TCA (1.0.0)

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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) .

Maintainer: Elior Rahmani
Author(s): Elior Rahmani [aut, cre]

License: GPL-3

Uses: config, data.table, futile.logger, gmodels, Matrix, matrixcalc, matrixStats, nloptr, pbapply, pracma, quadprog, rsvd, testthat, knitr, rmarkdown

Released 3 months ago.



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