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Multi modality data matrices are factorized conjointly into the multiplication of a shared sub-matrix and multiple modality specific sub-matrices, group sparse constraint is applied to the shared sub-matrix to capture the homogeneous and heterogeneous information, respectively. Then the samples are classified by clustering the shared sub-matrix with kmeanspp(), a new version of kmeans() developed here to obtain concordant results. The package also provides the cluster number estimation by rotation cost. Moreover, cluster specific features could be retrieved using hypergeometric tests.
| Version: | 0.1.0 |
| Imports: | MASS, SNFtool, dplyr, InterSIM, stats |
| Suggests: | knitr, rmarkdown |
| Published: | 2023-08-14 |
| DOI: | 10.32614/CRAN.package.M3JF |
| Author: | Xiaoyao Yin [aut, cre] |
| Maintainer: | Xiaoyao Yin <xyyin at xmail.ncba.ac.cn> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | M3JF results |
| Reference manual: | M3JF.pdf |
| Vignettes: |
An Introduction to the package M3JF |
| Package source: | M3JF_0.1.0.tar.gz |
| Windows binaries: | r-devel: M3JF_0.1.0.zip, r-release: M3JF_0.1.0.zip, r-oldrel: M3JF_0.1.0.zip |
| macOS binaries: | r-release (arm64): M3JF_0.1.0.tgz, r-oldrel (arm64): M3JF_0.1.0.tgz, r-release (x86_64): M3JF_0.1.0.tgz, r-oldrel (x86_64): M3JF_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.