisson2multinom |
| Predict Methods for Poisson NMF and Multinomial Topic Model | predict.multinom_topic_model_fit predict.poisson_nmf_fit |
| Perform HOMER Motif Enrichment Analysis using DE Genomic Positions | run_homer |
| Extract or Re-order Data Rows in Poisson NMF or Multinomial Topic Model Fit | select select.multinom_topic_model_fit select.poisson_nmf_fit select_loadings |
| Simulate Count Data from Poisson NMF Model | simulate_count_data |
| Simulate Gene Expression Data from Poisson NMF or Multinomial Topic Model | simulate_multinom_gene_data simulate_poisson_gene_data |
| Simulate Toy Gene Expression Data | simulate_toy_gene_data |
| Structure Plot | plot.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 Fit | print.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 Analysis | plot.topic_model_de_analysis volcano_plot volcano_plotly volcano_plot_do_label_default volcano_plot_ggplot_call volcano_plot_ly_call |
