HSSVD: Biclustering with Heterogeneous Variance

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A data mining tool for discovering subgroups of patients and genes that simultaneously display unusual levels of variability compared to other genes and patients. Based on sparse singular value decomposition (SSVD), the method can detect both mean and variance biclusters in the presence of heterogeneous residual variance.

Author
Guanhua Chen [aut, cre], Michael Kosorok [aut], Shannon Holloway [ctb]
Date of publication
2014-12-04 19:00:48
Maintainer
Guanhua Chen <guanhuac@live.unc.edu>
License
GPL-2
Version
1.2

View on CRAN

Man pages

HSSVD-package
Biclustering with heterogeneous variance
low_rank
Implementation of HSSVD framework for biclusering with...
Methylation
Methylation Data in cancer versus normal patients

Files in this package

HSSVD
HSSVD/NAMESPACE
HSSVD/data
HSSVD/data/Methylation.rda
HSSVD/data/datalist
HSSVD/R
HSSVD/R/SSVD.iter.thresh.R
HSSVD/R/initialization.R
HSSVD/R/Huber.R
HSSVD/R/subsp.dist.orth.R
HSSVD/R/error.est.full.R
HSSVD/R/consistent.signs.R
HSSVD/R/hard.thresh.R
HSSVD/R/onestep.R
HSSVD/R/soft.thresh.R
HSSVD/R/select.indices.R
HSSVD/R/soft.thresh.scalar.R
HSSVD/R/low_rank.R
HSSVD/R/layer.find.R
HSSVD/R/rank_est.R
HSSVD/R/hard.thresh.scalar.R
HSSVD/R/HSSVD-internal.R
HSSVD/MD5
HSSVD/DESCRIPTION
HSSVD/man
HSSVD/man/HSSVD-package.Rd
HSSVD/man/Methylation.Rd
HSSVD/man/low_rank.Rd