Package: vrnmf 1.0.2
Evan Biederstedt
vrnmf: Volume-Regularized Structured Matrix Factorization
Implements a set of routines to perform structured matrix factorization with minimum volume constraints. The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. <doi:10.1126/science.aba7408>. This package interacts with data available through the 'simulatedNMF' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/vrnmf>. The size of the 'simulatedNMF' package is approximately 8 MB.
Authors:
vrnmf_1.0.2.tar.gz
vrnmf_1.0.2.zip(r-4.5)vrnmf_1.0.2.zip(r-4.4)vrnmf_1.0.2.zip(r-4.3)
vrnmf_1.0.2.tgz(r-4.4-any)vrnmf_1.0.2.tgz(r-4.3-any)
vrnmf_1.0.2.tar.gz(r-4.5-noble)vrnmf_1.0.2.tar.gz(r-4.4-noble)
vrnmf_1.0.2.tgz(r-4.4-emscripten)vrnmf_1.0.2.tgz(r-4.3-emscripten)
vrnmf.pdf |vrnmf.html✨
vrnmf/json (API)
| # Install 'vrnmf' in R: |
| install.packages('vrnmf', repos = c('https://kharchenkolab.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kharchenkolab/vrnmf/issues
Last updated 2 years agofrom:968a29edda. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 03 2024 |
| R-4.5-win | OK | Sep 03 2024 |
| R-4.5-linux | OK | Sep 03 2024 |
| R-4.4-win | OK | Sep 03 2024 |
| R-4.4-mac | OK | Sep 03 2024 |
| R-4.3-win | OK | Sep 03 2024 |
| R-4.3-mac | OK | Sep 03 2024 |
Exports:AnchorFreefactor_intensitiesinfer_intensitiesprojection_onto_simplexsim_factorsvol_preprocessvolnmf_detvolnmf_estimatevolnmf_logdetvolnmf_mainvolnmf_procrustesvolnmf_simplex_colvolnmf_simplex_row
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Non-negative tri-factorization of co-occurence matrix using minimum volume approach. | AnchorFree |
| Infer a matrix of non-negative intensities in NMF with offset/nmf-offset. | factor_intensities |
| Infer a matrix of non-negative intensities in NMF | infer_intensities |
| Project vector onto a probabilistic simplex. | projection_onto_simplex |
| Simulate matrices to explores 'vrnmf' | sim_factors |
| Preprocess the data for downstream volume analysis. | vol_preprocess |
| Update volume-regularized matrix 'R' using det volume approximation | volnmf_det |
| Alternating optimization of volume-regularized NMF | volnmf_estimate |
| Update volume-regularized matrix 'R' using logdet volume approximation. | volnmf_logdet |
| Volume-regularized NMF | volnmf_main |
| Procrustes algorithm estimates orthonormal transformation between two matrices. | volnmf_procrustes |
| Update of a matrix in NMF with equality contstraints on columns. | volnmf_simplex_col |
| Update of a matrix in NMF with equality contstraints on rows. | volnmf_simplex_row |
