TCA: Tensor Composition Analysis

Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of sources into a three-dimensional matrix of signals (features by observations by sources). TCA further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects (TCA regression); particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a tensor of cell-type-specific methylation levels for each individual (methylation sites by individuals by cell types) and it allows to detect cell-type-specific relations (associations) with an outcome of interest. For more details see Rahmani et al. (2018) <doi:10.1101/437368>.

Version: 1.1.0
Depends: R (≥ 3.4.0)
Imports: config, data.table, futile.logger, gmodels, Matrix, matrixcalc, matrixStats, nloptr, parallel, pbapply, pracma, rsvd, stats, quadprog, glmnet
Suggests: testthat, knitr, rmarkdown
Published: 2019-11-16
Author: Elior Rahmani [aut, cre]
Maintainer: Elior Rahmani <elior.rahmani at>
License: GPL-3
NeedsCompilation: no
Citation: TCA citation info
Materials: README NEWS
CRAN checks: TCA results


Reference manual: TCA.pdf
Vignettes: Cell-type-specific resolution epigenetics using TCA
Package source: TCA_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: TCA_1.1.0.tgz, r-oldrel: TCA_1.1.0.tgz
Old sources: TCA archive


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