An optimized method for identifying mutually
exclusive genomic events.  Its main contribution is a
statistical analysis based on the Poisson-Binomial
distribution that takes into account that some samples
are more mutated than others. See [Canisius, Sander, John WM Martens,
and  Lodewyk FA Wessels. (2016)  "A novel independence test for
somatic alterations in cancer shows that
biology drives mutual exclusivity but chance explains
most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>].
The mutations matrices are sparse matrices. The method developed takes
advantage of the advantages of this type of matrix to save
time and computing resources.
knitr, rmarkdown, RUnit, BiocStyle, BiocGenerics, dplyr,
kableExtra, TCGAbiolinks, magick, stats, qvalue

R (>= 4.0), Matrix, speedglm, PoissonBinomial,
ShiftConvolvePoibin, utils, matrixStats

c(person(given = "Pedro", family = "Mac Dowell Innecco", role = c("aut","cre"), email = "dmu@pedroinnecco.com"),
person(given = "Erich", family = "Neuwirth", role = c("aut"), email = "erich.neuwirth@univie.ac.at"))
Pedro Mac Dowell Innecco <dmu@pedroinnecco.com>
Provide color schemes for maps (and other graphics) based on the
color palettes of several Microsoft(r) products. Forked from 'RColorBrewer' v1.1-2.
2017-01-03 15:34:03 UTC; Guybrush Threepwood
Pedro Mac Dowell Innecco [aut, cre],
Erich Neuwirth [aut]

EWAS using reference-Free DNA methylation mixture deconvolution
Reference-free method for conducting EWAS while deconvoluting DNA methylation arising as mixtures of cell types.  This method is similar to surrogate variable analysis (SVA and ISVA), except that it makes additional use of a biological mixture assumption.

EWAS using Reference-Free DNA Methylation Mixture Deconvolution
Reference-free method for conducting EWAS while deconvoluting DNA methylation arising as mixtures of cell types.
The older method (Houseman et al., 2014) is similar to surrogate variable analysis (SVA and ISVA), except that it makes additional use of a biological mixture assumption.
The newer method (Houseman et al., 2016) is similar to non-negative matrix factorization, with additional constraints and additional utilities.